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| author | Erwin Coumans <erwincoumans@google.com> | 2019-04-27 07:31:15 -0700 |
|---|---|---|
| committer | Erwin Coumans <erwincoumans@google.com> | 2019-04-27 07:31:15 -0700 |
| commit | ef9570c315ee0db1dffbb950dbd46aa726e06f83 (patch) | |
| tree | 44dcbe67c0cd84f189b5e0cd7cc5dfda3488c843 /examples/pybullet/gym/pybullet_envs | |
| parent | c59173504298ec79c04cbeb8df2b7766d4920627 (diff) | |
| download | bullet3-ef9570c315ee0db1dffbb950dbd46aa726e06f83.tar.gz | |
add yapf style and apply yapf to format all Python files
This recreates pull request #2192
Diffstat (limited to 'examples/pybullet/gym/pybullet_envs')
202 files changed, 37797 insertions, 14030 deletions
diff --git a/examples/pybullet/gym/pybullet_envs/ARS/ars.py b/examples/pybullet/gym/pybullet_envs/ARS/ars.py index 760ebd732..9e9c2b559 100644 --- a/examples/pybullet/gym/pybullet_envs/ARS/ars.py +++ b/examples/pybullet/gym/pybullet_envs/ARS/ars.py @@ -4,7 +4,7 @@ import os import inspect currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe()))) parentdir = os.path.dirname(os.path.dirname(currentdir)) -os.sys.path.insert(0,parentdir) +os.sys.path.insert(0, parentdir) # Importing the libraries import os @@ -20,17 +20,17 @@ import argparse # Setting the Hyper Parameters class Hp(): - - def __init__(self): - self.nb_steps = 10000 - self.episode_length = 1000 - self.learning_rate = 0.02 - self.nb_directions = 16 - self.nb_best_directions = 16 - assert self.nb_best_directions <= self.nb_directions - self.noise = 0.03 - self.seed = 1 - self.env_name = 'HalfCheetahBulletEnv-v0' + + def __init__(self): + self.nb_steps = 10000 + self.episode_length = 1000 + self.learning_rate = 0.02 + self.nb_directions = 16 + self.nb_best_directions = 16 + assert self.nb_best_directions <= self.nb_directions + self.noise = 0.03 + self.seed = 1 + self.env_name = 'HalfCheetahBulletEnv-v0' # Multiprocess Exploring the policy on one specific direction and over one episode @@ -39,239 +39,255 @@ _RESET = 1 _CLOSE = 2 _EXPLORE = 3 -def ExploreWorker(rank,childPipe, envname, args): - env = gym.make(envname) - nb_inputs = env.observation_space.shape[0] - normalizer = Normalizer(nb_inputs) - observation_n = env.reset() - n=0 - while True: - n+=1 - try: - # Only block for short times to have keyboard exceptions be raised. - if not childPipe.poll(0.001): - continue - message, payload = childPipe.recv() - except (EOFError, KeyboardInterrupt): - break - if message == _RESET: - observation_n = env.reset() - childPipe.send(["reset ok"]) - continue - if message == _EXPLORE: - #normalizer = payload[0] #use our local normalizer - policy = payload[1] - hp = payload[2] - direction = payload[3] - delta = payload[4] - state = env.reset() - done = False - num_plays = 0. - sum_rewards = 0 - while not done and num_plays < hp.episode_length: - normalizer.observe(state) - state = normalizer.normalize(state) - action = policy.evaluate(state, delta, direction,hp) - state, reward, done, _ = env.step(action) - reward = max(min(reward, 1), -1) - sum_rewards += reward - num_plays += 1 - childPipe.send([sum_rewards]) + +def ExploreWorker(rank, childPipe, envname, args): + env = gym.make(envname) + nb_inputs = env.observation_space.shape[0] + normalizer = Normalizer(nb_inputs) + observation_n = env.reset() + n = 0 + while True: + n += 1 + try: + # Only block for short times to have keyboard exceptions be raised. + if not childPipe.poll(0.001): continue - if message == _CLOSE: - childPipe.send(["close ok"]) - break - childPipe.close() - + message, payload = childPipe.recv() + except (EOFError, KeyboardInterrupt): + break + if message == _RESET: + observation_n = env.reset() + childPipe.send(["reset ok"]) + continue + if message == _EXPLORE: + #normalizer = payload[0] #use our local normalizer + policy = payload[1] + hp = payload[2] + direction = payload[3] + delta = payload[4] + state = env.reset() + done = False + num_plays = 0. + sum_rewards = 0 + while not done and num_plays < hp.episode_length: + normalizer.observe(state) + state = normalizer.normalize(state) + action = policy.evaluate(state, delta, direction, hp) + state, reward, done, _ = env.step(action) + reward = max(min(reward, 1), -1) + sum_rewards += reward + num_plays += 1 + childPipe.send([sum_rewards]) + continue + if message == _CLOSE: + childPipe.send(["close ok"]) + break + childPipe.close() + # Normalizing the states + class Normalizer(): - - def __init__(self, nb_inputs): - self.n = np.zeros(nb_inputs) - self.mean = np.zeros(nb_inputs) - self.mean_diff = np.zeros(nb_inputs) - self.var = np.zeros(nb_inputs) - - def observe(self, x): - self.n += 1. - last_mean = self.mean.copy() - self.mean += (x - self.mean) / self.n - self.mean_diff += (x - last_mean) * (x - self.mean) - self.var = (self.mean_diff / self.n).clip(min = 1e-2) - - def normalize(self, inputs): - obs_mean = self.mean - obs_std = np.sqrt(self.var) - return (inputs - obs_mean) / obs_std + + def __init__(self, nb_inputs): + self.n = np.zeros(nb_inputs) + self.mean = np.zeros(nb_inputs) + self.mean_diff = np.zeros(nb_inputs) + self.var = np.zeros(nb_inputs) + + def observe(self, x): + self.n += 1. + last_mean = self.mean.copy() + self.mean += (x - self.mean) / self.n + self.mean_diff += (x - last_mean) * (x - self.mean) + self.var = (self.mean_diff / self.n).clip(min=1e-2) + + def normalize(self, inputs): + obs_mean = self.mean + obs_std = np.sqrt(self.var) + return (inputs - obs_mean) / obs_std + # Building the AI + class Policy(): - def __init__(self, input_size, output_size, env_name, args): - try: - self.theta = np.load(args.policy) - except: - self.theta = np.zeros((output_size, input_size)) - self.env_name = env_name - print("Starting policy theta=",self.theta) - def evaluate(self, input, delta, direction, hp): - if direction is None: - return np.clip(self.theta.dot(input), -1.0, 1.0) - elif direction == "positive": - return np.clip((self.theta + hp.noise*delta).dot(input), -1.0, 1.0) - else: - return np.clip((self.theta - hp.noise*delta).dot(input), -1.0, 1.0) - - def sample_deltas(self): - return [np.random.randn(*self.theta.shape) for _ in range(hp.nb_directions)] - - def update(self, rollouts, sigma_r, args): - step = np.zeros(self.theta.shape) - for r_pos, r_neg, d in rollouts: - step += (r_pos - r_neg) * d - self.theta += hp.learning_rate / (hp.nb_best_directions * sigma_r) * step - timestr = time.strftime("%Y%m%d-%H%M%S") - np.save(args.logdir+"/policy_"+self.env_name+"_"+timestr+".npy", self.theta) + + def __init__(self, input_size, output_size, env_name, args): + try: + self.theta = np.load(args.policy) + except: + self.theta = np.zeros((output_size, input_size)) + self.env_name = env_name + print("Starting policy theta=", self.theta) + + def evaluate(self, input, delta, direction, hp): + if direction is None: + return np.clip(self.theta.dot(input), -1.0, 1.0) + elif direction == "positive": + return np.clip((self.theta + hp.noise * delta).dot(input), -1.0, 1.0) + else: + return np.clip((self.theta - hp.noise * delta).dot(input), -1.0, 1.0) + + def sample_deltas(self): + return [np.random.randn(*self.theta.shape) for _ in range(hp.nb_directions)] + + def update(self, rollouts, sigma_r, args): + step = np.zeros(self.theta.shape) + for r_pos, r_neg, d in rollouts: + step += (r_pos - r_neg) * d + self.theta += hp.learning_rate / (hp.nb_best_directions * sigma_r) * step + timestr = time.strftime("%Y%m%d-%H%M%S") + np.save(args.logdir + "/policy_" + self.env_name + "_" + timestr + ".npy", self.theta) # Exploring the policy on one specific direction and over one episode + def explore(env, normalizer, policy, direction, delta, hp): - state = env.reset() - done = False - num_plays = 0. - sum_rewards = 0 - while not done and num_plays < hp.episode_length: - normalizer.observe(state) - state = normalizer.normalize(state) - action = policy.evaluate(state, delta, direction, hp) - state, reward, done, _ = env.step(action) - reward = max(min(reward, 1), -1) - sum_rewards += reward - num_plays += 1 - return sum_rewards + state = env.reset() + done = False + num_plays = 0. + sum_rewards = 0 + while not done and num_plays < hp.episode_length: + normalizer.observe(state) + state = normalizer.normalize(state) + action = policy.evaluate(state, delta, direction, hp) + state, reward, done, _ = env.step(action) + reward = max(min(reward, 1), -1) + sum_rewards += reward + num_plays += 1 + return sum_rewards + # Training the AI + def train(env, policy, normalizer, hp, parentPipes, args): - - for step in range(hp.nb_steps): - - # Initializing the perturbations deltas and the positive/negative rewards - deltas = policy.sample_deltas() - positive_rewards = [0] * hp.nb_directions - negative_rewards = [0] * hp.nb_directions - - if parentPipes: - for k in range(hp.nb_directions): - parentPipe = parentPipes[k] - parentPipe.send([_EXPLORE,[normalizer, policy, hp, "positive", deltas[k]]]) - for k in range(hp.nb_directions): - positive_rewards[k] = parentPipes[k].recv()[0] - - for k in range(hp.nb_directions): - parentPipe = parentPipes[k] - parentPipe.send([_EXPLORE,[normalizer, policy, hp, "negative", deltas[k]]]) - for k in range(hp.nb_directions): - negative_rewards[k] = parentPipes[k].recv()[0] - - else: - # Getting the positive rewards in the positive directions - for k in range(hp.nb_directions): - positive_rewards[k] = explore(env, normalizer, policy, "positive", deltas[k], hp) - - - # Getting the negative rewards in the negative/opposite directions - for k in range(hp.nb_directions): - negative_rewards[k] = explore(env, normalizer, policy, "negative", deltas[k], hp) - - - # Gathering all the positive/negative rewards to compute the standard deviation of these rewards - all_rewards = np.array(positive_rewards + negative_rewards) - sigma_r = all_rewards.std() - - # Sorting the rollouts by the max(r_pos, r_neg) and selecting the best directions - scores = {k:max(r_pos, r_neg) for k,(r_pos,r_neg) in enumerate(zip(positive_rewards, negative_rewards))} - order = sorted(scores.keys(), key = lambda x:scores[x])[:hp.nb_best_directions] - rollouts = [(positive_rewards[k], negative_rewards[k], deltas[k]) for k in order] - - # Updating our policy - policy.update(rollouts, sigma_r, args) - - # Printing the final reward of the policy after the update - reward_evaluation = explore(env, normalizer, policy, None, None, hp) - print('Step:', step, 'Reward:', reward_evaluation) + + for step in range(hp.nb_steps): + + # Initializing the perturbations deltas and the positive/negative rewards + deltas = policy.sample_deltas() + positive_rewards = [0] * hp.nb_directions + negative_rewards = [0] * hp.nb_directions + + if parentPipes: + for k in range(hp.nb_directions): + parentPipe = parentPipes[k] + parentPipe.send([_EXPLORE, [normalizer, policy, hp, "positive", deltas[k]]]) + for k in range(hp.nb_directions): + positive_rewards[k] = parentPipes[k].recv()[0] + + for k in range(hp.nb_directions): + parentPipe = parentPipes[k] + parentPipe.send([_EXPLORE, [normalizer, policy, hp, "negative", deltas[k]]]) + for k in range(hp.nb_directions): + negative_rewards[k] = parentPipes[k].recv()[0] + + else: + # Getting the positive rewards in the positive directions + for k in range(hp.nb_directions): + positive_rewards[k] = explore(env, normalizer, policy, "positive", deltas[k], hp) + + # Getting the negative rewards in the negative/opposite directions + for k in range(hp.nb_directions): + negative_rewards[k] = explore(env, normalizer, policy, "negative", deltas[k], hp) + + # Gathering all the positive/negative rewards to compute the standard deviation of these rewards + all_rewards = np.array(positive_rewards + negative_rewards) + sigma_r = all_rewards.std() + + # Sorting the rollouts by the max(r_pos, r_neg) and selecting the best directions + scores = { + k: max(r_pos, r_neg) + for k, (r_pos, r_neg) in enumerate(zip(positive_rewards, negative_rewards)) + } + order = sorted(scores.keys(), key=lambda x: scores[x])[:hp.nb_best_directions] + rollouts = [(positive_rewards[k], negative_rewards[k], deltas[k]) for k in order] + + # Updating our policy + policy.update(rollouts, sigma_r, args) + + # Printing the final reward of the policy after the update + reward_evaluation = explore(env, normalizer, policy, None, None, hp) + print('Step:', step, 'Reward:', reward_evaluation) + # Running the main code + def mkdir(base, name): - path = os.path.join(base, name) - if not os.path.exists(path): - os.makedirs(path) - return path + path = os.path.join(base, name) + if not os.path.exists(path): + os.makedirs(path) + return path +if __name__ == "__main__": + mp.freeze_support() + parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter) + parser.add_argument('--env', + help='Gym environment name', + type=str, + default='HalfCheetahBulletEnv-v0') + parser.add_argument('--seed', help='RNG seed', type=int, default=1) + parser.add_argument('--render', help='OpenGL Visualizer', type=int, default=0) + parser.add_argument('--movie', help='rgb_array gym movie', type=int, default=0) + parser.add_argument('--steps', help='Number of steps', type=int, default=10000) + parser.add_argument('--policy', help='Starting policy file (npy)', type=str, default='') + parser.add_argument('--logdir', + help='Directory root to log policy files (npy)', + type=str, + default='.') + parser.add_argument('--mp', help='Enable multiprocessing', type=int, default=1) -if __name__ == "__main__": - mp.freeze_support() - - parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter) - parser.add_argument('--env', help='Gym environment name', type=str, default='HalfCheetahBulletEnv-v0') - parser.add_argument('--seed', help='RNG seed', type=int, default=1) - parser.add_argument('--render', help='OpenGL Visualizer', type=int, default=0) - parser.add_argument('--movie',help='rgb_array gym movie',type=int, default=0) - parser.add_argument('--steps', help='Number of steps', type=int, default=10000) - parser.add_argument('--policy', help='Starting policy file (npy)', type=str, default='') - parser.add_argument('--logdir', help='Directory root to log policy files (npy)', type=str, default='.') - parser.add_argument('--mp', help='Enable multiprocessing', type=int, default=1) - - args = parser.parse_args() - - hp = Hp() - hp.env_name = args.env - hp.seed = args.seed - hp.nb_steps = args.steps - print("seed = ", hp.seed) - np.random.seed(hp.seed) - - parentPipes = None - if args.mp: - num_processes = hp.nb_directions - processes = [] - childPipes = [] - parentPipes = [] - - for pr in range (num_processes): - parentPipe, childPipe = Pipe() - parentPipes.append(parentPipe) - childPipes.append(childPipe) - - for rank in range(num_processes): - p = mp.Process(target=ExploreWorker, args=(rank,childPipes[rank], hp.env_name, args)) - p.start() - processes.append(p) - - work_dir = mkdir('exp', 'brs') - monitor_dir = mkdir(work_dir, 'monitor') - env = gym.make(hp.env_name) - if args.render: - env.render(mode = "human") - if args.movie: - env = wrappers.Monitor(env, monitor_dir, force = True) - nb_inputs = env.observation_space.shape[0] - nb_outputs = env.action_space.shape[0] - policy = Policy(nb_inputs, nb_outputs,hp.env_name, args) - normalizer = Normalizer(nb_inputs) - - print("start training") - train(env, policy, normalizer, hp, parentPipes, args) - - if args.mp: - for parentPipe in parentPipes: - parentPipe.send([_CLOSE,"pay2"]) - - for p in processes: - p.join() + args = parser.parse_args() + + hp = Hp() + hp.env_name = args.env + hp.seed = args.seed + hp.nb_steps = args.steps + print("seed = ", hp.seed) + np.random.seed(hp.seed) + + parentPipes = None + if args.mp: + num_processes = hp.nb_directions + processes = [] + childPipes = [] + parentPipes = [] + + for pr in range(num_processes): + parentPipe, childPipe = Pipe() + parentPipes.append(parentPipe) + childPipes.append(childPipe) + + for rank in range(num_processes): + p = mp.Process(target=ExploreWorker, args=(rank, childPipes[rank], hp.env_name, args)) + p.start() + processes.append(p) + + work_dir = mkdir('exp', 'brs') + monitor_dir = mkdir(work_dir, 'monitor') + env = gym.make(hp.env_name) + if args.render: + env.render(mode="human") + if args.movie: + env = wrappers.Monitor(env, monitor_dir, force=True) + nb_inputs = env.observation_space.shape[0] + nb_outputs = env.action_space.shape[0] + policy = Policy(nb_inputs, nb_outputs, hp.env_name, args) + normalizer = Normalizer(nb_inputs) + + print("start training") + train(env, policy, normalizer, hp, parentPipes, args) + + if args.mp: + for parentPipe in parentPipes: + parentPipe.send([_CLOSE, "pay2"]) + + for p in processes: + p.join() diff --git a/examples/pybullet/gym/pybullet_envs/__init__.py b/examples/pybullet/gym/pybullet_envs/__init__.py index e34508ed2..03c064c36 100644 --- a/examples/pybullet/gym/pybullet_envs/__init__.py +++ b/examples/pybullet/gym/pybullet_envs/__init__.py @@ -1,25 +1,28 @@ import gym from gym.envs.registration import registry, make, spec -def register(id,*args,**kvargs): - if id in registry.env_specs: - return - else: - return gym.envs.registration.register(id,*args,**kvargs) + + +def register(id, *args, **kvargs): + if id in registry.env_specs: + return + else: + return gym.envs.registration.register(id, *args, **kvargs) + # ------------bullet------------- register( - id='HumanoidDeepMimicBulletEnv-v1', - entry_point='pybullet_envs.deep_mimic:HumanoidDeepMimicGymEnv', - max_episode_steps=1000, - reward_threshold=20000.0, + id='HumanoidDeepMimicBulletEnv-v1', + entry_point='pybullet_envs.deep_mimic:HumanoidDeepMimicGymEnv', + max_episode_steps=1000, + reward_threshold=20000.0, ) register( - id='CartPoleBulletEnv-v1', - entry_point='pybullet_envs.bullet:CartPoleBulletEnv', - max_episode_steps=200, - reward_threshold=190.0, + id='CartPoleBulletEnv-v1', + entry_point='pybullet_envs.bullet:CartPoleBulletEnv', + max_episode_steps=200, + reward_threshold=190.0, ) register( @@ -36,7 +39,6 @@ register( reward_threshold=5.0, ) - register( id='MinitaurReactiveEnv-v0', entry_point='pybullet_envs.minitaur.envs:MinitaurReactiveEnv', @@ -44,7 +46,6 @@ register( reward_threshold=5.0, ) - register( id='MinitaurBallGymEnv-v0', entry_point='pybullet_envs.minitaur.envs:MinitaurBallGymEnv', @@ -52,7 +53,6 @@ register( reward_threshold=5.0, ) - register( id='MinitaurTrottingEnv-v0', entry_point='pybullet_envs.minitaur.envs:MinitaurTrottingEnv', @@ -81,8 +81,6 @@ register( reward_threshold=5.0, ) - - register( id='RacecarBulletEnv-v0', entry_point='pybullet_envs.bullet:RacecarGymEnv', @@ -91,128 +89,113 @@ register( ) register( - id='RacecarZedBulletEnv-v0', - entry_point='pybullet_envs.bullet:RacecarZEDGymEnv', - max_episode_steps=1000, - reward_threshold=5.0, + id='RacecarZedBulletEnv-v0', + entry_point='pybullet_envs.bullet:RacecarZEDGymEnv', + max_episode_steps=1000, + reward_threshold=5.0, ) - register( - id='KukaBulletEnv-v0', - entry_point='pybullet_envs.bullet:KukaGymEnv', - max_episode_steps=1000, - reward_threshold=5.0, + id='KukaBulletEnv-v0', + entry_point='pybullet_envs.bullet:KukaGymEnv', + max_episode_steps=1000, + reward_threshold=5.0, ) register( - id='KukaCamBulletEnv-v0', - entry_point='pybullet_envs.bullet:KukaCamGymEnv', - max_episode_steps=1000, - reward_threshold=5.0, + id='KukaCamBulletEnv-v0', + entry_point='pybullet_envs.bullet:KukaCamGymEnv', + max_episode_steps=1000, + reward_threshold=5.0, ) register( - id='KukaDiverseObjectGrasping-v0', - entry_point='pybullet_envs.bullet:KukaDiverseObjectEnv', - max_episode_steps=1000, - reward_threshold=5.0, + id='KukaDiverseObjectGrasping-v0', + entry_point='pybullet_envs.bullet:KukaDiverseObjectEnv', + max_episode_steps=1000, + reward_threshold=5.0, ) register( - id='InvertedPendulumBulletEnv-v0', - entry_point='pybullet_envs.gym_pendulum_envs:InvertedPendulumBulletEnv', - max_episode_steps=1000, - reward_threshold=950.0, - ) + id='InvertedPendulumBulletEnv-v0', + entry_point='pybullet_envs.gym_pendulum_envs:InvertedPendulumBulletEnv', + max_episode_steps=1000, + reward_threshold=950.0, +) register( - id='InvertedDoublePendulumBulletEnv-v0', - entry_point='pybullet_envs.gym_pendulum_envs:InvertedDoublePendulumBulletEnv', - max_episode_steps=1000, - reward_threshold=9100.0, - ) + id='InvertedDoublePendulumBulletEnv-v0', + entry_point='pybullet_envs.gym_pendulum_envs:InvertedDoublePendulumBulletEnv', + max_episode_steps=1000, + reward_threshold=9100.0, +) register( - id='InvertedPendulumSwingupBulletEnv-v0', - entry_point='pybullet_envs.gym_pendulum_envs:InvertedPendulumSwingupBulletEnv', - max_episode_steps=1000, - reward_threshold=800.0, - ) + id='InvertedPendulumSwingupBulletEnv-v0', + entry_point='pybullet_envs.gym_pendulum_envs:InvertedPendulumSwingupBulletEnv', + max_episode_steps=1000, + reward_threshold=800.0, +) register( - id='ReacherBulletEnv-v0', - entry_point='pybullet_envs.gym_manipulator_envs:ReacherBulletEnv', - max_episode_steps=150, - reward_threshold=18.0, - ) + id='ReacherBulletEnv-v0', + entry_point='pybullet_envs.gym_manipulator_envs:ReacherBulletEnv', + max_episode_steps=150, + reward_threshold=18.0, +) register( - id='PusherBulletEnv-v0', - entry_point='pybullet_envs.gym_manipulator_envs:PusherBulletEnv', - max_episode_steps=150, - reward_threshold=18.0, + id='PusherBulletEnv-v0', + entry_point='pybullet_envs.gym_manipulator_envs:PusherBulletEnv', + max_episode_steps=150, + reward_threshold=18.0, ) register( - id='ThrowerBulletEnv-v0', - entry_point='pybullet_envs.gym_manipulator_envs:ThrowerBulletEnv', - max_episode_steps=100, - reward_threshold=18.0, + id='ThrowerBulletEnv-v0', + entry_point='pybullet_envs.gym_manipulator_envs:ThrowerBulletEnv', + max_episode_steps=100, + reward_threshold=18.0, ) register( - id='StrikerBulletEnv-v0', - entry_point='pybullet_envs.gym_manipulator_envs:StrikerBulletEnv', - max_episode_steps=100, - reward_threshold=18.0, + id='StrikerBulletEnv-v0', + entry_point='pybullet_envs.gym_manipulator_envs:StrikerBulletEnv', + max_episode_steps=100, + reward_threshold=18.0, ) -register( - id='Walker2DBulletEnv-v0', - entry_point='pybullet_envs.gym_locomotion_envs:Walker2DBulletEnv', - max_episode_steps=1000, - reward_threshold=2500.0 - ) -register( - id='HalfCheetahBulletEnv-v0', - entry_point='pybullet_envs.gym_locomotion_envs:HalfCheetahBulletEnv', - max_episode_steps=1000, - reward_threshold=3000.0 - ) +register(id='Walker2DBulletEnv-v0', + entry_point='pybullet_envs.gym_locomotion_envs:Walker2DBulletEnv', + max_episode_steps=1000, + reward_threshold=2500.0) +register(id='HalfCheetahBulletEnv-v0', + entry_point='pybullet_envs.gym_locomotion_envs:HalfCheetahBulletEnv', + max_episode_steps=1000, + reward_threshold=3000.0) -register( - id='AntBulletEnv-v0', - entry_point='pybullet_envs.gym_locomotion_envs:AntBulletEnv', - max_episode_steps=1000, - reward_threshold=2500.0 - ) +register(id='AntBulletEnv-v0', + entry_point='pybullet_envs.gym_locomotion_envs:AntBulletEnv', + max_episode_steps=1000, + reward_threshold=2500.0) -register( - id='HopperBulletEnv-v0', - entry_point='pybullet_envs.gym_locomotion_envs:HopperBulletEnv', - max_episode_steps=1000, - reward_threshold=2500.0 - ) +register(id='HopperBulletEnv-v0', + entry_point='pybullet_envs.gym_locomotion_envs:HopperBulletEnv', + max_episode_steps=1000, + reward_threshold=2500.0) -register( - id='HumanoidBulletEnv-v0', - entry_point='pybullet_envs.gym_locomotion_envs:HumanoidBulletEnv', - max_episode_steps=1000 - ) +register(id='HumanoidBulletEnv-v0', + entry_point='pybullet_envs.gym_locomotion_envs:HumanoidBulletEnv', + max_episode_steps=1000) -register( - id='HumanoidFlagrunBulletEnv-v0', - entry_point='pybullet_envs.gym_locomotion_envs:HumanoidFlagrunBulletEnv', - max_episode_steps=1000, - reward_threshold=2000.0 - ) +register(id='HumanoidFlagrunBulletEnv-v0', + entry_point='pybullet_envs.gym_locomotion_envs:HumanoidFlagrunBulletEnv', + max_episode_steps=1000, + reward_threshold=2000.0) -register( - id='HumanoidFlagrunHarderBulletEnv-v0', - entry_point='pybullet_envs.gym_locomotion_envs:HumanoidFlagrunHarderBulletEnv', - max_episode_steps=1000 - ) +register(id='HumanoidFlagrunHarderBulletEnv-v0', + entry_point='pybullet_envs.gym_locomotion_envs:HumanoidFlagrunHarderBulletEnv', + max_episode_steps=1000) #register( # id='AtlasBulletEnv-v0', @@ -220,6 +203,7 @@ register( # max_episode_steps=1000 # ) + def getList(): - btenvs = ['- ' + spec.id for spec in gym.envs.registry.all() if spec.id.find('Bullet')>=0] - return btenvs + btenvs = ['- ' + spec.id for spec in gym.envs.registry.all() if spec.id.find('Bullet') >= 0] + return btenvs diff --git a/examples/pybullet/gym/pybullet_envs/agents/__init__.py b/examples/pybullet/gym/pybullet_envs/agents/__init__.py index c29ce33fe..9fc2d2951 100644 --- a/examples/pybullet/gym/pybullet_envs/agents/__init__.py +++ b/examples/pybullet/gym/pybullet_envs/agents/__init__.py @@ -11,7 +11,6 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. - """Executable scripts for reinforcement learning.""" from __future__ import absolute_import diff --git a/examples/pybullet/gym/pybullet_envs/agents/configs.py b/examples/pybullet/gym/pybullet_envs/agents/configs.py index 6ac584db6..992d226ef 100644 --- a/examples/pybullet/gym/pybullet_envs/agents/configs.py +++ b/examples/pybullet/gym/pybullet_envs/agents/configs.py @@ -11,7 +11,6 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. - """Example configurations using the PPO algorithm.""" from __future__ import absolute_import @@ -29,6 +28,7 @@ import pybullet_envs.bullet.minitaur_gym_env as minitaur_gym_env import pybullet_envs import tensorflow as tf + def default(): """Default configuration for PPO.""" # General @@ -38,10 +38,7 @@ def default(): use_gpu = False # Network network = networks.feed_forward_gaussian - weight_summaries = dict( - all=r'.*', - policy=r'.*/policy/.*', - value=r'.*/value/.*') + weight_summaries = dict(all=r'.*', policy=r'.*/policy/.*', value=r'.*/value/.*') policy_layers = 200, 100 value_layers = 200, 100 init_mean_factor = 0.1 @@ -52,7 +49,7 @@ def default(): optimizer = tf.train.AdamOptimizer update_epochs_policy = 64 update_epochs_value = 64 - learning_rate = 1e-4 + learning_rate = 1e-4 # Losses discount = 0.995 kl_target = 1e-2 @@ -69,6 +66,7 @@ def pybullet_pendulum(): steps = 5e7 # 50M return locals() + def pybullet_doublependulum(): locals().update(default()) env = 'InvertedDoublePendulumBulletEnv-v0' @@ -76,6 +74,7 @@ def pybullet_doublependulum(): steps = 5e7 # 50M return locals() + def pybullet_pendulumswingup(): locals().update(default()) env = 'InvertedPendulumSwingupBulletEnv-v0' @@ -83,6 +82,7 @@ def pybullet_pendulumswingup(): steps = 5e7 # 50M return locals() + def pybullet_cheetah(): """Configuration for MuJoCo's half cheetah task.""" locals().update(default()) @@ -92,6 +92,7 @@ def pybullet_cheetah(): steps = 1e8 # 100M return locals() + def pybullet_ant(): locals().update(default()) env = 'AntBulletEnv-v0' @@ -99,6 +100,7 @@ def pybullet_ant(): steps = 5e7 # 50M return locals() + def pybullet_kuka_grasping(): """Configuration for Bullet Kuka grasping task.""" locals().update(default()) @@ -113,7 +115,7 @@ def pybullet_racecar(): """Configuration for Bullet MIT Racecar task.""" locals().update(default()) # Environment - env = 'RacecarBulletEnv-v0' #functools.partial(racecarGymEnv.RacecarGymEnv, isDiscrete=False, renders=True) + env = 'RacecarBulletEnv-v0' #functools.partial(racecarGymEnv.RacecarGymEnv, isDiscrete=False, renders=True) max_length = 10 steps = 1e7 # 10M return locals() @@ -132,29 +134,27 @@ def pybullet_minitaur(): """Configuration specific to minitaur_gym_env.MinitaurBulletEnv class.""" locals().update(default()) randomizer = (minitaur_env_randomizer.MinitaurEnvRandomizer()) - env = functools.partial( - minitaur_gym_env.MinitaurBulletEnv, - accurate_motor_model_enabled=True, - motor_overheat_protection=True, - pd_control_enabled=True, - env_randomizer=randomizer, - render=False) + env = functools.partial(minitaur_gym_env.MinitaurBulletEnv, + accurate_motor_model_enabled=True, + motor_overheat_protection=True, + pd_control_enabled=True, + env_randomizer=randomizer, + render=False) max_length = 1000 steps = 3e7 # 30M return locals() + def pybullet_duck_minitaur(): """Configuration specific to minitaur_gym_env.MinitaurBulletDuckEnv class.""" locals().update(default()) randomizer = (minitaur_env_randomizer.MinitaurEnvRandomizer()) - env = functools.partial( - minitaur_gym_env.MinitaurBulletDuckEnv, - accurate_motor_model_enabled=True, - motor_overheat_protection=True, - pd_control_enabled=True, - env_randomizer=randomizer, - render=False) + env = functools.partial(minitaur_gym_env.MinitaurBulletDuckEnv, + accurate_motor_model_enabled=True, + motor_overheat_protection=True, + pd_control_enabled=True, + env_randomizer=randomizer, + render=False) max_length = 1000 steps = 3e7 # 30M return locals() - diff --git a/examples/pybullet/gym/pybullet_envs/agents/networks.py b/examples/pybullet/gym/pybullet_envs/agents/networks.py index 3d5de1fbb..3b6802d02 100644 --- a/examples/pybullet/gym/pybullet_envs/agents/networks.py +++ b/examples/pybullet/gym/pybullet_envs/agents/networks.py @@ -11,7 +11,6 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. - """Network definitions for the PPO algorithm.""" from __future__ import absolute_import @@ -24,13 +23,10 @@ import operator import tensorflow as tf - -NetworkOutput = collections.namedtuple( - 'NetworkOutput', 'policy, mean, logstd, value, state') +NetworkOutput = collections.namedtuple('NetworkOutput', 'policy, mean, logstd, value, state') -def feed_forward_gaussian( - config, action_size, observations, unused_length, state=None): +def feed_forward_gaussian(config, action_size, observations, unused_length, state=None): """Independent feed forward networks for policy and value. The policy network outputs the mean action and the log standard deviation @@ -50,20 +46,22 @@ def feed_forward_gaussian( factor=config.init_mean_factor) logstd_initializer = tf.random_normal_initializer(config.init_logstd, 1e-10) flat_observations = tf.reshape(observations, [ - tf.shape(observations)[0], tf.shape(observations)[1], - functools.reduce(operator.mul, observations.shape.as_list()[2:], 1)]) + tf.shape(observations)[0], + tf.shape(observations)[1], + functools.reduce(operator.mul, + observations.shape.as_list()[2:], 1) + ]) with tf.variable_scope('policy'): x = flat_observations for size in config.policy_layers: x = tf.contrib.layers.fully_connected(x, size, tf.nn.relu) - mean = tf.contrib.layers.fully_connected( - x, action_size, tf.tanh, - weights_initializer=mean_weights_initializer) - logstd = tf.get_variable( - 'logstd', mean.shape[2:], tf.float32, logstd_initializer) - logstd = tf.tile( - logstd[None, None], - [tf.shape(mean)[0], tf.shape(mean)[1]] + [1] * (mean.shape.ndims - 2)) + mean = tf.contrib.layers.fully_connected(x, + action_size, + tf.tanh, + weights_initializer=mean_weights_initializer) + logstd = tf.get_variable('logstd', mean.shape[2:], tf.float32, logstd_initializer) + logstd = tf.tile(logstd[None, None], + [tf.shape(mean)[0], tf.shape(mean)[1]] + [1] * (mean.shape.ndims - 2)) with tf.variable_scope('value'): x = flat_observations for size in config.value_layers: @@ -72,13 +70,11 @@ def feed_forward_gaussian( mean = tf.check_numerics(mean, 'mean') logstd = tf.check_numerics(logstd, 'logstd') value = tf.check_numerics(value, 'value') - policy = tf.contrib.distributions.MultivariateNormalDiag( - mean, tf.exp(logstd)) + policy = tf.contrib.distributions.MultivariateNormalDiag(mean, tf.exp(logstd)) return NetworkOutput(policy, mean, logstd, value, state) -def recurrent_gaussian( - config, action_size, observations, length, state=None): +def recurrent_gaussian(config, action_size, observations, length, state=None): """Independent recurrent policy and feed forward value networks. The policy network outputs the mean action and the log standard deviation @@ -100,21 +96,23 @@ def recurrent_gaussian( logstd_initializer = tf.random_normal_initializer(config.init_logstd, 1e-10) cell = tf.contrib.rnn.GRUBlockCell(config.policy_layers[-1]) flat_observations = tf.reshape(observations, [ - tf.shape(observations)[0], tf.shape(observations)[1], - functools.reduce(operator.mul, observations.shape.as_list()[2:], 1)]) + tf.shape(observations)[0], + tf.shape(observations)[1], + functools.reduce(operator.mul, + observations.shape.as_list()[2:], 1) + ]) with tf.variable_scope('policy'): x = flat_observations for size in config.policy_layers[:-1]: x = tf.contrib.layers.fully_connected(x, size, tf.nn.relu) x, state = tf.nn.dynamic_rnn(cell, x, length, state, tf.float32) - mean = tf.contrib.layers.fully_connected( - x, action_size, tf.tanh, - weights_initializer=mean_weights_initializer) - logstd = tf.get_variable( - 'logstd', mean.shape[2:], tf.float32, logstd_initializer) - logstd = tf.tile( - logstd[None, None], - [tf.shape(mean)[0], tf.shape(mean)[1]] + [1] * (mean.shape.ndims - 2)) + mean = tf.contrib.layers.fully_connected(x, + action_size, + tf.tanh, + weights_initializer=mean_weights_initializer) + logstd = tf.get_variable('logstd', mean.shape[2:], tf.float32, logstd_initializer) + logstd = tf.tile(logstd[None, None], + [tf.shape(mean)[0], tf.shape(mean)[1]] + [1] * (mean.shape.ndims - 2)) with tf.variable_scope('value'): x = flat_observations for size in config.value_layers: @@ -123,7 +121,6 @@ def recurrent_gaussian( mean = tf.check_numerics(mean, 'mean') logstd = tf.check_numerics(logstd, 'logstd') value = tf.check_numerics(value, 'value') - policy = tf.contrib.distributions.MultivariateNormalDiag( - mean, tf.exp(logstd)) + policy = tf.contrib.distributions.MultivariateNormalDiag(mean, tf.exp(logstd)) # assert state.shape.as_list()[0] is not None return NetworkOutput(policy, mean, logstd, value, state) diff --git a/examples/pybullet/gym/pybullet_envs/agents/ppo/__init__.py b/examples/pybullet/gym/pybullet_envs/agents/ppo/__init__.py index 26a87baf9..6f4c222dd 100644 --- a/examples/pybullet/gym/pybullet_envs/agents/ppo/__init__.py +++ b/examples/pybullet/gym/pybullet_envs/agents/ppo/__init__.py @@ -11,7 +11,6 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. - """Proximal Policy Optimization algorithm.""" from __future__ import absolute_import diff --git a/examples/pybullet/gym/pybullet_envs/agents/ppo/algorithm.py b/examples/pybullet/gym/pybullet_envs/agents/ppo/algorithm.py index 9e97425f9..c6921095f 100644 --- a/examples/pybullet/gym/pybullet_envs/agents/ppo/algorithm.py +++ b/examples/pybullet/gym/pybullet_envs/agents/ppo/algorithm.py @@ -11,7 +11,6 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. - """Proximal Policy Optimization algorithm. Based on John Schulman's implementation in Python and Theano: @@ -49,51 +48,51 @@ class PPOAlgorithm(object): self._is_training = is_training self._should_log = should_log self._config = config - self._observ_filter = normalize.StreamingNormalize( - self._batch_env.observ[0], center=True, scale=True, clip=5, - name='normalize_observ') - self._reward_filter = normalize.StreamingNormalize( - self._batch_env.reward[0], center=False, scale=True, clip=10, - name='normalize_reward') + self._observ_filter = normalize.StreamingNormalize(self._batch_env.observ[0], + center=True, + scale=True, + clip=5, + name='normalize_observ') + self._reward_filter = normalize.StreamingNormalize(self._batch_env.reward[0], + center=False, + scale=True, + clip=10, + name='normalize_reward') # Memory stores tuple of observ, action, mean, logstd, reward. - template = ( - self._batch_env.observ[0], self._batch_env.action[0], - self._batch_env.action[0], self._batch_env.action[0], - self._batch_env.reward[0]) - self._memory = memory.EpisodeMemory( - template, config.update_every, config.max_length, 'memory') + template = (self._batch_env.observ[0], self._batch_env.action[0], self._batch_env.action[0], + self._batch_env.action[0], self._batch_env.reward[0]) + self._memory = memory.EpisodeMemory(template, config.update_every, config.max_length, 'memory') self._memory_index = tf.Variable(0, False) use_gpu = self._config.use_gpu and utility.available_gpus() with tf.device('/gpu:0' if use_gpu else '/cpu:0'): # Create network variables for later calls to reuse. action_size = self._batch_env.action.shape[1].value - self._network = tf.make_template( - 'network', functools.partial(config.network, config, action_size)) + self._network = tf.make_template('network', + functools.partial(config.network, config, action_size)) output = self._network( - tf.zeros_like(self._batch_env.observ)[:, None], - tf.ones(len(self._batch_env))) + tf.zeros_like(self._batch_env.observ)[:, None], tf.ones(len(self._batch_env))) with tf.variable_scope('ppo_temporary'): - self._episodes = memory.EpisodeMemory( - template, len(batch_env), config.max_length, 'episodes') + self._episodes = memory.EpisodeMemory(template, len(batch_env), config.max_length, + 'episodes') if output.state is None: self._last_state = None else: # Ensure the batch dimension is set. tf.contrib.framework.nest.map_structure( - lambda x: x.set_shape([len(batch_env)] + x.shape.as_list()[1:]), - output.state) + lambda x: x.set_shape([len(batch_env)] + x.shape.as_list()[1:]), output.state) # pylint: disable=undefined-variable self._last_state = tf.contrib.framework.nest.map_structure( - lambda x: tf.Variable(lambda: tf.zeros_like(x), False), - output.state) - self._last_action = tf.Variable( - tf.zeros_like(self._batch_env.action), False, name='last_action') - self._last_mean = tf.Variable( - tf.zeros_like(self._batch_env.action), False, name='last_mean') - self._last_logstd = tf.Variable( - tf.zeros_like(self._batch_env.action), False, name='last_logstd') - self._penalty = tf.Variable( - self._config.kl_init_penalty, False, dtype=tf.float32) + lambda x: tf.Variable(lambda: tf.zeros_like(x), False), output.state) + self._last_action = tf.Variable(tf.zeros_like(self._batch_env.action), + False, + name='last_action') + self._last_mean = tf.Variable(tf.zeros_like(self._batch_env.action), + False, + name='last_mean') + self._last_logstd = tf.Variable(tf.zeros_like(self._batch_env.action), + False, + name='last_logstd') + self._penalty = tf.Variable(self._config.kl_init_penalty, False, dtype=tf.float32) self._optimizer = self._config.optimizer(self._config.learning_rate) def begin_episode(self, agent_indices): @@ -109,8 +108,7 @@ class PPOAlgorithm(object): if self._last_state is None: reset_state = tf.no_op() else: - reset_state = utility.reinit_nested_vars( - self._last_state, agent_indices) + reset_state = utility.reinit_nested_vars(self._last_state, agent_indices) reset_buffer = self._episodes.clear(agent_indices) with tf.control_dependencies([reset_state, reset_buffer]): return tf.constant('') @@ -130,36 +128,33 @@ class PPOAlgorithm(object): if self._last_state is None: state = None else: - state = tf.contrib.framework.nest.map_structure( - lambda x: tf.gather(x, agent_indices), self._last_state) + state = tf.contrib.framework.nest.map_structure(lambda x: tf.gather(x, agent_indices), + self._last_state) output = self._network(observ[:, None], tf.ones(observ.shape[0]), state) - action = tf.cond( - self._is_training, output.policy.sample, lambda: output.mean) + action = tf.cond(self._is_training, output.policy.sample, lambda: output.mean) logprob = output.policy.log_prob(action)[:, 0] # pylint: disable=g-long-lambda - summary = tf.cond(self._should_log, lambda: tf.summary.merge([ - tf.summary.histogram('mean', output.mean[:, 0]), - tf.summary.histogram('std', tf.exp(output.logstd[:, 0])), - tf.summary.histogram('action', action[:, 0]), - tf.summary.histogram('logprob', logprob)]), str) + summary = tf.cond( + self._should_log, lambda: tf.summary.merge([ + tf.summary.histogram('mean', output.mean[:, 0]), + tf.summary.histogram('std', tf.exp(output.logstd[:, 0])), + tf.summary.histogram('action', action[:, 0]), + tf.summary.histogram('logprob', logprob) + ]), str) # Remember current policy to append to memory in the experience callback. if self._last_state is None: assign_state = tf.no_op() else: - assign_state = utility.assign_nested_vars( - self._last_state, output.state, agent_indices) + assign_state = utility.assign_nested_vars(self._last_state, output.state, agent_indices) with tf.control_dependencies([ assign_state, - tf.scatter_update( - self._last_action, agent_indices, action[:, 0]), - tf.scatter_update( - self._last_mean, agent_indices, output.mean[:, 0]), - tf.scatter_update( - self._last_logstd, agent_indices, output.logstd[:, 0])]): + tf.scatter_update(self._last_action, agent_indices, action[:, 0]), + tf.scatter_update(self._last_mean, agent_indices, output.mean[:, 0]), + tf.scatter_update(self._last_logstd, agent_indices, output.logstd[:, 0]) + ]): return tf.check_numerics(action[:, 0], 'action'), tf.identity(summary) - def experience( - self, agent_indices, observ, action, reward, unused_done, unused_nextob): + def experience(self, agent_indices, observ, action, reward, unused_done, unused_nextob): """Process the transition tuple of the current step. When training, add the current transition tuple to the memory and update @@ -181,34 +176,36 @@ class PPOAlgorithm(object): return tf.cond( self._is_training, # pylint: disable=g-long-lambda - lambda: self._define_experience( - agent_indices, observ, action, reward), str) + lambda: self._define_experience(agent_indices, observ, action, reward), + str) def _define_experience(self, agent_indices, observ, action, reward): """Implement the branch of experience() entered during training.""" - update_filters = tf.summary.merge([ - self._observ_filter.update(observ), - self._reward_filter.update(reward)]) + update_filters = tf.summary.merge( + [self._observ_filter.update(observ), + self._reward_filter.update(reward)]) with tf.control_dependencies([update_filters]): if self._config.train_on_agent_action: # NOTE: Doesn't seem to change much. action = self._last_action - batch = ( - observ, action, tf.gather(self._last_mean, agent_indices), - tf.gather(self._last_logstd, agent_indices), reward) + batch = (observ, action, tf.gather(self._last_mean, + agent_indices), tf.gather(self._last_logstd, + agent_indices), reward) append = self._episodes.append(batch, agent_indices) with tf.control_dependencies([append]): norm_observ = self._observ_filter.transform(observ) norm_reward = tf.reduce_mean(self._reward_filter.transform(reward)) # pylint: disable=g-long-lambda - summary = tf.cond(self._should_log, lambda: tf.summary.merge([ - update_filters, - self._observ_filter.summary(), - self._reward_filter.summary(), - tf.summary.scalar('memory_size', self._memory_index), - tf.summary.histogram('normalized_observ', norm_observ), - tf.summary.histogram('action', self._last_action), - tf.summary.scalar('normalized_reward', norm_reward)]), str) + summary = tf.cond( + self._should_log, lambda: tf.summary.merge([ + update_filters, + self._observ_filter.summary(), + self._reward_filter.summary(), + tf.summary.scalar('memory_size', self._memory_index), + tf.summary.histogram('normalized_observ', norm_observ), + tf.summary.histogram('action', self._last_action), + tf.summary.scalar('normalized_reward', norm_reward) + ]), str) return summary def end_episode(self, agent_indices): @@ -226,20 +223,16 @@ class PPOAlgorithm(object): Summary tensor. """ with tf.name_scope('end_episode/'): - return tf.cond( - self._is_training, - lambda: self._define_end_episode(agent_indices), str) + return tf.cond(self._is_training, lambda: self._define_end_episode(agent_indices), str) def _define_end_episode(self, agent_indices): """Implement the branch of end_episode() entered during training.""" episodes, length = self._episodes.data(agent_indices) space_left = self._config.update_every - self._memory_index - use_episodes = tf.range(tf.minimum( - tf.shape(agent_indices)[0], space_left)) + use_episodes = tf.range(tf.minimum(tf.shape(agent_indices)[0], space_left)) episodes = [tf.gather(elem, use_episodes) for elem in episodes] - append = self._memory.replace( - episodes, tf.gather(length, use_episodes), - use_episodes + self._memory_index) + append = self._memory.replace(episodes, tf.gather(length, use_episodes), + use_episodes + self._memory_index) with tf.control_dependencies([append]): inc_index = self._memory_index.assign_add(tf.shape(use_episodes)[0]) with tf.control_dependencies([inc_index]): @@ -256,8 +249,7 @@ class PPOAlgorithm(object): Summary tensor. """ with tf.name_scope('training'): - assert_full = tf.assert_equal( - self._memory_index, self._config.update_every) + assert_full = tf.assert_equal(self._memory_index, self._config.update_every) with tf.control_dependencies([assert_full]): data = self._memory.data() (observ, action, old_mean, old_logstd, reward), length = data @@ -265,22 +257,18 @@ class PPOAlgorithm(object): length = tf.identity(length) observ = self._observ_filter.transform(observ) reward = self._reward_filter.transform(reward) - update_summary = self._perform_update_steps( - observ, action, old_mean, old_logstd, reward, length) + update_summary = self._perform_update_steps(observ, action, old_mean, old_logstd, reward, + length) with tf.control_dependencies([update_summary]): - penalty_summary = self._adjust_penalty( - observ, old_mean, old_logstd, length) + penalty_summary = self._adjust_penalty(observ, old_mean, old_logstd, length) with tf.control_dependencies([penalty_summary]): - clear_memory = tf.group( - self._memory.clear(), self._memory_index.assign(0)) + clear_memory = tf.group(self._memory.clear(), self._memory_index.assign(0)) with tf.control_dependencies([clear_memory]): - weight_summary = utility.variable_summaries( - tf.trainable_variables(), self._config.weight_summaries) - return tf.summary.merge([ - update_summary, penalty_summary, weight_summary]) + weight_summary = utility.variable_summaries(tf.trainable_variables(), + self._config.weight_summaries) + return tf.summary.merge([update_summary, penalty_summary, weight_summary]) - def _perform_update_steps( - self, observ, action, old_mean, old_logstd, reward, length): + def _perform_update_steps(self, observ, action, old_mean, old_logstd, reward, length): """Perform multiple update steps of value function and policy. The advantage is computed once at the beginning and shared across @@ -298,37 +286,29 @@ class PPOAlgorithm(object): Returns: Summary tensor. """ - return_ = utility.discounted_return( - reward, length, self._config.discount) + return_ = utility.discounted_return(reward, length, self._config.discount) value = self._network(observ, length).value if self._config.gae_lambda: - advantage = utility.lambda_return( - reward, value, length, self._config.discount, - self._config.gae_lambda) + advantage = utility.lambda_return(reward, value, length, self._config.discount, + self._config.gae_lambda) else: advantage = return_ - value mean, variance = tf.nn.moments(advantage, axes=[0, 1], keep_dims=True) advantage = (advantage - mean) / (tf.sqrt(variance) + 1e-8) - advantage = tf.Print( - advantage, [tf.reduce_mean(return_), tf.reduce_mean(value)], - 'return and value: ') - advantage = tf.Print( - advantage, [tf.reduce_mean(advantage)], - 'normalized advantage: ') + advantage = tf.Print(advantage, + [tf.reduce_mean(return_), tf.reduce_mean(value)], 'return and value: ') + advantage = tf.Print(advantage, [tf.reduce_mean(advantage)], 'normalized advantage: ') # pylint: disable=g-long-lambda - value_loss, policy_loss, summary = tf.scan( - lambda _1, _2: self._update_step( - observ, action, old_mean, old_logstd, reward, advantage, length), - tf.range(self._config.update_epochs), - [0., 0., ''], parallel_iterations=1) - print_losses = tf.group( - tf.Print(0, [tf.reduce_mean(value_loss)], 'value loss: '), - tf.Print(0, [tf.reduce_mean(policy_loss)], 'policy loss: ')) + value_loss, policy_loss, summary = tf.scan(lambda _1, _2: self._update_step( + observ, action, old_mean, old_logstd, reward, advantage, length), + tf.range(self._config.update_epochs), [0., 0., ''], + parallel_iterations=1) + print_losses = tf.group(tf.Print(0, [tf.reduce_mean(value_loss)], 'value loss: '), + tf.Print(0, [tf.reduce_mean(policy_loss)], 'policy loss: ')) with tf.control_dependencies([value_loss, policy_loss, print_losses]): return summary[self._config.update_epochs // 2] - def _update_step( - self, observ, action, old_mean, old_logstd, reward, advantage, length): + def _update_step(self, observ, action, old_mean, old_logstd, reward, advantage, length): """Compute the current combined loss and perform a gradient update step. Args: @@ -345,27 +325,20 @@ class PPOAlgorithm(object): """ value_loss, value_summary = self._value_loss(observ, reward, length) network = self._network(observ, length) - policy_loss, policy_summary = self._policy_loss( - network.mean, network.logstd, old_mean, old_logstd, action, - advantage, length) - value_gradients, value_variables = ( - zip(*self._optimizer.compute_gradients(value_loss))) - policy_gradients, policy_variables = ( - zip(*self._optimizer.compute_gradients(policy_loss))) + policy_loss, policy_summary = self._policy_loss(network.mean, network.logstd, old_mean, + old_logstd, action, advantage, length) + value_gradients, value_variables = (zip(*self._optimizer.compute_gradients(value_loss))) + policy_gradients, policy_variables = (zip(*self._optimizer.compute_gradients(policy_loss))) all_gradients = value_gradients + policy_gradients all_variables = value_variables + policy_variables - optimize = self._optimizer.apply_gradients( - zip(all_gradients, all_variables)) + optimize = self._optimizer.apply_gradients(zip(all_gradients, all_variables)) summary = tf.summary.merge([ value_summary, policy_summary, - tf.summary.scalar( - 'value_gradient_norm', tf.global_norm(value_gradients)), - tf.summary.scalar( - 'policy_gradient_norm', tf.global_norm(policy_gradients)), - utility.gradient_summaries( - zip(value_gradients, value_variables), dict(value=r'.*')), - utility.gradient_summaries( - zip(policy_gradients, policy_variables), dict(policy=r'.*'))]) + tf.summary.scalar('value_gradient_norm', tf.global_norm(value_gradients)), + tf.summary.scalar('policy_gradient_norm', tf.global_norm(policy_gradients)), + utility.gradient_summaries(zip(value_gradients, value_variables), dict(value=r'.*')), + utility.gradient_summaries(zip(policy_gradients, policy_variables), dict(policy=r'.*')) + ]) with tf.control_dependencies([optimize]): return [tf.identity(x) for x in (value_loss, policy_loss, summary)] @@ -385,18 +358,17 @@ class PPOAlgorithm(object): """ with tf.name_scope('value_loss'): value = self._network(observ, length).value - return_ = utility.discounted_return( - reward, length, self._config.discount) + return_ = utility.discounted_return(reward, length, self._config.discount) advantage = return_ - value - value_loss = 0.5 * self._mask(advantage ** 2, length) + value_loss = 0.5 * self._mask(advantage**2, length) summary = tf.summary.merge([ tf.summary.histogram('value_loss', value_loss), - tf.summary.scalar('avg_value_loss', tf.reduce_mean(value_loss))]) + tf.summary.scalar('avg_value_loss', tf.reduce_mean(value_loss)) + ]) value_loss = tf.reduce_mean(value_loss) return tf.check_numerics(value_loss, 'value_loss'), summary - def _policy_loss( - self, mean, logstd, old_mean, old_logstd, action, advantage, length): + def _policy_loss(self, mean, logstd, old_mean, old_logstd, action, advantage, length): """Compute the policy loss composed of multiple components. 1. The policy gradient loss is importance sampled from the data-collecting @@ -420,24 +392,20 @@ class PPOAlgorithm(object): """ with tf.name_scope('policy_loss'): entropy = utility.diag_normal_entropy(mean, logstd) - kl = tf.reduce_mean(self._mask(utility.diag_normal_kl( - old_mean, old_logstd, mean, logstd), length), 1) + kl = tf.reduce_mean( + self._mask(utility.diag_normal_kl(old_mean, old_logstd, mean, logstd), length), 1) policy_gradient = tf.exp( utility.diag_normal_logpdf(mean, logstd, action) - utility.diag_normal_logpdf(old_mean, old_logstd, action)) - surrogate_loss = -tf.reduce_mean(self._mask( - policy_gradient * tf.stop_gradient(advantage), length), 1) + surrogate_loss = -tf.reduce_mean( + self._mask(policy_gradient * tf.stop_gradient(advantage), length), 1) kl_penalty = self._penalty * kl cutoff_threshold = self._config.kl_target * self._config.kl_cutoff_factor - cutoff_count = tf.reduce_sum( - tf.cast(kl > cutoff_threshold, tf.int32)) - with tf.control_dependencies([tf.cond( - cutoff_count > 0, - lambda: tf.Print(0, [cutoff_count], 'kl cutoff! '), int)]): - kl_cutoff = ( - self._config.kl_cutoff_coef * - tf.cast(kl > cutoff_threshold, tf.float32) * - (kl - cutoff_threshold) ** 2) + cutoff_count = tf.reduce_sum(tf.cast(kl > cutoff_threshold, tf.int32)) + with tf.control_dependencies( + [tf.cond(cutoff_count > 0, lambda: tf.Print(0, [cutoff_count], 'kl cutoff! '), int)]): + kl_cutoff = (self._config.kl_cutoff_coef * tf.cast(kl > cutoff_threshold, tf.float32) * + (kl - cutoff_threshold)**2) policy_loss = surrogate_loss + kl_penalty + kl_cutoff summary = tf.summary.merge([ tf.summary.histogram('entropy', entropy), @@ -449,7 +417,8 @@ class PPOAlgorithm(object): tf.summary.histogram('policy_loss', policy_loss), tf.summary.scalar('avg_surr_loss', tf.reduce_mean(surrogate_loss)), tf.summary.scalar('avg_kl_penalty', tf.reduce_mean(kl_penalty)), - tf.summary.scalar('avg_policy_loss', tf.reduce_mean(policy_loss))]) + tf.summary.scalar('avg_policy_loss', tf.reduce_mean(policy_loss)) + ]) policy_loss = tf.reduce_mean(policy_loss, 0) return tf.check_numerics(policy_loss, 'policy_loss'), summary @@ -471,30 +440,30 @@ class PPOAlgorithm(object): """ with tf.name_scope('adjust_penalty'): network = self._network(observ, length) - assert_change = tf.assert_equal( - tf.reduce_all(tf.equal(network.mean, old_mean)), False, - message='policy should change') + assert_change = tf.assert_equal(tf.reduce_all(tf.equal(network.mean, old_mean)), + False, + message='policy should change') print_penalty = tf.Print(0, [self._penalty], 'current penalty: ') with tf.control_dependencies([assert_change, print_penalty]): - kl_change = tf.reduce_mean(self._mask(utility.diag_normal_kl( - old_mean, old_logstd, network.mean, network.logstd), length)) + kl_change = tf.reduce_mean( + self._mask(utility.diag_normal_kl(old_mean, old_logstd, network.mean, network.logstd), + length)) kl_change = tf.Print(kl_change, [kl_change], 'kl change: ') maybe_increase = tf.cond( kl_change > 1.3 * self._config.kl_target, # pylint: disable=g-long-lambda - lambda: tf.Print(self._penalty.assign( - self._penalty * 1.5), [0], 'increase penalty '), + lambda: tf.Print(self._penalty.assign(self._penalty * 1.5), [0], 'increase penalty '), float) maybe_decrease = tf.cond( kl_change < 0.7 * self._config.kl_target, # pylint: disable=g-long-lambda - lambda: tf.Print(self._penalty.assign( - self._penalty / 1.5), [0], 'decrease penalty '), + lambda: tf.Print(self._penalty.assign(self._penalty / 1.5), [0], 'decrease penalty '), float) with tf.control_dependencies([maybe_increase, maybe_decrease]): return tf.summary.merge([ tf.summary.scalar('kl_change', kl_change), - tf.summary.scalar('penalty', self._penalty)]) + tf.summary.scalar('penalty', self._penalty) + ]) def _mask(self, tensor, length): """Set padding elements of a batch of sequences to zero. diff --git a/examples/pybullet/gym/pybullet_envs/agents/ppo/memory.py b/examples/pybullet/gym/pybullet_envs/agents/ppo/memory.py index 10e79ef72..eee19ac07 100644 --- a/examples/pybullet/gym/pybullet_envs/agents/ppo/memory.py +++ b/examples/pybullet/gym/pybullet_envs/agents/ppo/memory.py @@ -11,7 +11,6 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. - """Memory that stores episodes.""" from __future__ import absolute_import @@ -43,10 +42,9 @@ class EpisodeMemory(object): self._scope = var_scope self._length = tf.Variable(tf.zeros(capacity, tf.int32), False) self._buffers = [ - tf.Variable(tf.zeros( - [capacity, max_length] + elem.shape.as_list(), - elem.dtype), False) - for elem in template] + tf.Variable(tf.zeros([capacity, max_length] + elem.shape.as_list(), elem.dtype), False) + for elem in template + ] def length(self, rows=None): """Tensor holding the current length of episodes. @@ -72,13 +70,11 @@ class EpisodeMemory(object): """ rows = tf.range(self._capacity) if rows is None else rows assert rows.shape.ndims == 1 - assert_capacity = tf.assert_less( - rows, self._capacity, - message='capacity exceeded') + assert_capacity = tf.assert_less(rows, self._capacity, message='capacity exceeded') with tf.control_dependencies([assert_capacity]): - assert_max_length = tf.assert_less( - tf.gather(self._length, rows), self._max_length, - message='max length exceeded') + assert_max_length = tf.assert_less(tf.gather(self._length, rows), + self._max_length, + message='max length exceeded') append_ops = [] with tf.control_dependencies([assert_max_length]): for buffer_, elements in zip(self._buffers, transitions): @@ -86,8 +82,7 @@ class EpisodeMemory(object): indices = tf.stack([rows, timestep], 1) append_ops.append(tf.scatter_nd_update(buffer_, indices, elements)) with tf.control_dependencies(append_ops): - episode_mask = tf.reduce_sum(tf.one_hot( - rows, self._capacity, dtype=tf.int32), 0) + episode_mask = tf.reduce_sum(tf.one_hot(rows, self._capacity, dtype=tf.int32), 0) return self._length.assign_add(episode_mask) def replace(self, episodes, length, rows=None): @@ -103,11 +98,11 @@ class EpisodeMemory(object): """ rows = tf.range(self._capacity) if rows is None else rows assert rows.shape.ndims == 1 - assert_capacity = tf.assert_less( - rows, self._capacity, message='capacity exceeded') + assert_capacity = tf.assert_less(rows, self._capacity, message='capacity exceeded') with tf.control_dependencies([assert_capacity]): - assert_max_length = tf.assert_less_equal( - length, self._max_length, message='max length exceeded') + assert_max_length = tf.assert_less_equal(length, + self._max_length, + message='max length exceeded') replace_ops = [] with tf.control_dependencies([assert_max_length]): for buffer_, elements in zip(self._buffers, episodes): diff --git a/examples/pybullet/gym/pybullet_envs/agents/ppo/normalize.py b/examples/pybullet/gym/pybullet_envs/agents/ppo/normalize.py index 6c4170519..4de38fd25 100644 --- a/examples/pybullet/gym/pybullet_envs/agents/ppo/normalize.py +++ b/examples/pybullet/gym/pybullet_envs/agents/ppo/normalize.py @@ -11,7 +11,6 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. - """Normalize tensors based on streaming estimates of mean and variance.""" from __future__ import absolute_import @@ -24,8 +23,7 @@ import tensorflow as tf class StreamingNormalize(object): """Normalize tensors based on streaming estimates of mean and variance.""" - def __init__( - self, template, center=True, scale=True, clip=10, name='normalize'): + def __init__(self, template, center=True, scale=True, clip=10, name='normalize'): """Normalize tensors based on streaming estimates of mean and variance. Centering the value, scaling it by the standard deviation, and clipping @@ -69,8 +67,7 @@ class StreamingNormalize(object): if self._scale: # We cannot scale before seeing at least two samples. value /= tf.cond( - self._count > 1, lambda: self._std() + 1e-8, - lambda: tf.ones_like(self._var_sum))[None] + self._count > 1, lambda: self._std() + 1e-8, lambda: tf.ones_like(self._var_sum))[None] if self._clip: value = tf.clip_by_value(value, -self._clip, self._clip) # Remove batch dimension if necessary. @@ -97,8 +94,7 @@ class StreamingNormalize(object): mean_delta = tf.reduce_sum(value - self._mean[None, ...], 0) new_mean = self._mean + mean_delta / step new_mean = tf.cond(self._count > 1, lambda: new_mean, lambda: value[0]) - var_delta = ( - value - self._mean[None, ...]) * (value - new_mean[None, ...]) + var_delta = (value - self._mean[None, ...]) * (value - new_mean[None, ...]) new_var_sum = self._var_sum + tf.reduce_sum(var_delta, 0) with tf.control_dependencies([new_mean, new_var_sum]): update = self._mean.assign(new_mean), self._var_sum.assign(new_var_sum) @@ -116,10 +112,8 @@ class StreamingNormalize(object): Operation. """ with tf.name_scope(self._name + '/reset'): - return tf.group( - self._count.assign(0), - self._mean.assign(tf.zeros_like(self._mean)), - self._var_sum.assign(tf.zeros_like(self._var_sum))) + return tf.group(self._count.assign(0), self._mean.assign(tf.zeros_like(self._mean)), + self._var_sum.assign(tf.zeros_like(self._var_sum))) def summary(self): """Summary string of mean and standard deviation. @@ -128,10 +122,8 @@ class StreamingNormalize(object): Summary tensor. """ with tf.name_scope(self._name + '/summary'): - mean_summary = tf.cond( - self._count > 0, lambda: self._summary('mean', self._mean), str) - std_summary = tf.cond( - self._count > 1, lambda: self._summary('stddev', self._std()), str) + mean_summary = tf.cond(self._count > 0, lambda: self._summary('mean', self._mean), str) + std_summary = tf.cond(self._count > 1, lambda: self._summary('stddev', self._std()), str) return tf.summary.merge([mean_summary, std_summary]) def _std(self): @@ -143,10 +135,8 @@ class StreamingNormalize(object): Returns: Tensor of current variance. """ - variance = tf.cond( - self._count > 1, - lambda: self._var_sum / tf.cast(self._count - 1, tf.float32), - lambda: tf.ones_like(self._var_sum) * float('nan')) + variance = tf.cond(self._count > 1, lambda: self._var_sum / tf.cast( + self._count - 1, tf.float32), lambda: tf.ones_like(self._var_sum) * float('nan')) # The epsilon corrects for small negative variance values caused by # the algorithm. It was empirically chosen to work with all environments # tested. diff --git a/examples/pybullet/gym/pybullet_envs/agents/ppo/utility.py b/examples/pybullet/gym/pybullet_envs/agents/ppo/utility.py index c46934c49..29d9bf9ea 100644 --- a/examples/pybullet/gym/pybullet_envs/agents/ppo/utility.py +++ b/examples/pybullet/gym/pybullet_envs/agents/ppo/utility.py @@ -11,7 +11,6 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. - """Utilities for the PPO algorithm.""" from __future__ import absolute_import @@ -37,8 +36,7 @@ def reinit_nested_vars(variables, indices=None): Operation. """ if isinstance(variables, (tuple, list)): - return tf.group(*[ - reinit_nested_vars(variable, indices) for variable in variables]) + return tf.group(*[reinit_nested_vars(variable, indices) for variable in variables]) if indices is None: return variables.assign(tf.zeros_like(variables)) else: @@ -58,9 +56,8 @@ def assign_nested_vars(variables, tensors, indices=None): Operation. """ if isinstance(variables, (tuple, list)): - return tf.group(*[ - assign_nested_vars(variable, tensor) - for variable, tensor in zip(variables, tensors)]) + return tf.group( + *[assign_nested_vars(variable, tensor) for variable, tensor in zip(variables, tensors)]) if indices is None: return variables.assign(tensors) else: @@ -71,10 +68,11 @@ def discounted_return(reward, length, discount): """Discounted Monte-Carlo returns.""" timestep = tf.range(reward.shape[1].value) mask = tf.cast(timestep[None, :] < length[:, None], tf.float32) - return_ = tf.reverse(tf.transpose(tf.scan( - lambda agg, cur: cur + discount * agg, - tf.transpose(tf.reverse(mask * reward, [1]), [1, 0]), - tf.zeros_like(reward[:, -1]), 1, False), [1, 0]), [1]) + return_ = tf.reverse( + tf.transpose( + tf.scan(lambda agg, cur: cur + discount * agg, + tf.transpose(tf.reverse(mask * reward, [1]), [1, 0]), + tf.zeros_like(reward[:, -1]), 1, False), [1, 0]), [1]) return tf.check_numerics(tf.stop_gradient(return_), 'return') @@ -85,9 +83,8 @@ def fixed_step_return(reward, value, length, discount, window): return_ = tf.zeros_like(reward) for _ in range(window): return_ += reward - reward = discount * tf.concat( - [reward[:, 1:], tf.zeros_like(reward[:, -1:])], 1) - return_ += discount ** window * tf.concat( + reward = discount * tf.concat([reward[:, 1:], tf.zeros_like(reward[:, -1:])], 1) + return_ += discount**window * tf.concat( [value[:, window:], tf.zeros_like(value[:, -window:]), 1]) return tf.check_numerics(tf.stop_gradient(mask * return_), 'return') @@ -99,10 +96,11 @@ def lambda_return(reward, value, length, discount, lambda_): sequence = mask * reward + discount * value * (1 - lambda_) discount = mask * discount * lambda_ sequence = tf.stack([sequence, discount], 2) - return_ = tf.reverse(tf.transpose(tf.scan( - lambda agg, cur: cur[0] + cur[1] * agg, - tf.transpose(tf.reverse(sequence, [1]), [1, 2, 0]), - tf.zeros_like(value[:, -1]), 1, False), [1, 0]), [1]) + return_ = tf.reverse( + tf.transpose( + tf.scan(lambda agg, cur: cur[0] + cur[1] * agg, + tf.transpose(tf.reverse(sequence, [1]), [1, 2, 0]), tf.zeros_like(value[:, -1]), + 1, False), [1, 0]), [1]) return tf.check_numerics(tf.stop_gradient(return_), 'return') @@ -112,27 +110,26 @@ def lambda_advantage(reward, value, length, discount): mask = tf.cast(timestep[None, :] < length[:, None], tf.float32) next_value = tf.concat([value[:, 1:], tf.zeros_like(value[:, -1:])], 1) delta = reward + discount * next_value - value - advantage = tf.reverse(tf.transpose(tf.scan( - lambda agg, cur: cur + discount * agg, - tf.transpose(tf.reverse(mask * delta, [1]), [1, 0]), - tf.zeros_like(delta[:, -1]), 1, False), [1, 0]), [1]) + advantage = tf.reverse( + tf.transpose( + tf.scan(lambda agg, cur: cur + discount * agg, + tf.transpose(tf.reverse(mask * delta, [1]), [1, 0]), tf.zeros_like(delta[:, -1]), + 1, False), [1, 0]), [1]) return tf.check_numerics(tf.stop_gradient(advantage), 'advantage') def diag_normal_kl(mean0, logstd0, mean1, logstd1): """Epirical KL divergence of two normals with diagonal covariance.""" logstd0_2, logstd1_2 = 2 * logstd0, 2 * logstd1 - return 0.5 * ( - tf.reduce_sum(tf.exp(logstd0_2 - logstd1_2), -1) + - tf.reduce_sum((mean1 - mean0) ** 2 / tf.exp(logstd1_2), -1) + - tf.reduce_sum(logstd1_2, -1) - tf.reduce_sum(logstd0_2, -1) - - mean0.shape[-1].value) + return 0.5 * (tf.reduce_sum(tf.exp(logstd0_2 - logstd1_2), -1) + tf.reduce_sum( + (mean1 - mean0)**2 / tf.exp(logstd1_2), -1) + tf.reduce_sum(logstd1_2, -1) - + tf.reduce_sum(logstd0_2, -1) - mean0.shape[-1].value) def diag_normal_logpdf(mean, logstd, loc): """Log density of a normal with diagonal covariance.""" constant = -0.5 * math.log(2 * math.pi) - logstd - value = -0.5 * ((loc - mean) / tf.exp(logstd)) ** 2 + value = -0.5 * ((loc - mean) / tf.exp(logstd))**2 return tf.reduce_sum(constant + value, -1) diff --git a/examples/pybullet/gym/pybullet_envs/agents/tools/__init__.py b/examples/pybullet/gym/pybullet_envs/agents/tools/__init__.py index 0201de98f..3baf78ae0 100644 --- a/examples/pybullet/gym/pybullet_envs/agents/tools/__init__.py +++ b/examples/pybullet/gym/pybullet_envs/agents/tools/__init__.py @@ -11,7 +11,6 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. - """Tools for reinforcement learning.""" from __future__ import absolute_import diff --git a/examples/pybullet/gym/pybullet_envs/agents/tools/attr_dict.py b/examples/pybullet/gym/pybullet_envs/agents/tools/attr_dict.py index 1707486d8..fbb08290f 100644 --- a/examples/pybullet/gym/pybullet_envs/agents/tools/attr_dict.py +++ b/examples/pybullet/gym/pybullet_envs/agents/tools/attr_dict.py @@ -11,7 +11,6 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. - """Wrap a dictionary to access keys as attributes.""" from __future__ import absolute_import diff --git a/examples/pybullet/gym/pybullet_envs/agents/tools/batch_env.py b/examples/pybullet/gym/pybullet_envs/agents/tools/batch_env.py index 946fd7211..ef8b7430e 100644 --- a/examples/pybullet/gym/pybullet_envs/agents/tools/batch_env.py +++ b/examples/pybullet/gym/pybullet_envs/agents/tools/batch_env.py @@ -11,7 +11,6 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. - """Combine multiple environments to step them in batch.""" from __future__ import absolute_import @@ -83,13 +82,9 @@ class BatchEnv(object): message = 'Invalid action at index {}: {}' raise ValueError(message.format(index, action)) if self._blocking: - transitions = [ - env.step(action) - for env, action in zip(self._envs, actions)] + transitions = [env.step(action) for env, action in zip(self._envs, actions)] else: - transitions = [ - env.step(action, blocking=False) - for env, action in zip(self._envs, actions)] + transitions = [env.step(action, blocking=False) for env, action in zip(self._envs, actions)] transitions = [transition() for transition in transitions] observs, rewards, dones, infos = zip(*transitions) observ = np.stack(observs) diff --git a/examples/pybullet/gym/pybullet_envs/agents/tools/count_weights.py b/examples/pybullet/gym/pybullet_envs/agents/tools/count_weights.py index dd0d870f6..279df82a0 100644 --- a/examples/pybullet/gym/pybullet_envs/agents/tools/count_weights.py +++ b/examples/pybullet/gym/pybullet_envs/agents/tools/count_weights.py @@ -11,7 +11,6 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. - """Count learnable parameters.""" from __future__ import absolute_import diff --git a/examples/pybullet/gym/pybullet_envs/agents/tools/in_graph_batch_env.py b/examples/pybullet/gym/pybullet_envs/agents/tools/in_graph_batch_env.py index d4e1644d3..a4709f0a5 100644 --- a/examples/pybullet/gym/pybullet_envs/agents/tools/in_graph_batch_env.py +++ b/examples/pybullet/gym/pybullet_envs/agents/tools/in_graph_batch_env.py @@ -11,7 +11,6 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. - """Batch of environments inside the TensorFlow graph.""" from __future__ import absolute_import @@ -42,18 +41,18 @@ class InGraphBatchEnv(object): action_shape = self._parse_shape(self._batch_env.action_space) action_dtype = self._parse_dtype(self._batch_env.action_space) with tf.variable_scope('env_temporary'): - self._observ = tf.Variable( - tf.zeros((len(self._batch_env),) + observ_shape, observ_dtype), - name='observ', trainable=False) - self._action = tf.Variable( - tf.zeros((len(self._batch_env),) + action_shape, action_dtype), - name='action', trainable=False) - self._reward = tf.Variable( - tf.zeros((len(self._batch_env),), tf.float32), - name='reward', trainable=False) - self._done = tf.Variable( - tf.cast(tf.ones((len(self._batch_env),)), tf.bool), - name='done', trainable=False) + self._observ = tf.Variable(tf.zeros((len(self._batch_env),) + observ_shape, observ_dtype), + name='observ', + trainable=False) + self._action = tf.Variable(tf.zeros((len(self._batch_env),) + action_shape, action_dtype), + name='action', + trainable=False) + self._reward = tf.Variable(tf.zeros((len(self._batch_env),), tf.float32), + name='reward', + trainable=False) + self._done = tf.Variable(tf.cast(tf.ones((len(self._batch_env),)), tf.bool), + name='done', + trainable=False) def __getattr__(self, name): """Forward unimplemented attributes to one of the original environments. @@ -89,16 +88,13 @@ class InGraphBatchEnv(object): if action.dtype in (tf.float16, tf.float32, tf.float64): action = tf.check_numerics(action, 'action') observ_dtype = self._parse_dtype(self._batch_env.observation_space) - observ, reward, done = tf.py_func( - lambda a: self._batch_env.step(a)[:3], [action], - [observ_dtype, tf.float32, tf.bool], name='step') + observ, reward, done = tf.py_func(lambda a: self._batch_env.step(a)[:3], [action], + [observ_dtype, tf.float32, tf.bool], + name='step') observ = tf.check_numerics(observ, 'observ') reward = tf.check_numerics(reward, 'reward') - return tf.group( - self._observ.assign(observ), - self._action.assign(action), - self._reward.assign(reward), - self._done.assign(done)) + return tf.group(self._observ.assign(observ), self._action.assign(action), + self._reward.assign(reward), self._done.assign(done)) def reset(self, indices=None): """Reset the batch of environments. @@ -112,15 +108,15 @@ class InGraphBatchEnv(object): if indices is None: indices = tf.range(len(self._batch_env)) observ_dtype = self._parse_dtype(self._batch_env.observation_space) - observ = tf.py_func( - self._batch_env.reset, [indices], observ_dtype, name='reset') + observ = tf.py_func(self._batch_env.reset, [indices], observ_dtype, name='reset') observ = tf.check_numerics(observ, 'observ') reward = tf.zeros_like(indices, tf.float32) done = tf.zeros_like(indices, tf.bool) with tf.control_dependencies([ tf.scatter_update(self._observ, indices, observ), tf.scatter_update(self._reward, indices, reward), - tf.scatter_update(self._done, indices, done)]): + tf.scatter_update(self._done, indices, done) + ]): return tf.identity(observ) @property diff --git a/examples/pybullet/gym/pybullet_envs/agents/tools/in_graph_env.py b/examples/pybullet/gym/pybullet_envs/agents/tools/in_graph_env.py index 33ff31d07..6a71f4516 100644 --- a/examples/pybullet/gym/pybullet_envs/agents/tools/in_graph_env.py +++ b/examples/pybullet/gym/pybullet_envs/agents/tools/in_graph_env.py @@ -11,7 +11,6 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. - """Put an OpenAI Gym environment into the TensorFlow graph.""" from __future__ import absolute_import @@ -42,16 +41,15 @@ class InGraphEnv(object): action_shape = self._parse_shape(self._env.action_space) action_dtype = self._parse_dtype(self._env.action_space) with tf.name_scope('environment'): - self._observ = tf.Variable( - tf.zeros(observ_shape, observ_dtype), name='observ', trainable=False) - self._action = tf.Variable( - tf.zeros(action_shape, action_dtype), name='action', trainable=False) - self._reward = tf.Variable( - 0.0, dtype=tf.float32, name='reward', trainable=False) - self._done = tf.Variable( - True, dtype=tf.bool, name='done', trainable=False) - self._step = tf.Variable( - 0, dtype=tf.int32, name='step', trainable=False) + self._observ = tf.Variable(tf.zeros(observ_shape, observ_dtype), + name='observ', + trainable=False) + self._action = tf.Variable(tf.zeros(action_shape, action_dtype), + name='action', + trainable=False) + self._reward = tf.Variable(0.0, dtype=tf.float32, name='reward', trainable=False) + self._done = tf.Variable(True, dtype=tf.bool, name='done', trainable=False) + self._step = tf.Variable(0, dtype=tf.int32, name='step', trainable=False) def __getattr__(self, name): """Forward unimplemented attributes to the original environment. @@ -79,17 +77,14 @@ class InGraphEnv(object): if action.dtype in (tf.float16, tf.float32, tf.float64): action = tf.check_numerics(action, 'action') observ_dtype = self._parse_dtype(self._env.observation_space) - observ, reward, done = tf.py_func( - lambda a: self._env.step(a)[:3], [action], - [observ_dtype, tf.float32, tf.bool], name='step') + observ, reward, done = tf.py_func(lambda a: self._env.step(a)[:3], [action], + [observ_dtype, tf.float32, tf.bool], + name='step') observ = tf.check_numerics(observ, 'observ') reward = tf.check_numerics(reward, 'reward') - return tf.group( - self._observ.assign(observ), - self._action.assign(action), - self._reward.assign(reward), - self._done.assign(done), - self._step.assign_add(1)) + return tf.group(self._observ.assign(observ), self._action.assign(action), + self._reward.assign(reward), self._done.assign(done), + self._step.assign_add(1)) def reset(self): """Reset the environment. @@ -100,10 +95,10 @@ class InGraphEnv(object): observ_dtype = self._parse_dtype(self._env.observation_space) observ = tf.py_func(self._env.reset, [], observ_dtype, name='reset') observ = tf.check_numerics(observ, 'observ') - with tf.control_dependencies([ - self._observ.assign(observ), - self._reward.assign(0), - self._done.assign(False)]): + with tf.control_dependencies( + [self._observ.assign(observ), + self._reward.assign(0), + self._done.assign(False)]): return tf.identity(observ) @property diff --git a/examples/pybullet/gym/pybullet_envs/agents/tools/loop.py b/examples/pybullet/gym/pybullet_envs/agents/tools/loop.py index b8f118c85..b70798c18 100644 --- a/examples/pybullet/gym/pybullet_envs/agents/tools/loop.py +++ b/examples/pybullet/gym/pybullet_envs/agents/tools/loop.py @@ -11,7 +11,6 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. - """Execute operations in a loop and coordinate logging and checkpoints.""" from __future__ import absolute_import @@ -25,10 +24,8 @@ import tensorflow as tf from . import streaming_mean - _Phase = collections.namedtuple( - 'Phase', - 'name, writer, op, batch, steps, feed, report_every, log_every,' + 'Phase', 'name, writer, op, batch, steps, feed, report_every, log_every,' 'checkpoint_every') @@ -56,16 +53,22 @@ class Loop(object): reset: Tensor indicating to the model to start a new computation. """ self._logdir = logdir - self._step = ( - tf.Variable(0, False, name='global_step') if step is None else step) + self._step = (tf.Variable(0, False, name='global_step') if step is None else step) self._log = tf.placeholder(tf.bool) if log is None else log self._report = tf.placeholder(tf.bool) if report is None else report self._reset = tf.placeholder(tf.bool) if reset is None else reset self._phases = [] - def add_phase( - self, name, done, score, summary, steps, - report_every=None, log_every=None, checkpoint_every=None, feed=None): + def add_phase(self, + name, + done, + score, + summary, + steps, + report_every=None, + log_every=None, + checkpoint_every=None, + feed=None): """Add a phase to the loop protocol. If the model breaks long computation into multiple steps, the done tensor @@ -97,13 +100,12 @@ class Loop(object): if done.shape.ndims is None or score.shape.ndims is None: raise ValueError("Rank of 'done' and 'score' tensors must be known.") writer = self._logdir and tf.summary.FileWriter( - os.path.join(self._logdir, name), tf.get_default_graph(), - flush_secs=60) + os.path.join(self._logdir, name), tf.get_default_graph(), flush_secs=60) op = self._define_step(done, score, summary) batch = 1 if score.shape.ndims == 0 else score.shape[0].value - self._phases.append(_Phase( - name, writer, op, batch, int(steps), feed, report_every, - log_every, checkpoint_every)) + self._phases.append( + _Phase(name, writer, op, batch, int(steps), feed, report_every, log_every, + checkpoint_every)) def run(self, sess, saver, max_step=None): """Run the loop schedule for a specified number of steps. @@ -133,13 +135,11 @@ class Loop(object): tf.logging.info(message.format(phase.name, phase_step, global_step)) # Populate book keeping tensors. phase.feed[self._reset] = (steps_in < steps_made) - phase.feed[self._log] = ( - phase.writer and - self._is_every_steps(phase_step, phase.batch, phase.log_every)) - phase.feed[self._report] = ( - self._is_every_steps(phase_step, phase.batch, phase.report_every)) - summary, mean_score, global_step, steps_made = sess.run( - phase.op, phase.feed) + phase.feed[self._log] = (phase.writer and + self._is_every_steps(phase_step, phase.batch, phase.log_every)) + phase.feed[self._report] = (self._is_every_steps(phase_step, phase.batch, + phase.report_every)) + summary, mean_score, global_step, steps_made = sess.run(phase.op, phase.feed) if self._is_every_steps(phase_step, phase.batch, phase.checkpoint_every): self._store_checkpoint(sess, saver, global_step) if self._is_every_steps(phase_step, phase.batch, phase.report_every): @@ -207,8 +207,7 @@ class Loop(object): score_mean = streaming_mean.StreamingMean((), tf.float32) with tf.control_dependencies([done, score, summary]): done_score = tf.gather(score, tf.where(done)[:, 0]) - submit_score = tf.cond( - tf.reduce_any(done), lambda: score_mean.submit(done_score), tf.no_op) + submit_score = tf.cond(tf.reduce_any(done), lambda: score_mean.submit(done_score), tf.no_op) with tf.control_dependencies([submit_score]): mean_score = tf.cond(self._report, score_mean.clear, float) steps_made = tf.shape(score)[0] diff --git a/examples/pybullet/gym/pybullet_envs/agents/tools/mock_algorithm.py b/examples/pybullet/gym/pybullet_envs/agents/tools/mock_algorithm.py index c60712d8b..c9c55ac60 100644 --- a/examples/pybullet/gym/pybullet_envs/agents/tools/mock_algorithm.py +++ b/examples/pybullet/gym/pybullet_envs/agents/tools/mock_algorithm.py @@ -11,7 +11,6 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. - """Mock algorithm for testing reinforcement learning code.""" from __future__ import absolute_import diff --git a/examples/pybullet/gym/pybullet_envs/agents/tools/mock_environment.py b/examples/pybullet/gym/pybullet_envs/agents/tools/mock_environment.py index 248f515b1..9db14d56c 100644 --- a/examples/pybullet/gym/pybullet_envs/agents/tools/mock_environment.py +++ b/examples/pybullet/gym/pybullet_envs/agents/tools/mock_environment.py @@ -11,7 +11,6 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. - """Mock environment for testing reinforcement learning code.""" from __future__ import absolute_import diff --git a/examples/pybullet/gym/pybullet_envs/agents/tools/simulate.py b/examples/pybullet/gym/pybullet_envs/agents/tools/simulate.py index ebe9a898b..128b83f00 100644 --- a/examples/pybullet/gym/pybullet_envs/agents/tools/simulate.py +++ b/examples/pybullet/gym/pybullet_envs/agents/tools/simulate.py @@ -11,7 +11,6 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. - """In-graph simulation step of a vectorized algorithm with environments.""" from __future__ import absolute_import @@ -55,7 +54,8 @@ def simulate(batch_env, algo, log=True, reset=False): reset_ops = [ batch_env.reset(agent_indices), tf.scatter_update(score, agent_indices, zero_scores), - tf.scatter_update(length, agent_indices, zero_durations)] + tf.scatter_update(length, agent_indices, zero_durations) + ] with tf.control_dependencies(reset_ops): return algo.begin_episode(agent_indices) @@ -78,9 +78,8 @@ def simulate(batch_env, algo, log=True, reset=False): inc_length = length.assign_add(tf.ones(len(batch_env), tf.int32)) with tf.control_dependencies([add_score, inc_length]): agent_indices = tf.range(len(batch_env)) - experience_summary = algo.experience( - agent_indices, prevob, batch_env.action, batch_env.reward, - batch_env.done, batch_env.observ) + experience_summary = algo.experience(agent_indices, prevob, batch_env.action, + batch_env.reward, batch_env.done, batch_env.observ) return tf.summary.merge([step_summary, experience_summary]) def _define_end_episode(agent_indices): @@ -96,8 +95,7 @@ def simulate(batch_env, algo, log=True, reset=False): """ assert agent_indices.shape.ndims == 1 submit_score = mean_score.submit(tf.gather(score, agent_indices)) - submit_length = mean_length.submit( - tf.cast(tf.gather(length, agent_indices), tf.float32)) + submit_length = mean_length.submit(tf.cast(tf.gather(length, agent_indices), tf.float32)) with tf.control_dependencies([submit_score, submit_length]): return algo.end_episode(agent_indices) @@ -107,41 +105,34 @@ def simulate(batch_env, algo, log=True, reset=False): Returns: Summary string. """ - score_summary = tf.cond( - tf.logical_and(log, tf.cast(mean_score.count, tf.bool)), - lambda: tf.summary.scalar('mean_score', mean_score.clear()), str) - length_summary = tf.cond( - tf.logical_and(log, tf.cast(mean_length.count, tf.bool)), - lambda: tf.summary.scalar('mean_length', mean_length.clear()), str) + score_summary = tf.cond(tf.logical_and(log, tf.cast( + mean_score.count, tf.bool)), lambda: tf.summary.scalar('mean_score', mean_score.clear()), + str) + length_summary = tf.cond(tf.logical_and( + log, tf.cast(mean_length.count, + tf.bool)), lambda: tf.summary.scalar('mean_length', mean_length.clear()), str) return tf.summary.merge([score_summary, length_summary]) with tf.name_scope('simulate'): log = tf.convert_to_tensor(log) reset = tf.convert_to_tensor(reset) with tf.variable_scope('simulate_temporary'): - score = tf.Variable( - tf.zeros(len(batch_env), dtype=tf.float32), False, name='score') - length = tf.Variable( - tf.zeros(len(batch_env), dtype=tf.int32), False, name='length') + score = tf.Variable(tf.zeros(len(batch_env), dtype=tf.float32), False, name='score') + length = tf.Variable(tf.zeros(len(batch_env), dtype=tf.int32), False, name='length') mean_score = streaming_mean.StreamingMean((), tf.float32) mean_length = streaming_mean.StreamingMean((), tf.float32) - agent_indices = tf.cond( - reset, - lambda: tf.range(len(batch_env)), - lambda: tf.cast(tf.where(batch_env.done)[:, 0], tf.int32)) - begin_episode = tf.cond( - tf.cast(tf.shape(agent_indices)[0], tf.bool), - lambda: _define_begin_episode(agent_indices), str) + agent_indices = tf.cond(reset, lambda: tf.range(len(batch_env)), lambda: tf.cast( + tf.where(batch_env.done)[:, 0], tf.int32)) + begin_episode = tf.cond(tf.cast(tf.shape(agent_indices)[0], + tf.bool), lambda: _define_begin_episode(agent_indices), str) with tf.control_dependencies([begin_episode]): step = _define_step() with tf.control_dependencies([step]): agent_indices = tf.cast(tf.where(batch_env.done)[:, 0], tf.int32) - end_episode = tf.cond( - tf.cast(tf.shape(agent_indices)[0], tf.bool), - lambda: _define_end_episode(agent_indices), str) + end_episode = tf.cond(tf.cast(tf.shape(agent_indices)[0], + tf.bool), lambda: _define_end_episode(agent_indices), str) with tf.control_dependencies([end_episode]): - summary = tf.summary.merge([ - _define_summaries(), begin_episode, step, end_episode]) + summary = tf.summary.merge([_define_summaries(), begin_episode, step, end_episode]) with tf.control_dependencies([summary]): done, score = tf.identity(batch_env.done), tf.identity(score) return done, score, summary diff --git a/examples/pybullet/gym/pybullet_envs/agents/tools/streaming_mean.py b/examples/pybullet/gym/pybullet_envs/agents/tools/streaming_mean.py index 3f620fe37..5992eaa6b 100644 --- a/examples/pybullet/gym/pybullet_envs/agents/tools/streaming_mean.py +++ b/examples/pybullet/gym/pybullet_envs/agents/tools/streaming_mean.py @@ -11,7 +11,6 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. - """Compute a streaming estimation of the mean of submitted tensors.""" from __future__ import absolute_import @@ -53,9 +52,8 @@ class StreamingMean(object): # Add a batch dimension if necessary. if value.shape.ndims == self._sum.shape.ndims: value = value[None, ...] - return tf.group( - self._sum.assign_add(tf.reduce_sum(value, 0)), - self._count.assign_add(tf.shape(value)[0])) + return tf.group(self._sum.assign_add(tf.reduce_sum(value, 0)), + self._count.assign_add(tf.shape(value)[0])) def clear(self): """Return the mean estimate and reset the streaming statistics.""" diff --git a/examples/pybullet/gym/pybullet_envs/agents/tools/wrappers.py b/examples/pybullet/gym/pybullet_envs/agents/tools/wrappers.py index e7c7543e3..0c3ff47b6 100644 --- a/examples/pybullet/gym/pybullet_envs/agents/tools/wrappers.py +++ b/examples/pybullet/gym/pybullet_envs/agents/tools/wrappers.py @@ -11,7 +11,6 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. - """Wrappers for OpenAI Gym environments.""" from __future__ import absolute_import @@ -149,8 +148,7 @@ class FrameHistory(object): return self._select_frames() def _select_frames(self): - indices = [ - (self._step - index) % self._capacity for index in self._past_indices] + indices = [(self._step - index) % self._capacity for index in self._past_indices] observ = self._buffer[indices] if self._flatten: observ = np.reshape(observ, (-1,) + observ.shape[2:]) @@ -191,14 +189,14 @@ class RangeNormalize(object): def __init__(self, env, observ=None, action=None): self._env = env - self._should_normalize_observ = ( - observ is not False and self._is_finite(self._env.observation_space)) + self._should_normalize_observ = (observ is not False and + self._is_finite(self._env.observation_space)) if observ is True and not self._should_normalize_observ: raise ValueError('Cannot normalize infinite observation range.') if observ is None and not self._should_normalize_observ: tf.logging.info('Not normalizing infinite observation range.') - self._should_normalize_action = ( - action is not False and self._is_finite(self._env.action_space)) + self._should_normalize_action = (action is not False and + self._is_finite(self._env.action_space)) if action is True and not self._should_normalize_action: raise ValueError('Cannot normalize infinite action range.') if action is None and not self._should_normalize_action: @@ -323,8 +321,7 @@ class ExternalProcess(object): action_space: The cached action space of the environment. """ self._conn, conn = multiprocessing.Pipe() - self._process = multiprocessing.Process( - target=self._worker, args=(constructor, conn)) + self._process = multiprocessing.Process(target=self._worker, args=(constructor, conn)) atexit.register(self.close) self._process.start() self._observ_space = None diff --git a/examples/pybullet/gym/pybullet_envs/agents/train_ppo.py b/examples/pybullet/gym/pybullet_envs/agents/train_ppo.py index e2f3b4114..aa41634a2 100644 --- a/examples/pybullet/gym/pybullet_envs/agents/train_ppo.py +++ b/examples/pybullet/gym/pybullet_envs/agents/train_ppo.py @@ -11,7 +11,6 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. - r"""Script to train a batch reinforcement learning algorithm. Command line: @@ -67,21 +66,25 @@ def _define_loop(graph, logdir, train_steps, eval_steps): Returns: Loop object. """ - loop = tools.Loop( - logdir, graph.step, graph.should_log, graph.do_report, - graph.force_reset) - loop.add_phase( - 'train', graph.done, graph.score, graph.summary, train_steps, - report_every=train_steps, - log_every=train_steps // 2, - checkpoint_every=None, - feed={graph.is_training: True}) - loop.add_phase( - 'eval', graph.done, graph.score, graph.summary, eval_steps, - report_every=eval_steps, - log_every=eval_steps // 2, - checkpoint_every=10 * eval_steps, - feed={graph.is_training: False}) + loop = tools.Loop(logdir, graph.step, graph.should_log, graph.do_report, graph.force_reset) + loop.add_phase('train', + graph.done, + graph.score, + graph.summary, + train_steps, + report_every=train_steps, + log_every=train_steps // 2, + checkpoint_every=None, + feed={graph.is_training: True}) + loop.add_phase('eval', + graph.done, + graph.score, + graph.summary, + eval_steps, + report_every=eval_steps, + log_every=eval_steps // 2, + checkpoint_every=10 * eval_steps, + feed={graph.is_training: False}) return loop @@ -102,18 +105,13 @@ def train(config, env_processes): if config.update_every % config.num_agents: tf.logging.warn('Number of agents should divide episodes per update.') with tf.device('/cpu:0'): - batch_env = utility.define_batch_env( - lambda: _create_environment(config), - config.num_agents, env_processes) - graph = utility.define_simulation_graph( - batch_env, config.algorithm, config) - loop = _define_loop( - graph, config.logdir, - config.update_every * config.max_length, - config.eval_episodes * config.max_length) - total_steps = int( - config.steps / config.update_every * - (config.update_every + config.eval_episodes)) + batch_env = utility.define_batch_env(lambda: _create_environment(config), config.num_agents, + env_processes) + graph = utility.define_simulation_graph(batch_env, config.algorithm, config) + loop = _define_loop(graph, config.logdir, config.update_every * config.max_length, + config.eval_episodes * config.max_length) + total_steps = int(config.steps / config.update_every * + (config.update_every + config.eval_episodes)) # Exclude episode related variables since the Python state of environments is # not checkpointed and thus new episodes start after resuming. saver = utility.define_saver(exclude=(r'.*_temporary/.*',)) @@ -131,8 +129,8 @@ def main(_): utility.set_up_logging() if not FLAGS.config: raise KeyError('You must specify a configuration.') - logdir = FLAGS.logdir and os.path.expanduser(os.path.join( - FLAGS.logdir, '{}-{}'.format(FLAGS.timestamp, FLAGS.config))) + logdir = FLAGS.logdir and os.path.expanduser( + os.path.join(FLAGS.logdir, '{}-{}'.format(FLAGS.timestamp, FLAGS.config))) try: config = utility.load_config(logdir) except IOError: @@ -144,16 +142,11 @@ def main(_): if __name__ == '__main__': FLAGS = tf.app.flags.FLAGS - tf.app.flags.DEFINE_string( - 'logdir', None, - 'Base directory to store logs.') - tf.app.flags.DEFINE_string( - 'timestamp', datetime.datetime.now().strftime('%Y%m%dT%H%M%S'), - 'Sub directory to store logs.') - tf.app.flags.DEFINE_string( - 'config', None, - 'Configuration to execute.') - tf.app.flags.DEFINE_boolean( - 'env_processes', True, - 'Step environments in separate processes to circumvent the GIL.') + tf.app.flags.DEFINE_string('logdir', None, 'Base directory to store logs.') + tf.app.flags.DEFINE_string('timestamp', + datetime.datetime.now().strftime('%Y%m%dT%H%M%S'), + 'Sub directory to store logs.') + tf.app.flags.DEFINE_string('config', None, 'Configuration to execute.') + tf.app.flags.DEFINE_boolean('env_processes', True, + 'Step environments in separate processes to circumvent the GIL.') tf.app.run() diff --git a/examples/pybullet/gym/pybullet_envs/agents/utility.py b/examples/pybullet/gym/pybullet_envs/agents/utility.py index 0836a2ed2..1ccebbd10 100644 --- a/examples/pybullet/gym/pybullet_envs/agents/utility.py +++ b/examples/pybullet/gym/pybullet_envs/agents/utility.py @@ -11,7 +11,6 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. - """Utilities for using reinforcement learning algorithms.""" from __future__ import absolute_import @@ -46,8 +45,7 @@ def define_simulation_graph(batch_env, algo_cls, config): do_report = tf.placeholder(tf.bool, name='do_report') force_reset = tf.placeholder(tf.bool, name='force_reset') algo = algo_cls(batch_env, step, is_training, should_log, config) - done, score, summary = tools.simulate( - batch_env, algo, should_log, force_reset) + done, score, summary = tools.simulate(batch_env, algo, should_log, force_reset) message = 'Graph contains {} trainable variables.' tf.logging.info(message.format(tools.count_weights())) # pylint: enable=unused-variable @@ -67,9 +65,7 @@ def define_batch_env(constructor, num_agents, env_processes): """ with tf.variable_scope('environments'): if env_processes: - envs = [ - tools.wrappers.ExternalProcess(constructor) - for _ in range(num_agents)] + envs = [tools.wrappers.ExternalProcess(constructor) for _ in range(num_agents)] else: envs = [constructor() for _ in range(num_agents)] batch_env = tools.BatchEnv(envs, blocking=not env_processes) @@ -111,9 +107,7 @@ def initialize_variables(sess, saver, logdir, checkpoint=None, resume=None): ValueError: If resume expected but no log directory specified. RuntimeError: If no resume expected but a checkpoint was found. """ - sess.run(tf.group( - tf.local_variables_initializer(), - tf.global_variables_initializer())) + sess.run(tf.group(tf.local_variables_initializer(), tf.global_variables_initializer())) if resume and not (logdir or checkpoint): raise ValueError('Need to specify logdir to resume a checkpoint.') if logdir: @@ -152,9 +146,8 @@ def save_config(config, logdir=None): with tf.gfile.GFile(config_path, 'w') as file_: yaml.dump(config, file_, default_flow_style=False) else: - message = ( - 'Start a new run without storing summaries and checkpoints since no ' - 'logging directory was specified.') + message = ('Start a new run without storing summaries and checkpoints since no ' + 'logging directory was specified.') tf.logging.info(message) return config @@ -173,9 +166,8 @@ def load_config(logdir): """ config_path = logdir and os.path.join(logdir, 'config.yaml') if not config_path or not tf.gfile.Exists(config_path): - message = ( - 'Cannot resume an existing run since the logging directory does not ' - 'contain a configuration file.') + message = ('Cannot resume an existing run since the logging directory does not ' + 'contain a configuration file.') raise IOError(message) with tf.gfile.FastGFile(config_path, 'r') as file_: config = yaml.load(file_, Loader=yaml.Loader) diff --git a/examples/pybullet/gym/pybullet_envs/agents/visualize_ppo.py b/examples/pybullet/gym/pybullet_envs/agents/visualize_ppo.py index dd27a546d..8524b6311 100644 --- a/examples/pybullet/gym/pybullet_envs/agents/visualize_ppo.py +++ b/examples/pybullet/gym/pybullet_envs/agents/visualize_ppo.py @@ -11,7 +11,6 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. - r"""Script to render videos of the Proximal Policy Gradient algorithm. Command line: @@ -53,8 +52,7 @@ def _create_environment(config, outdir): setattr(env, 'spec', getattr(env, 'spec', None)) if config.max_length: env = tools.wrappers.LimitDuration(env, config.max_length) - env = gym.wrappers.Monitor( - env, outdir, lambda unused_episode_number: True) + env = gym.wrappers.Monitor(env, outdir, lambda unused_episode_number: True) env = tools.wrappers.RangeNormalize(env) env = tools.wrappers.ClipAction(env) env = tools.wrappers.ConvertTo32Bit(env) @@ -71,20 +69,20 @@ def _define_loop(graph, eval_steps): Returns: Loop object. """ - loop = tools.Loop( - None, graph.step, graph.should_log, graph.do_report, graph.force_reset) - loop.add_phase( - 'eval', graph.done, graph.score, graph.summary, eval_steps, - report_every=eval_steps, - log_every=None, - checkpoint_every=None, - feed={graph.is_training: False}) + loop = tools.Loop(None, graph.step, graph.should_log, graph.do_report, graph.force_reset) + loop.add_phase('eval', + graph.done, + graph.score, + graph.summary, + eval_steps, + report_every=eval_steps, + log_every=None, + checkpoint_every=None, + feed={graph.is_training: False}) return loop -def visualize( - logdir, outdir, num_agents, num_episodes, checkpoint=None, - env_processes=True): +def visualize(logdir, outdir, num_agents, num_episodes, checkpoint=None, env_processes=True): """Recover checkpoint and render videos from it. Args: @@ -97,20 +95,16 @@ def visualize( """ config = utility.load_config(logdir) with tf.device('/cpu:0'): - batch_env = utility.define_batch_env( - lambda: _create_environment(config, outdir), - num_agents, env_processes) - graph = utility.define_simulation_graph( - batch_env, config.algorithm, config) + batch_env = utility.define_batch_env(lambda: _create_environment(config, outdir), num_agents, + env_processes) + graph = utility.define_simulation_graph(batch_env, config.algorithm, config) total_steps = num_episodes * config.max_length loop = _define_loop(graph, total_steps) - saver = utility.define_saver( - exclude=(r'.*_temporary/.*', r'global_step')) + saver = utility.define_saver(exclude=(r'.*_temporary/.*', r'global_step')) sess_config = tf.ConfigProto(allow_soft_placement=True) sess_config.gpu_options.allow_growth = True with tf.Session(config=sess_config) as sess: - utility.initialize_variables( - sess, saver, config.logdir, checkpoint, resume=True) + utility.initialize_variables(sess, saver, config.logdir, checkpoint, resume=True) for unused_score in loop.run(sess, saver, total_steps): pass batch_env.close() @@ -123,29 +117,18 @@ def main(_): raise KeyError('You must specify logging and outdirs directories.') FLAGS.logdir = os.path.expanduser(FLAGS.logdir) FLAGS.outdir = os.path.expanduser(FLAGS.outdir) - visualize( - FLAGS.logdir, FLAGS.outdir, FLAGS.num_agents, FLAGS.num_episodes, - FLAGS.checkpoint, FLAGS.env_processes) + visualize(FLAGS.logdir, FLAGS.outdir, FLAGS.num_agents, FLAGS.num_episodes, FLAGS.checkpoint, + FLAGS.env_processes) if __name__ == '__main__': FLAGS = tf.app.flags.FLAGS - tf.app.flags.DEFINE_string( - 'logdir', None, - 'Directory to the checkpoint of a training run.') - tf.app.flags.DEFINE_string( - 'outdir', None, - 'Local directory for storing the monitoring outdir.') - tf.app.flags.DEFINE_string( - 'checkpoint', None, - 'Checkpoint name to load; defaults to most recent.') - tf.app.flags.DEFINE_integer( - 'num_agents', 1, - 'How many environments to step in parallel.') - tf.app.flags.DEFINE_integer( - 'num_episodes', 5, - 'Minimum number of episodes to render.') - tf.app.flags.DEFINE_boolean( - 'env_processes', True, - 'Step environments in separate processes to circumvent the GIL.') + tf.app.flags.DEFINE_string('logdir', None, 'Directory to the checkpoint of a training run.') + tf.app.flags.DEFINE_string('outdir', None, 'Local directory for storing the monitoring outdir.') + tf.app.flags.DEFINE_string('checkpoint', None, + 'Checkpoint name to load; defaults to most recent.') + tf.app.flags.DEFINE_integer('num_agents', 1, 'How many environments to step in parallel.') + tf.app.flags.DEFINE_integer('num_episodes', 5, 'Minimum number of episodes to render.') + tf.app.flags.DEFINE_boolean('env_processes', True, + 'Step environments in separate processes to circumvent the GIL.') tf.app.run() diff --git a/examples/pybullet/gym/pybullet_envs/baselines/__init__.py b/examples/pybullet/gym/pybullet_envs/baselines/__init__.py index b67603479..e7d027b19 100644 --- a/examples/pybullet/gym/pybullet_envs/baselines/__init__.py +++ b/examples/pybullet/gym/pybullet_envs/baselines/__init__.py @@ -7,4 +7,3 @@ from . import train_kuka_grasping from . import train_pybullet_cartpole from . import train_pybullet_racecar from . import train_pybullet_zed_racecar - diff --git a/examples/pybullet/gym/pybullet_envs/baselines/enjoy_kuka_diverse_object_grasping.py b/examples/pybullet/gym/pybullet_envs/baselines/enjoy_kuka_diverse_object_grasping.py index da40d6e94..509e33d1f 100644 --- a/examples/pybullet/gym/pybullet_envs/baselines/enjoy_kuka_diverse_object_grasping.py +++ b/examples/pybullet/gym/pybullet_envs/baselines/enjoy_kuka_diverse_object_grasping.py @@ -6,17 +6,17 @@ import numpy as np currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe()))) parentdir = os.path.dirname(os.path.dirname(currentdir)) -os.sys.path.insert(0,parentdir) +os.sys.path.insert(0, parentdir) import gym from pybullet_envs.bullet.kuka_diverse_object_gym_env import KukaDiverseObjectEnv from gym import spaces - class ContinuousDownwardBiasPolicy(object): """Policy which takes continuous actions, and is biased to move down. """ + def __init__(self, height_hack_prob=0.9): """Initializes the DownwardBiasPolicy. @@ -36,25 +36,25 @@ class ContinuousDownwardBiasPolicy(object): def main(): - - env = KukaDiverseObjectEnv(renders=True, isDiscrete=False) - policy = ContinuousDownwardBiasPolicy() - - while True: - obs, done = env.reset(), False - print("===================================") - print("obs") - print(obs) - episode_rew = 0 - while not done: - env.render(mode='human') - act = policy.sample_action(obs, .1) - print("Action") - print(act) - obs, rew, done, _ = env.step([0, 0, 0, 0, 0]) - episode_rew += rew - print("Episode reward", episode_rew) + + env = KukaDiverseObjectEnv(renders=True, isDiscrete=False) + policy = ContinuousDownwardBiasPolicy() + + while True: + obs, done = env.reset(), False + print("===================================") + print("obs") + print(obs) + episode_rew = 0 + while not done: + env.render(mode='human') + act = policy.sample_action(obs, .1) + print("Action") + print(act) + obs, rew, done, _ = env.step([0, 0, 0, 0, 0]) + episode_rew += rew + print("Episode reward", episode_rew) if __name__ == '__main__': - main() + main() diff --git a/examples/pybullet/gym/pybullet_envs/baselines/enjoy_kuka_grasping.py b/examples/pybullet/gym/pybullet_envs/baselines/enjoy_kuka_grasping.py index 1a5b7c2a8..422a7a621 100644 --- a/examples/pybullet/gym/pybullet_envs/baselines/enjoy_kuka_grasping.py +++ b/examples/pybullet/gym/pybullet_envs/baselines/enjoy_kuka_grasping.py @@ -2,7 +2,7 @@ import os, inspect currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe()))) parentdir = os.path.dirname(os.path.dirname(currentdir)) -os.sys.path.insert(0,parentdir) +os.sys.path.insert(0, parentdir) import gym from pybullet_envs.bullet.kukaGymEnv import KukaGymEnv @@ -11,22 +11,22 @@ from baselines import deepq def main(): - - env = KukaGymEnv(renders=True, isDiscrete=True) - act = deepq.load("kuka_model.pkl") - print(act) - while True: - obs, done = env.reset(), False - print("===================================") - print("obs") - print(obs) - episode_rew = 0 - while not done: - env.render() - obs, rew, done, _ = env.step(act(obs[None])[0]) - episode_rew += rew - print("Episode reward", episode_rew) + + env = KukaGymEnv(renders=True, isDiscrete=True) + act = deepq.load("kuka_model.pkl") + print(act) + while True: + obs, done = env.reset(), False + print("===================================") + print("obs") + print(obs) + episode_rew = 0 + while not done: + env.render() + obs, rew, done, _ = env.step(act(obs[None])[0]) + episode_rew += rew + print("Episode reward", episode_rew) if __name__ == '__main__': - main() + main() diff --git a/examples/pybullet/gym/pybullet_envs/baselines/enjoy_pybullet_cartpole.py b/examples/pybullet/gym/pybullet_envs/baselines/enjoy_pybullet_cartpole.py index 0de7dc074..341029dd4 100644 --- a/examples/pybullet/gym/pybullet_envs/baselines/enjoy_pybullet_cartpole.py +++ b/examples/pybullet/gym/pybullet_envs/baselines/enjoy_pybullet_cartpole.py @@ -2,7 +2,7 @@ import os, inspect currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe()))) parentdir = os.path.dirname(os.path.dirname(currentdir)) -os.sys.path.insert(0,parentdir) +os.sys.path.insert(0, parentdir) import gym import time @@ -10,28 +10,29 @@ import time from baselines import deepq from pybullet_envs.bullet.cartpole_bullet import CartPoleBulletEnv + def main(): - env = gym.make('CartPoleBulletEnv-v1') - act = deepq.load("cartpole_model.pkl") - - while True: - obs, done = env.reset(), False - print("obs") - print(obs) - print("type(obs)") - print(type(obs)) - episode_rew = 0 - while not done: - env.render() - - o = obs[None] - aa = act(o) - a = aa[0] - obs, rew, done, _ = env.step(a) - episode_rew += rew - time.sleep(1./240.) - print("Episode reward", episode_rew) + env = gym.make('CartPoleBulletEnv-v1') + act = deepq.load("cartpole_model.pkl") + + while True: + obs, done = env.reset(), False + print("obs") + print(obs) + print("type(obs)") + print(type(obs)) + episode_rew = 0 + while not done: + env.render() + + o = obs[None] + aa = act(o) + a = aa[0] + obs, rew, done, _ = env.step(a) + episode_rew += rew + time.sleep(1. / 240.) + print("Episode reward", episode_rew) if __name__ == '__main__': - main() + main() diff --git a/examples/pybullet/gym/pybullet_envs/baselines/enjoy_pybullet_racecar.py b/examples/pybullet/gym/pybullet_envs/baselines/enjoy_pybullet_racecar.py index ce3e74358..64936dd9a 100644 --- a/examples/pybullet/gym/pybullet_envs/baselines/enjoy_pybullet_racecar.py +++ b/examples/pybullet/gym/pybullet_envs/baselines/enjoy_pybullet_racecar.py @@ -2,7 +2,7 @@ import os, inspect currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe()))) parentdir = os.path.dirname(os.path.dirname(currentdir)) -os.sys.path.insert(0,parentdir) +os.sys.path.insert(0, parentdir) import gym from pybullet_envs.bullet.racecarGymEnv import RacecarGymEnv @@ -11,22 +11,22 @@ from baselines import deepq def main(): - - env = RacecarGymEnv(renders=True,isDiscrete=True) - act = deepq.load("racecar_model.pkl") - print(act) - while True: - obs, done = env.reset(), False - print("===================================") - print("obs") - print(obs) - episode_rew = 0 - while not done: - env.render() - obs, rew, done, _ = env.step(act(obs[None])[0]) - episode_rew += rew - print("Episode reward", episode_rew) + + env = RacecarGymEnv(renders=True, isDiscrete=True) + act = deepq.load("racecar_model.pkl") + print(act) + while True: + obs, done = env.reset(), False + print("===================================") + print("obs") + print(obs) + episode_rew = 0 + while not done: + env.render() + obs, rew, done, _ = env.step(act(obs[None])[0]) + episode_rew += rew + print("Episode reward", episode_rew) if __name__ == '__main__': - main() + main() diff --git a/examples/pybullet/gym/pybullet_envs/baselines/enjoy_pybullet_zed_racecar.py b/examples/pybullet/gym/pybullet_envs/baselines/enjoy_pybullet_zed_racecar.py index acf6cf0f3..a8bcaa987 100644 --- a/examples/pybullet/gym/pybullet_envs/baselines/enjoy_pybullet_zed_racecar.py +++ b/examples/pybullet/gym/pybullet_envs/baselines/enjoy_pybullet_zed_racecar.py @@ -2,7 +2,7 @@ import os, inspect currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe()))) parentdir = os.path.dirname(os.path.dirname(currentdir)) -os.sys.path.insert(0,parentdir) +os.sys.path.insert(0, parentdir) import gym from pybullet_envs.bullet.racecarZEDGymEnv import RacecarZEDGymEnv @@ -11,22 +11,22 @@ from baselines import deepq def main(): - - env = RacecarZEDGymEnv(renders=True) - act = deepq.load("racecar_zed_model.pkl") - print(act) - while True: - obs, done = env.reset(), False - print("===================================") - print("obs") - print(obs) - episode_rew = 0 - while not done: - env.render() - obs, rew, done, _ = env.step(act(obs[None])[0]) - episode_rew += rew - print("Episode reward", episode_rew) + + env = RacecarZEDGymEnv(renders=True) + act = deepq.load("racecar_zed_model.pkl") + print(act) + while True: + obs, done = env.reset(), False + print("===================================") + print("obs") + print(obs) + episode_rew = 0 + while not done: + env.render() + obs, rew, done, _ = env.step(act(obs[None])[0]) + episode_rew += rew + print("Episode reward", episode_rew) if __name__ == '__main__': - main() + main() diff --git a/examples/pybullet/gym/pybullet_envs/baselines/train_kuka_cam_grasping.py b/examples/pybullet/gym/pybullet_envs/baselines/train_kuka_cam_grasping.py index 4fd9f5a48..804be59f6 100644 --- a/examples/pybullet/gym/pybullet_envs/baselines/train_kuka_cam_grasping.py +++ b/examples/pybullet/gym/pybullet_envs/baselines/train_kuka_cam_grasping.py @@ -2,7 +2,7 @@ import os, inspect currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe()))) parentdir = os.path.dirname(os.path.dirname(currentdir)) -os.sys.path.insert(0,parentdir) +os.sys.path.insert(0, parentdir) import gym from pybullet_envs.bullet.kukaCamGymEnv import KukaCamGymEnv @@ -12,39 +12,34 @@ from baselines import deepq import datetime - def callback(lcl, glb): - # stop training if reward exceeds 199 - total = sum(lcl['episode_rewards'][-101:-1]) / 100 - totalt = lcl['t'] - #print("totalt") - #print(totalt) - is_solved = totalt > 2000 and total >= 10 - return is_solved + # stop training if reward exceeds 199 + total = sum(lcl['episode_rewards'][-101:-1]) / 100 + totalt = lcl['t'] + #print("totalt") + #print(totalt) + is_solved = totalt > 2000 and total >= 10 + return is_solved def main(): - - env = KukaCamGymEnv(renders=False, isDiscrete=True) - model = deepq.models.cnn_to_mlp( - convs=[(32, 8, 4), (64, 4, 2), (64, 3, 1)], - hiddens=[256], - dueling=False - ) - act = deepq.learn( - env, - q_func=model, - lr=1e-3, - max_timesteps=10000000, - buffer_size=50000, - exploration_fraction=0.1, - exploration_final_eps=0.02, - print_freq=10, - callback=callback - ) - print("Saving model to kuka_cam_model.pkl") - act.save("kuka_cam_model.pkl") + + env = KukaCamGymEnv(renders=False, isDiscrete=True) + model = deepq.models.cnn_to_mlp(convs=[(32, 8, 4), (64, 4, 2), (64, 3, 1)], + hiddens=[256], + dueling=False) + act = deepq.learn(env, + q_func=model, + lr=1e-3, + max_timesteps=10000000, + buffer_size=50000, + exploration_fraction=0.1, + exploration_final_eps=0.02, + print_freq=10, + callback=callback) + print("Saving model to kuka_cam_model.pkl") + act.save("kuka_cam_model.pkl") if __name__ == '__main__': - main() + main() diff --git a/examples/pybullet/gym/pybullet_envs/baselines/train_kuka_grasping.py b/examples/pybullet/gym/pybullet_envs/baselines/train_kuka_grasping.py index 932a08e77..10225a194 100644 --- a/examples/pybullet/gym/pybullet_envs/baselines/train_kuka_grasping.py +++ b/examples/pybullet/gym/pybullet_envs/baselines/train_kuka_grasping.py @@ -2,7 +2,7 @@ import os, inspect currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe()))) parentdir = os.path.dirname(os.path.dirname(currentdir)) -os.sys.path.insert(0,parentdir) +os.sys.path.insert(0, parentdir) import gym from pybullet_envs.bullet.kukaGymEnv import KukaGymEnv @@ -12,35 +12,32 @@ from baselines import deepq import datetime - def callback(lcl, glb): - # stop training if reward exceeds 199 - total = sum(lcl['episode_rewards'][-101:-1]) / 100 - totalt = lcl['t'] - #print("totalt") - #print(totalt) - is_solved = totalt > 2000 and total >= 10 - return is_solved + # stop training if reward exceeds 199 + total = sum(lcl['episode_rewards'][-101:-1]) / 100 + totalt = lcl['t'] + #print("totalt") + #print(totalt) + is_solved = totalt > 2000 and total >= 10 + return is_solved def main(): - - env = KukaGymEnv(renders=False, isDiscrete=True) - model = deepq.models.mlp([64]) - act = deepq.learn( - env, - q_func=model, - lr=1e-3, - max_timesteps=10000000, - buffer_size=50000, - exploration_fraction=0.1, - exploration_final_eps=0.02, - print_freq=10, - callback=callback - ) - print("Saving model to kuka_model.pkl") - act.save("kuka_model.pkl") + + env = KukaGymEnv(renders=False, isDiscrete=True) + model = deepq.models.mlp([64]) + act = deepq.learn(env, + q_func=model, + lr=1e-3, + max_timesteps=10000000, + buffer_size=50000, + exploration_fraction=0.1, + exploration_final_eps=0.02, + print_freq=10, + callback=callback) + print("Saving model to kuka_model.pkl") + act.save("kuka_model.pkl") if __name__ == '__main__': - main() + main() diff --git a/examples/pybullet/gym/pybullet_envs/baselines/train_pybullet_cartpole.py b/examples/pybullet/gym/pybullet_envs/baselines/train_pybullet_cartpole.py index f730d9154..9c369a3a2 100644 --- a/examples/pybullet/gym/pybullet_envs/baselines/train_pybullet_cartpole.py +++ b/examples/pybullet/gym/pybullet_envs/baselines/train_pybullet_cartpole.py @@ -2,7 +2,7 @@ import os, inspect currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe()))) parentdir = os.path.dirname(os.path.dirname(currentdir)) -os.sys.path.insert(0,parentdir) +os.sys.path.insert(0, parentdir) import gym from pybullet_envs.bullet.cartpole_bullet import CartPoleBulletEnv @@ -11,29 +11,27 @@ from baselines import deepq def callback(lcl, glb): - # stop training if reward exceeds 199 - is_solved = lcl['t'] > 100 and sum(lcl['episode_rewards'][-101:-1]) / 100 >= 199 - return is_solved + # stop training if reward exceeds 199 + is_solved = lcl['t'] > 100 and sum(lcl['episode_rewards'][-101:-1]) / 100 >= 199 + return is_solved def main(): - - env = CartPoleBulletEnv(renders=False) - model = deepq.models.mlp([64]) - act = deepq.learn( - env, - q_func=model, - lr=1e-3, - max_timesteps=100000, - buffer_size=50000, - exploration_fraction=0.1, - exploration_final_eps=0.02, - print_freq=10, - callback=callback - ) - print("Saving model to cartpole_model.pkl") - act.save("cartpole_model.pkl") + + env = CartPoleBulletEnv(renders=False) + model = deepq.models.mlp([64]) + act = deepq.learn(env, + q_func=model, + lr=1e-3, + max_timesteps=100000, + buffer_size=50000, + exploration_fraction=0.1, + exploration_final_eps=0.02, + print_freq=10, + callback=callback) + print("Saving model to cartpole_model.pkl") + act.save("cartpole_model.pkl") if __name__ == '__main__': - main() + main() diff --git a/examples/pybullet/gym/pybullet_envs/baselines/train_pybullet_racecar.py b/examples/pybullet/gym/pybullet_envs/baselines/train_pybullet_racecar.py index c17bc11c4..9b06bcfb2 100644 --- a/examples/pybullet/gym/pybullet_envs/baselines/train_pybullet_racecar.py +++ b/examples/pybullet/gym/pybullet_envs/baselines/train_pybullet_racecar.py @@ -2,7 +2,7 @@ import os, inspect currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe()))) parentdir = os.path.dirname(os.path.dirname(currentdir)) -os.sys.path.insert(0,parentdir) +os.sys.path.insert(0, parentdir) import gym from pybullet_envs.bullet.racecarGymEnv import RacecarGymEnv @@ -12,33 +12,30 @@ from baselines import deepq import datetime - def callback(lcl, glb): - # stop training if reward exceeds 199 - total = sum(lcl['episode_rewards'][-101:-1]) / 100 - totalt = lcl['t'] - is_solved = totalt > 2000 and total >= -50 - return is_solved + # stop training if reward exceeds 199 + total = sum(lcl['episode_rewards'][-101:-1]) / 100 + totalt = lcl['t'] + is_solved = totalt > 2000 and total >= -50 + return is_solved def main(): - - env = RacecarGymEnv(renders=False,isDiscrete=True) - model = deepq.models.mlp([64]) - act = deepq.learn( - env, - q_func=model, - lr=1e-3, - max_timesteps=10000, - buffer_size=50000, - exploration_fraction=0.1, - exploration_final_eps=0.02, - print_freq=10, - callback=callback - ) - print("Saving model to racecar_model.pkl") - act.save("racecar_model.pkl") + + env = RacecarGymEnv(renders=False, isDiscrete=True) + model = deepq.models.mlp([64]) + act = deepq.learn(env, + q_func=model, + lr=1e-3, + max_timesteps=10000, + buffer_size=50000, + exploration_fraction=0.1, + exploration_final_eps=0.02, + print_freq=10, + callback=callback) + print("Saving model to racecar_model.pkl") + act.save("racecar_model.pkl") if __name__ == '__main__': - main() + main() diff --git a/examples/pybullet/gym/pybullet_envs/baselines/train_pybullet_zed_racecar.py b/examples/pybullet/gym/pybullet_envs/baselines/train_pybullet_zed_racecar.py index 68c5b0046..825197b79 100644 --- a/examples/pybullet/gym/pybullet_envs/baselines/train_pybullet_zed_racecar.py +++ b/examples/pybullet/gym/pybullet_envs/baselines/train_pybullet_zed_racecar.py @@ -2,7 +2,7 @@ import os, inspect currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe()))) parentdir = os.path.dirname(os.path.dirname(currentdir)) -os.sys.path.insert(0,parentdir) +os.sys.path.insert(0, parentdir) import gym from pybullet_envs.bullet.racecarZEDGymEnv import RacecarZEDGymEnv @@ -12,36 +12,32 @@ from baselines import deepq import datetime - def callback(lcl, glb): - # stop training if reward exceeds 199 - total = sum(lcl['episode_rewards'][-101:-1]) / 100 - totalt = lcl['t'] - is_solved = totalt > 2000 and total >= -50 - return is_solved + # stop training if reward exceeds 199 + total = sum(lcl['episode_rewards'][-101:-1]) / 100 + totalt = lcl['t'] + is_solved = totalt > 2000 and total >= -50 + return is_solved + def main(): - - env = RacecarZEDGymEnv(renders=False, isDiscrete=True) - model = deepq.models.cnn_to_mlp( - convs=[(32, 8, 4), (64, 4, 2), (64, 3, 1)], - hiddens=[256], - dueling=False - ) - act = deepq.learn( - env, - q_func=model, - lr=1e-3, - max_timesteps=10000, - buffer_size=50000, - exploration_fraction=0.1, - exploration_final_eps=0.02, - print_freq=10, - callback=callback - ) - print("Saving model to racecar_zed_model.pkl") - act.save("racecar_zed_model.pkl") + + env = RacecarZEDGymEnv(renders=False, isDiscrete=True) + model = deepq.models.cnn_to_mlp(convs=[(32, 8, 4), (64, 4, 2), (64, 3, 1)], + hiddens=[256], + dueling=False) + act = deepq.learn(env, + q_func=model, + lr=1e-3, + max_timesteps=10000, + buffer_size=50000, + exploration_fraction=0.1, + exploration_final_eps=0.02, + print_freq=10, + callback=callback) + print("Saving model to racecar_zed_model.pkl") + act.save("racecar_zed_model.pkl") if __name__ == '__main__': - main() + main() diff --git a/examples/pybullet/gym/pybullet_envs/bullet/bullet_client.py b/examples/pybullet/gym/pybullet_envs/bullet/bullet_client.py index 13a82eef5..bd248f1d8 100644 --- a/examples/pybullet/gym/pybullet_envs/bullet/bullet_client.py +++ b/examples/pybullet/gym/pybullet_envs/bullet/bullet_client.py @@ -9,9 +9,9 @@ class BulletClient(object): def __init__(self, connection_mode=pybullet.DIRECT, options=""): """Create a simulation and connect to it.""" self._client = pybullet.connect(pybullet.SHARED_MEMORY) - if(self._client<0): - print("options=",options) - self._client = pybullet.connect(connection_mode,options=options) + if (self._client < 0): + print("options=", options) + self._client = pybullet.connect(connection_mode, options=options) self._shapes = {} def __del__(self): @@ -25,5 +25,5 @@ class BulletClient(object): """Inject the client id into Bullet functions.""" attribute = getattr(pybullet, name) if inspect.isbuiltin(attribute): - attribute = functools.partial(attribute, physicsClientId=self._client) + attribute = functools.partial(attribute, physicsClientId=self._client) return attribute diff --git a/examples/pybullet/gym/pybullet_envs/bullet/cartpole_bullet.py b/examples/pybullet/gym/pybullet_envs/bullet/cartpole_bullet.py index d3e945b1a..f1b594cde 100644 --- a/examples/pybullet/gym/pybullet_envs/bullet/cartpole_bullet.py +++ b/examples/pybullet/gym/pybullet_envs/bullet/cartpole_bullet.py @@ -2,10 +2,10 @@ Classic cart-pole system implemented by Rich Sutton et al. Copied from https://webdocs.cs.ualberta.ca/~sutton/book/code/pole.c """ -import os, inspect +import os, inspect currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe()))) parentdir = os.path.dirname(os.path.dirname(currentdir)) -os.sys.path.insert(0,parentdir) +os.sys.path.insert(0, parentdir) import logging import math @@ -21,26 +21,24 @@ from pkg_resources import parse_version logger = logging.getLogger(__name__) + class CartPoleBulletEnv(gym.Env): - metadata = { - 'render.modes': ['human', 'rgb_array'], - 'video.frames_per_second' : 50 - } + metadata = {'render.modes': ['human', 'rgb_array'], 'video.frames_per_second': 50} def __init__(self, renders=True): # start the bullet physics server self._renders = renders if (renders): - p.connect(p.GUI) + p.connect(p.GUI) else: - p.connect(p.DIRECT) + p.connect(p.DIRECT) self.theta_threshold_radians = 12 * 2 * math.pi / 360 - self.x_threshold = 0.4 #2.4 + self.x_threshold = 0.4 #2.4 high = np.array([ - self.x_threshold * 2, - np.finfo(np.float32).max, - self.theta_threshold_radians * 2, - np.finfo(np.float32).max]) + self.x_threshold * 2, + np.finfo(np.float32).max, self.theta_threshold_radians * 2, + np.finfo(np.float32).max + ]) self.force_mag = 10 @@ -60,7 +58,7 @@ class CartPoleBulletEnv(gym.Env): return [seed] def step(self, action): - force = self.force_mag if action==1 else -self.force_mag + force = self.force_mag if action == 1 else -self.force_mag p.setJointMotorControl2(self.cartpole, 0, p.TORQUE_CONTROL, force=force) p.stepSimulation() @@ -78,16 +76,17 @@ class CartPoleBulletEnv(gym.Env): return np.array(self.state), reward, done, {} def reset(self): -# print("-----------reset simulation---------------") + # print("-----------reset simulation---------------") p.resetSimulation() - self.cartpole = p.loadURDF(os.path.join(pybullet_data.getDataPath(),"cartpole.urdf"),[0,0,0]) + self.cartpole = p.loadURDF(os.path.join(pybullet_data.getDataPath(), "cartpole.urdf"), + [0, 0, 0]) p.changeDynamics(self.cartpole, -1, linearDamping=0, angularDamping=0) p.changeDynamics(self.cartpole, 0, linearDamping=0, angularDamping=0) p.changeDynamics(self.cartpole, 1, linearDamping=0, angularDamping=0) self.timeStep = 0.02 p.setJointMotorControl2(self.cartpole, 1, p.VELOCITY_CONTROL, force=0) p.setJointMotorControl2(self.cartpole, 0, p.VELOCITY_CONTROL, force=0) - p.setGravity(0,0, -9.8) + p.setGravity(0, 0, -9.8) p.setTimeStep(self.timeStep) p.setRealTimeSimulation(0) @@ -100,4 +99,4 @@ class CartPoleBulletEnv(gym.Env): return np.array(self.state) def render(self, mode='human', close=False): - return + return diff --git a/examples/pybullet/gym/pybullet_envs/bullet/env_randomizer_base.py b/examples/pybullet/gym/pybullet_envs/bullet/env_randomizer_base.py index c39f3b888..49dfc5372 100644 --- a/examples/pybullet/gym/pybullet_envs/bullet/env_randomizer_base.py +++ b/examples/pybullet/gym/pybullet_envs/bullet/env_randomizer_base.py @@ -22,4 +22,3 @@ class EnvRandomizerBase(object): env: The environment to be randomized. """ pass - diff --git a/examples/pybullet/gym/pybullet_envs/bullet/kuka.py b/examples/pybullet/gym/pybullet_envs/bullet/kuka.py index f9752760d..b2a4ffb30 100644 --- a/examples/pybullet/gym/pybullet_envs/bullet/kuka.py +++ b/examples/pybullet/gym/pybullet_envs/bullet/kuka.py @@ -1,7 +1,7 @@ -import os, inspect +import os, inspect currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe()))) parentdir = os.path.dirname(os.path.dirname(currentdir)) -os.sys.path.insert(0,parentdir) +os.sys.path.insert(0, parentdir) import pybullet as p import numpy as np @@ -17,49 +17,60 @@ class Kuka: self.timeStep = timeStep self.maxVelocity = .35 self.maxForce = 200. - self.fingerAForce = 2 + self.fingerAForce = 2 self.fingerBForce = 2.5 self.fingerTipForce = 2 self.useInverseKinematics = 1 self.useSimulation = 1 - self.useNullSpace =21 + self.useNullSpace = 21 self.useOrientation = 1 self.kukaEndEffectorIndex = 6 self.kukaGripperIndex = 7 #lower limits for null space - self.ll=[-.967,-2 ,-2.96,0.19,-2.96,-2.09,-3.05] + self.ll = [-.967, -2, -2.96, 0.19, -2.96, -2.09, -3.05] #upper limits for null space - self.ul=[.967,2 ,2.96,2.29,2.96,2.09,3.05] + self.ul = [.967, 2, 2.96, 2.29, 2.96, 2.09, 3.05] #joint ranges for null space - self.jr=[5.8,4,5.8,4,5.8,4,6] + self.jr = [5.8, 4, 5.8, 4, 5.8, 4, 6] #restposes for null space - self.rp=[0,0,0,0.5*math.pi,0,-math.pi*0.5*0.66,0] + self.rp = [0, 0, 0, 0.5 * math.pi, 0, -math.pi * 0.5 * 0.66, 0] #joint damping coefficents - self.jd=[0.00001,0.00001,0.00001,0.00001,0.00001,0.00001,0.00001,0.00001,0.00001,0.00001,0.00001,0.00001,0.00001,0.00001] + self.jd = [ + 0.00001, 0.00001, 0.00001, 0.00001, 0.00001, 0.00001, 0.00001, 0.00001, 0.00001, 0.00001, + 0.00001, 0.00001, 0.00001, 0.00001 + ] self.reset() - + def reset(self): - objects = p.loadSDF(os.path.join(self.urdfRootPath,"kuka_iiwa/kuka_with_gripper2.sdf")) + objects = p.loadSDF(os.path.join(self.urdfRootPath, "kuka_iiwa/kuka_with_gripper2.sdf")) self.kukaUid = objects[0] #for i in range (p.getNumJoints(self.kukaUid)): # print(p.getJointInfo(self.kukaUid,i)) - p.resetBasePositionAndOrientation(self.kukaUid,[-0.100000,0.000000,0.070000],[0.000000,0.000000,0.000000,1.000000]) - self.jointPositions=[ 0.006418, 0.413184, -0.011401, -1.589317, 0.005379, 1.137684, -0.006539, 0.000048, -0.299912, 0.000000, -0.000043, 0.299960, 0.000000, -0.000200 ] + p.resetBasePositionAndOrientation(self.kukaUid, [-0.100000, 0.000000, 0.070000], + [0.000000, 0.000000, 0.000000, 1.000000]) + self.jointPositions = [ + 0.006418, 0.413184, -0.011401, -1.589317, 0.005379, 1.137684, -0.006539, 0.000048, + -0.299912, 0.000000, -0.000043, 0.299960, 0.000000, -0.000200 + ] self.numJoints = p.getNumJoints(self.kukaUid) - for jointIndex in range (self.numJoints): - p.resetJointState(self.kukaUid,jointIndex,self.jointPositions[jointIndex]) - p.setJointMotorControl2(self.kukaUid,jointIndex,p.POSITION_CONTROL,targetPosition=self.jointPositions[jointIndex],force=self.maxForce) - - self.trayUid = p.loadURDF(os.path.join(self.urdfRootPath,"tray/tray.urdf"), 0.640000,0.075000,-0.190000,0.000000,0.000000,1.000000,0.000000) - self.endEffectorPos = [0.537,0.0,0.5] + for jointIndex in range(self.numJoints): + p.resetJointState(self.kukaUid, jointIndex, self.jointPositions[jointIndex]) + p.setJointMotorControl2(self.kukaUid, + jointIndex, + p.POSITION_CONTROL, + targetPosition=self.jointPositions[jointIndex], + force=self.maxForce) + + self.trayUid = p.loadURDF(os.path.join(self.urdfRootPath, "tray/tray.urdf"), 0.640000, + 0.075000, -0.190000, 0.000000, 0.000000, 1.000000, 0.000000) + self.endEffectorPos = [0.537, 0.0, 0.5] self.endEffectorAngle = 0 - - + self.motorNames = [] self.motorIndices = [] - - for i in range (self.numJoints): - jointInfo = p.getJointInfo(self.kukaUid,i) + + for i in range(self.numJoints): + jointInfo = p.getJointInfo(self.kukaUid, i) qIndex = jointInfo[3] if qIndex > -1: #print("motorname") @@ -70,98 +81,136 @@ class Kuka: def getActionDimension(self): if (self.useInverseKinematics): return len(self.motorIndices) - return 6 #position x,y,z and roll/pitch/yaw euler angles of end effector + return 6 #position x,y,z and roll/pitch/yaw euler angles of end effector def getObservationDimension(self): return len(self.getObservation()) def getObservation(self): observation = [] - state = p.getLinkState(self.kukaUid,self.kukaGripperIndex) + state = p.getLinkState(self.kukaUid, self.kukaGripperIndex) pos = state[0] orn = state[1] euler = p.getEulerFromQuaternion(orn) - + observation.extend(list(pos)) observation.extend(list(euler)) - + return observation def applyAction(self, motorCommands): - + #print ("self.numJoints") #print (self.numJoints) if (self.useInverseKinematics): - + dx = motorCommands[0] dy = motorCommands[1] dz = motorCommands[2] da = motorCommands[3] fingerAngle = motorCommands[4] - - state = p.getLinkState(self.kukaUid,self.kukaEndEffectorIndex) + + state = p.getLinkState(self.kukaUid, self.kukaEndEffectorIndex) actualEndEffectorPos = state[0] #print("pos[2] (getLinkState(kukaEndEffectorIndex)") #print(actualEndEffectorPos[2]) - - - - self.endEffectorPos[0] = self.endEffectorPos[0]+dx - if (self.endEffectorPos[0]>0.65): - self.endEffectorPos[0]=0.65 - if (self.endEffectorPos[0]<0.50): - self.endEffectorPos[0]=0.50 - self.endEffectorPos[1] = self.endEffectorPos[1]+dy - if (self.endEffectorPos[1]<-0.17): - self.endEffectorPos[1]=-0.17 - if (self.endEffectorPos[1]>0.22): - self.endEffectorPos[1]=0.22 - + + self.endEffectorPos[0] = self.endEffectorPos[0] + dx + if (self.endEffectorPos[0] > 0.65): + self.endEffectorPos[0] = 0.65 + if (self.endEffectorPos[0] < 0.50): + self.endEffectorPos[0] = 0.50 + self.endEffectorPos[1] = self.endEffectorPos[1] + dy + if (self.endEffectorPos[1] < -0.17): + self.endEffectorPos[1] = -0.17 + if (self.endEffectorPos[1] > 0.22): + self.endEffectorPos[1] = 0.22 + #print ("self.endEffectorPos[2]") #print (self.endEffectorPos[2]) #print("actualEndEffectorPos[2]") #print(actualEndEffectorPos[2]) #if (dz<0 or actualEndEffectorPos[2]<0.5): - self.endEffectorPos[2] = self.endEffectorPos[2]+dz - - + self.endEffectorPos[2] = self.endEffectorPos[2] + dz + self.endEffectorAngle = self.endEffectorAngle + da pos = self.endEffectorPos - orn = p.getQuaternionFromEuler([0,-math.pi,0]) # -math.pi,yaw]) - if (self.useNullSpace==1): - if (self.useOrientation==1): - jointPoses = p.calculateInverseKinematics(self.kukaUid,self.kukaEndEffectorIndex,pos,orn,self.ll,self.ul,self.jr,self.rp) + orn = p.getQuaternionFromEuler([0, -math.pi, 0]) # -math.pi,yaw]) + if (self.useNullSpace == 1): + if (self.useOrientation == 1): + jointPoses = p.calculateInverseKinematics(self.kukaUid, self.kukaEndEffectorIndex, pos, + orn, self.ll, self.ul, self.jr, self.rp) else: - jointPoses = p.calculateInverseKinematics(self.kukaUid,self.kukaEndEffectorIndex,pos,lowerLimits=self.ll, upperLimits=self.ul, jointRanges=self.jr, restPoses=self.rp) + jointPoses = p.calculateInverseKinematics(self.kukaUid, + self.kukaEndEffectorIndex, + pos, + lowerLimits=self.ll, + upperLimits=self.ul, + jointRanges=self.jr, + restPoses=self.rp) else: - if (self.useOrientation==1): - jointPoses = p.calculateInverseKinematics(self.kukaUid,self.kukaEndEffectorIndex,pos,orn,jointDamping=self.jd) + if (self.useOrientation == 1): + jointPoses = p.calculateInverseKinematics(self.kukaUid, + self.kukaEndEffectorIndex, + pos, + orn, + jointDamping=self.jd) else: - jointPoses = p.calculateInverseKinematics(self.kukaUid,self.kukaEndEffectorIndex,pos) - + jointPoses = p.calculateInverseKinematics(self.kukaUid, self.kukaEndEffectorIndex, pos) + #print("jointPoses") #print(jointPoses) #print("self.kukaEndEffectorIndex") #print(self.kukaEndEffectorIndex) if (self.useSimulation): - for i in range (self.kukaEndEffectorIndex+1): + for i in range(self.kukaEndEffectorIndex + 1): #print(i) - p.setJointMotorControl2(bodyUniqueId=self.kukaUid,jointIndex=i,controlMode=p.POSITION_CONTROL,targetPosition=jointPoses[i],targetVelocity=0,force=self.maxForce,maxVelocity=self.maxVelocity, positionGain=0.3,velocityGain=1) + p.setJointMotorControl2(bodyUniqueId=self.kukaUid, + jointIndex=i, + controlMode=p.POSITION_CONTROL, + targetPosition=jointPoses[i], + targetVelocity=0, + force=self.maxForce, + maxVelocity=self.maxVelocity, + positionGain=0.3, + velocityGain=1) else: #reset the joint state (ignoring all dynamics, not recommended to use during simulation) - for i in range (self.numJoints): - p.resetJointState(self.kukaUid,i,jointPoses[i]) + for i in range(self.numJoints): + p.resetJointState(self.kukaUid, i, jointPoses[i]) #fingers - p.setJointMotorControl2(self.kukaUid,7,p.POSITION_CONTROL,targetPosition=self.endEffectorAngle,force=self.maxForce) - p.setJointMotorControl2(self.kukaUid,8,p.POSITION_CONTROL,targetPosition=-fingerAngle,force=self.fingerAForce) - p.setJointMotorControl2(self.kukaUid,11,p.POSITION_CONTROL,targetPosition=fingerAngle,force=self.fingerBForce) - - p.setJointMotorControl2(self.kukaUid,10,p.POSITION_CONTROL,targetPosition=0,force=self.fingerTipForce) - p.setJointMotorControl2(self.kukaUid,13,p.POSITION_CONTROL,targetPosition=0,force=self.fingerTipForce) - - + p.setJointMotorControl2(self.kukaUid, + 7, + p.POSITION_CONTROL, + targetPosition=self.endEffectorAngle, + force=self.maxForce) + p.setJointMotorControl2(self.kukaUid, + 8, + p.POSITION_CONTROL, + targetPosition=-fingerAngle, + force=self.fingerAForce) + p.setJointMotorControl2(self.kukaUid, + 11, + p.POSITION_CONTROL, + targetPosition=fingerAngle, + force=self.fingerBForce) + + p.setJointMotorControl2(self.kukaUid, + 10, + p.POSITION_CONTROL, + targetPosition=0, + force=self.fingerTipForce) + p.setJointMotorControl2(self.kukaUid, + 13, + p.POSITION_CONTROL, + targetPosition=0, + force=self.fingerTipForce) + else: - for action in range (len(motorCommands)): + for action in range(len(motorCommands)): motor = self.motorIndices[action] - p.setJointMotorControl2(self.kukaUid,motor,p.POSITION_CONTROL,targetPosition=motorCommands[action],force=self.maxForce) - + p.setJointMotorControl2(self.kukaUid, + motor, + p.POSITION_CONTROL, + targetPosition=motorCommands[action], + force=self.maxForce) diff --git a/examples/pybullet/gym/pybullet_envs/bullet/kukaCamGymEnv.py b/examples/pybullet/gym/pybullet_envs/bullet/kukaCamGymEnv.py index d7c0cac51..235bfa42d 100644 --- a/examples/pybullet/gym/pybullet_envs/bullet/kukaCamGymEnv.py +++ b/examples/pybullet/gym/pybullet_envs/bullet/kukaCamGymEnv.py @@ -1,8 +1,7 @@ -import os, inspect +import os, inspect currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe()))) parentdir = os.path.dirname(os.path.dirname(currentdir)) -os.sys.path.insert(0,parentdir) - +os.sys.path.insert(0, parentdir) import math import gym @@ -21,11 +20,9 @@ maxSteps = 1000 RENDER_HEIGHT = 720 RENDER_WIDTH = 960 + class KukaCamGymEnv(gym.Env): - metadata = { - 'render.modes': ['human', 'rgb_array'], - 'video.frames_per_second' : 50 - } + metadata = {'render.modes': ['human', 'rgb_array'], 'video.frames_per_second': 50} def __init__(self, urdfRoot=pybullet_data.getDataPath(), @@ -33,7 +30,7 @@ class KukaCamGymEnv(gym.Env): isEnableSelfCollision=True, renders=False, isDiscrete=False): - self._timeStep = 1./240. + self._timeStep = 1. / 240. self._urdfRoot = urdfRoot self._actionRepeat = actionRepeat self._isEnableSelfCollision = isEnableSelfCollision @@ -42,14 +39,14 @@ class KukaCamGymEnv(gym.Env): self._renders = renders self._width = 341 self._height = 256 - self._isDiscrete=isDiscrete + self._isDiscrete = isDiscrete self.terminated = 0 self._p = p if self._renders: cid = p.connect(p.SHARED_MEMORY) - if (cid<0): - p.connect(p.GUI) - p.resetDebugVisualizerCamera(1.3,180,-41,[0.52,-0.2,-0.33]) + if (cid < 0): + p.connect(p.GUI) + p.resetDebugVisualizerCamera(1.3, 180, -41, [0.52, -0.2, -0.33]) else: p.connect(p.DIRECT) #timinglog = p.startStateLogging(p.STATE_LOGGING_PROFILE_TIMINGS, "kukaTimings.json") @@ -67,7 +64,10 @@ class KukaCamGymEnv(gym.Env): self._action_bound = 1 action_high = np.array([self._action_bound] * action_dim) self.action_space = spaces.Box(-action_high, action_high, dtype=np.float32) - self.observation_space = spaces.Box(low=0, high=255, shape=(self._height, self._width, 4), dtype=np.uint8) + self.observation_space = spaces.Box(low=0, + high=255, + shape=(self._height, self._width, 4), + dtype=np.uint8) self.viewer = None def reset(self): @@ -75,17 +75,19 @@ class KukaCamGymEnv(gym.Env): p.resetSimulation() p.setPhysicsEngineParameter(numSolverIterations=150) p.setTimeStep(self._timeStep) - p.loadURDF(os.path.join(self._urdfRoot,"plane.urdf"),[0,0,-1]) + p.loadURDF(os.path.join(self._urdfRoot, "plane.urdf"), [0, 0, -1]) - p.loadURDF(os.path.join(self._urdfRoot,"table/table.urdf"), 0.5000000,0.00000,-.820000,0.000000,0.000000,0.0,1.0) + p.loadURDF(os.path.join(self._urdfRoot, "table/table.urdf"), 0.5000000, 0.00000, -.820000, + 0.000000, 0.000000, 0.0, 1.0) - xpos = 0.5 +0.2*random.random() - ypos = 0 +0.25*random.random() - ang = 3.1415925438*random.random() - orn = p.getQuaternionFromEuler([0,0,ang]) - self.blockUid =p.loadURDF(os.path.join(self._urdfRoot,"block.urdf"), xpos,ypos,-0.1,orn[0],orn[1],orn[2],orn[3]) + xpos = 0.5 + 0.2 * random.random() + ypos = 0 + 0.25 * random.random() + ang = 3.1415925438 * random.random() + orn = p.getQuaternionFromEuler([0, 0, ang]) + self.blockUid = p.loadURDF(os.path.join(self._urdfRoot, "block.urdf"), xpos, ypos, -0.1, + orn[0], orn[1], orn[2], orn[3]) - p.setGravity(0,0,-10) + p.setGravity(0, 0, -10) self._kuka = kuka.Kuka(urdfRootPath=self._urdfRoot, timeStep=self._timeStep) self._envStepCounter = 0 p.stepSimulation() @@ -101,49 +103,59 @@ class KukaCamGymEnv(gym.Env): def getExtendedObservation(self): - #camEyePos = [0.03,0.236,0.54] - #distance = 1.06 - #pitch=-56 - #yaw = 258 - #roll=0 - #upAxisIndex = 2 - #camInfo = p.getDebugVisualizerCamera() - #print("width,height") - #print(camInfo[0]) - #print(camInfo[1]) - #print("viewMatrix") - #print(camInfo[2]) - #print("projectionMatrix") - #print(camInfo[3]) - #viewMat = camInfo[2] - #viewMat = p.computeViewMatrixFromYawPitchRoll(camEyePos,distance,yaw, pitch,roll,upAxisIndex) - viewMat = [-0.5120397806167603, 0.7171027660369873, -0.47284144163131714, 0.0, -0.8589617609977722, -0.42747554183006287, 0.28186774253845215, 0.0, 0.0, 0.5504802465438843, 0.8348482847213745, 0.0, 0.1925382763147354, -0.24935829639434814, -0.4401884973049164, 1.0] - #projMatrix = camInfo[3]#[0.7499999403953552, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, -1.0000200271606445, -1.0, 0.0, 0.0, -0.02000020071864128, 0.0] - projMatrix = [0.75, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, -1.0000200271606445, -1.0, 0.0, 0.0, -0.02000020071864128, 0.0] - - img_arr = p.getCameraImage(width=self._width,height=self._height,viewMatrix=viewMat,projectionMatrix=projMatrix) - rgb=img_arr[2] - np_img_arr = np.reshape(rgb, (self._height, self._width, 4)) - self._observation = np_img_arr - return self._observation + #camEyePos = [0.03,0.236,0.54] + #distance = 1.06 + #pitch=-56 + #yaw = 258 + #roll=0 + #upAxisIndex = 2 + #camInfo = p.getDebugVisualizerCamera() + #print("width,height") + #print(camInfo[0]) + #print(camInfo[1]) + #print("viewMatrix") + #print(camInfo[2]) + #print("projectionMatrix") + #print(camInfo[3]) + #viewMat = camInfo[2] + #viewMat = p.computeViewMatrixFromYawPitchRoll(camEyePos,distance,yaw, pitch,roll,upAxisIndex) + viewMat = [ + -0.5120397806167603, 0.7171027660369873, -0.47284144163131714, 0.0, -0.8589617609977722, + -0.42747554183006287, 0.28186774253845215, 0.0, 0.0, 0.5504802465438843, + 0.8348482847213745, 0.0, 0.1925382763147354, -0.24935829639434814, -0.4401884973049164, 1.0 + ] + #projMatrix = camInfo[3]#[0.7499999403953552, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, -1.0000200271606445, -1.0, 0.0, 0.0, -0.02000020071864128, 0.0] + projMatrix = [ + 0.75, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, -1.0000200271606445, -1.0, 0.0, 0.0, + -0.02000020071864128, 0.0 + ] + + img_arr = p.getCameraImage(width=self._width, + height=self._height, + viewMatrix=viewMat, + projectionMatrix=projMatrix) + rgb = img_arr[2] + np_img_arr = np.reshape(rgb, (self._height, self._width, 4)) + self._observation = np_img_arr + return self._observation def step(self, action): if (self._isDiscrete): dv = 0.01 - dx = [0,-dv,dv,0,0,0,0][action] - dy = [0,0,0,-dv,dv,0,0][action] - da = [0,0,0,0,0,-0.1,0.1][action] + dx = [0, -dv, dv, 0, 0, 0, 0][action] + dy = [0, 0, 0, -dv, dv, 0, 0][action] + da = [0, 0, 0, 0, 0, -0.1, 0.1][action] f = 0.3 - realAction = [dx,dy,-0.002,da,f] + realAction = [dx, dy, -0.002, da, f] else: dv = 0.01 dx = action[0] * dv dy = action[1] * dv da = action[2] * 0.1 f = 0.3 - realAction = [dx,dy,-0.002,da,f] + realAction = [dx, dy, -0.002, da, f] - return self.step2( realAction) + return self.step2(realAction) def step2(self, action): for i in range(self._actionRepeat): @@ -156,7 +168,7 @@ class KukaCamGymEnv(gym.Env): self._observation = self.getExtendedObservation() if self._renders: - time.sleep(self._timeStep) + time.sleep(self._timeStep) #print("self._envStepCounter") #print(self._envStepCounter) @@ -170,66 +182,67 @@ class KukaCamGymEnv(gym.Env): def render(self, mode='human', close=False): if mode != "rgb_array": return np.array([]) - base_pos,orn = self._p.getBasePositionAndOrientation(self._racecar.racecarUniqueId) - view_matrix = self._p.computeViewMatrixFromYawPitchRoll( - cameraTargetPosition=base_pos, - distance=self._cam_dist, - yaw=self._cam_yaw, - pitch=self._cam_pitch, - roll=0, - upAxisIndex=2) - proj_matrix = self._p.computeProjectionMatrixFOV( - fov=60, aspect=float(RENDER_WIDTH)/RENDER_HEIGHT, - nearVal=0.1, farVal=100.0) - (_, _, px, _, _) = self._p.getCameraImage( - width=RENDER_WIDTH, height=RENDER_HEIGHT, viewMatrix=view_matrix, - projectionMatrix=proj_matrix, renderer=pybullet.ER_BULLET_HARDWARE_OPENGL) + base_pos, orn = self._p.getBasePositionAndOrientation(self._racecar.racecarUniqueId) + view_matrix = self._p.computeViewMatrixFromYawPitchRoll(cameraTargetPosition=base_pos, + distance=self._cam_dist, + yaw=self._cam_yaw, + pitch=self._cam_pitch, + roll=0, + upAxisIndex=2) + proj_matrix = self._p.computeProjectionMatrixFOV(fov=60, + aspect=float(RENDER_WIDTH) / RENDER_HEIGHT, + nearVal=0.1, + farVal=100.0) + (_, _, px, _, _) = self._p.getCameraImage(width=RENDER_WIDTH, + height=RENDER_HEIGHT, + viewMatrix=view_matrix, + projectionMatrix=proj_matrix, + renderer=pybullet.ER_BULLET_HARDWARE_OPENGL) rgb_array = np.array(px) rgb_array = rgb_array[:, :, :3] return rgb_array def _termination(self): #print (self._kuka.endEffectorPos[2]) - state = p.getLinkState(self._kuka.kukaUid,self._kuka.kukaEndEffectorIndex) + state = p.getLinkState(self._kuka.kukaUid, self._kuka.kukaEndEffectorIndex) actualEndEffectorPos = state[0] #print("self._envStepCounter") #print(self._envStepCounter) - if (self.terminated or self._envStepCounter>maxSteps): + if (self.terminated or self._envStepCounter > maxSteps): self._observation = self.getExtendedObservation() return True maxDist = 0.005 - closestPoints = p.getClosestPoints(self._kuka.trayUid, self._kuka.kukaUid,maxDist) + closestPoints = p.getClosestPoints(self._kuka.trayUid, self._kuka.kukaUid, maxDist) - if (len(closestPoints)):#(actualEndEffectorPos[2] <= -0.43): + if (len(closestPoints)): #(actualEndEffectorPos[2] <= -0.43): self.terminated = 1 #print("closing gripper, attempting grasp") #start grasp and terminate fingerAngle = 0.3 - for i in range (100): - graspAction = [0,0,0.0001,0,fingerAngle] + for i in range(100): + graspAction = [0, 0, 0.0001, 0, fingerAngle] self._kuka.applyAction(graspAction) p.stepSimulation() - fingerAngle = fingerAngle-(0.3/100.) - if (fingerAngle<0): - fingerAngle=0 + fingerAngle = fingerAngle - (0.3 / 100.) + if (fingerAngle < 0): + fingerAngle = 0 - for i in range (1000): - graspAction = [0,0,0.001,0,fingerAngle] + for i in range(1000): + graspAction = [0, 0, 0.001, 0, fingerAngle] self._kuka.applyAction(graspAction) p.stepSimulation() - blockPos,blockOrn=p.getBasePositionAndOrientation(self.blockUid) + blockPos, blockOrn = p.getBasePositionAndOrientation(self.blockUid) if (blockPos[2] > 0.23): #print("BLOCKPOS!") #print(blockPos[2]) break - state = p.getLinkState(self._kuka.kukaUid,self._kuka.kukaEndEffectorIndex) + state = p.getLinkState(self._kuka.kukaUid, self._kuka.kukaEndEffectorIndex) actualEndEffectorPos = state[0] - if (actualEndEffectorPos[2]>0.5): + if (actualEndEffectorPos[2] > 0.5): break - self._observation = self.getExtendedObservation() return True return False @@ -237,20 +250,21 @@ class KukaCamGymEnv(gym.Env): def _reward(self): #rewards is height of target object - blockPos,blockOrn=p.getBasePositionAndOrientation(self.blockUid) - closestPoints = p.getClosestPoints(self.blockUid,self._kuka.kukaUid,1000, -1, self._kuka.kukaEndEffectorIndex) + blockPos, blockOrn = p.getBasePositionAndOrientation(self.blockUid) + closestPoints = p.getClosestPoints(self.blockUid, self._kuka.kukaUid, 1000, -1, + self._kuka.kukaEndEffectorIndex) reward = -1000 numPt = len(closestPoints) #print(numPt) - if (numPt>0): + if (numPt > 0): #print("reward:") - reward = -closestPoints[0][8]*10 - if (blockPos[2] >0.2): + reward = -closestPoints[0][8] * 10 + if (blockPos[2] > 0.2): #print("grasped a block!!!") #print("self._envStepCounter") #print(self._envStepCounter) - reward = reward+1000 + reward = reward + 1000 #print("reward") #print(reward) diff --git a/examples/pybullet/gym/pybullet_envs/bullet/kukaGymEnv.py b/examples/pybullet/gym/pybullet_envs/bullet/kukaGymEnv.py index e1310c861..f47a0dd13 100644 --- a/examples/pybullet/gym/pybullet_envs/bullet/kukaGymEnv.py +++ b/examples/pybullet/gym/pybullet_envs/bullet/kukaGymEnv.py @@ -1,7 +1,7 @@ import os, inspect currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe()))) -print ("current_dir=" + currentdir) -os.sys.path.insert(0,currentdir) +print("current_dir=" + currentdir) +os.sys.path.insert(0, currentdir) import math import gym @@ -20,11 +20,9 @@ largeValObservation = 100 RENDER_HEIGHT = 720 RENDER_WIDTH = 960 + class KukaGymEnv(gym.Env): - metadata = { - 'render.modes': ['human', 'rgb_array'], - 'video.frames_per_second' : 50 - } + metadata = {'render.modes': ['human', 'rgb_array'], 'video.frames_per_second': 50} def __init__(self, urdfRoot=pybullet_data.getDataPath(), @@ -32,10 +30,10 @@ class KukaGymEnv(gym.Env): isEnableSelfCollision=True, renders=False, isDiscrete=False, - maxSteps = 1000): + maxSteps=1000): #print("KukaGymEnv __init__") self._isDiscrete = isDiscrete - self._timeStep = 1./240. + self._timeStep = 1. / 240. self._urdfRoot = urdfRoot self._actionRepeat = actionRepeat self._isEnableSelfCollision = isEnableSelfCollision @@ -51,9 +49,9 @@ class KukaGymEnv(gym.Env): self._p = p if self._renders: cid = p.connect(p.SHARED_MEMORY) - if (cid<0): - cid = p.connect(p.GUI) - p.resetDebugVisualizerCamera(1.3,180,-41,[0.52,-0.2,-0.33]) + if (cid < 0): + cid = p.connect(p.GUI) + p.resetDebugVisualizerCamera(1.3, 180, -41, [0.52, -0.2, -0.33]) else: p.connect(p.DIRECT) #timinglog = p.startStateLogging(p.STATE_LOGGING_PROFILE_TIMINGS, "kukaTimings.json") @@ -67,10 +65,10 @@ class KukaGymEnv(gym.Env): if (self._isDiscrete): self.action_space = spaces.Discrete(7) else: - action_dim = 3 - self._action_bound = 1 - action_high = np.array([self._action_bound] * action_dim) - self.action_space = spaces.Box(-action_high, action_high) + action_dim = 3 + self._action_bound = 1 + action_high = np.array([self._action_bound] * action_dim) + self.action_space = spaces.Box(-action_high, action_high) self.observation_space = spaces.Box(-observation_high, observation_high) self.viewer = None @@ -80,17 +78,19 @@ class KukaGymEnv(gym.Env): p.resetSimulation() p.setPhysicsEngineParameter(numSolverIterations=150) p.setTimeStep(self._timeStep) - p.loadURDF(os.path.join(self._urdfRoot,"plane.urdf"),[0,0,-1]) + p.loadURDF(os.path.join(self._urdfRoot, "plane.urdf"), [0, 0, -1]) - p.loadURDF(os.path.join(self._urdfRoot,"table/table.urdf"), 0.5000000,0.00000,-.820000,0.000000,0.000000,0.0,1.0) + p.loadURDF(os.path.join(self._urdfRoot, "table/table.urdf"), 0.5000000, 0.00000, -.820000, + 0.000000, 0.000000, 0.0, 1.0) - xpos = 0.55 +0.12*random.random() - ypos = 0 +0.2*random.random() - ang = 3.14*0.5+3.1415925438*random.random() - orn = p.getQuaternionFromEuler([0,0,ang]) - self.blockUid =p.loadURDF(os.path.join(self._urdfRoot,"block.urdf"), xpos,ypos,-0.15,orn[0],orn[1],orn[2],orn[3]) + xpos = 0.55 + 0.12 * random.random() + ypos = 0 + 0.2 * random.random() + ang = 3.14 * 0.5 + 3.1415925438 * random.random() + orn = p.getQuaternionFromEuler([0, 0, ang]) + self.blockUid = p.loadURDF(os.path.join(self._urdfRoot, "block.urdf"), xpos, ypos, -0.15, + orn[0], orn[1], orn[2], orn[3]) - p.setGravity(0,0,-10) + p.setGravity(0, 0, -10) self._kuka = kuka.Kuka(urdfRootPath=self._urdfRoot, timeStep=self._timeStep) self._envStepCounter = 0 p.stepSimulation() @@ -105,47 +105,48 @@ class KukaGymEnv(gym.Env): return [seed] def getExtendedObservation(self): - self._observation = self._kuka.getObservation() - gripperState = p.getLinkState(self._kuka.kukaUid,self._kuka.kukaGripperIndex) - gripperPos = gripperState[0] - gripperOrn = gripperState[1] - blockPos,blockOrn = p.getBasePositionAndOrientation(self.blockUid) - - invGripperPos,invGripperOrn = p.invertTransform(gripperPos,gripperOrn) - gripperMat = p.getMatrixFromQuaternion(gripperOrn) - dir0 = [gripperMat[0],gripperMat[3],gripperMat[6]] - dir1 = [gripperMat[1],gripperMat[4],gripperMat[7]] - dir2 = [gripperMat[2],gripperMat[5],gripperMat[8]] - - gripperEul = p.getEulerFromQuaternion(gripperOrn) - #print("gripperEul") - #print(gripperEul) - blockPosInGripper,blockOrnInGripper = p.multiplyTransforms(invGripperPos,invGripperOrn,blockPos,blockOrn) - projectedBlockPos2D =[blockPosInGripper[0],blockPosInGripper[1]] - blockEulerInGripper = p.getEulerFromQuaternion(blockOrnInGripper) - #print("projectedBlockPos2D") - #print(projectedBlockPos2D) - #print("blockEulerInGripper") - #print(blockEulerInGripper) - - #we return the relative x,y position and euler angle of block in gripper space - blockInGripperPosXYEulZ =[blockPosInGripper[0],blockPosInGripper[1],blockEulerInGripper[2]] - - #p.addUserDebugLine(gripperPos,[gripperPos[0]+dir0[0],gripperPos[1]+dir0[1],gripperPos[2]+dir0[2]],[1,0,0],lifeTime=1) - #p.addUserDebugLine(gripperPos,[gripperPos[0]+dir1[0],gripperPos[1]+dir1[1],gripperPos[2]+dir1[2]],[0,1,0],lifeTime=1) - #p.addUserDebugLine(gripperPos,[gripperPos[0]+dir2[0],gripperPos[1]+dir2[1],gripperPos[2]+dir2[2]],[0,0,1],lifeTime=1) - - self._observation.extend(list(blockInGripperPosXYEulZ)) - return self._observation + self._observation = self._kuka.getObservation() + gripperState = p.getLinkState(self._kuka.kukaUid, self._kuka.kukaGripperIndex) + gripperPos = gripperState[0] + gripperOrn = gripperState[1] + blockPos, blockOrn = p.getBasePositionAndOrientation(self.blockUid) + + invGripperPos, invGripperOrn = p.invertTransform(gripperPos, gripperOrn) + gripperMat = p.getMatrixFromQuaternion(gripperOrn) + dir0 = [gripperMat[0], gripperMat[3], gripperMat[6]] + dir1 = [gripperMat[1], gripperMat[4], gripperMat[7]] + dir2 = [gripperMat[2], gripperMat[5], gripperMat[8]] + + gripperEul = p.getEulerFromQuaternion(gripperOrn) + #print("gripperEul") + #print(gripperEul) + blockPosInGripper, blockOrnInGripper = p.multiplyTransforms(invGripperPos, invGripperOrn, + blockPos, blockOrn) + projectedBlockPos2D = [blockPosInGripper[0], blockPosInGripper[1]] + blockEulerInGripper = p.getEulerFromQuaternion(blockOrnInGripper) + #print("projectedBlockPos2D") + #print(projectedBlockPos2D) + #print("blockEulerInGripper") + #print(blockEulerInGripper) + + #we return the relative x,y position and euler angle of block in gripper space + blockInGripperPosXYEulZ = [blockPosInGripper[0], blockPosInGripper[1], blockEulerInGripper[2]] + + #p.addUserDebugLine(gripperPos,[gripperPos[0]+dir0[0],gripperPos[1]+dir0[1],gripperPos[2]+dir0[2]],[1,0,0],lifeTime=1) + #p.addUserDebugLine(gripperPos,[gripperPos[0]+dir1[0],gripperPos[1]+dir1[1],gripperPos[2]+dir1[2]],[0,1,0],lifeTime=1) + #p.addUserDebugLine(gripperPos,[gripperPos[0]+dir2[0],gripperPos[1]+dir2[1],gripperPos[2]+dir2[2]],[0,0,1],lifeTime=1) + + self._observation.extend(list(blockInGripperPosXYEulZ)) + return self._observation def step(self, action): if (self._isDiscrete): dv = 0.005 - dx = [0,-dv,dv,0,0,0,0][action] - dy = [0,0,0,-dv,dv,0,0][action] - da = [0,0,0,0,0,-0.05,0.05][action] + dx = [0, -dv, dv, 0, 0, 0, 0][action] + dy = [0, 0, 0, -dv, dv, 0, 0][action] + da = [0, 0, 0, 0, 0, -0.05, 0.05][action] f = 0.3 - realAction = [dx,dy,-0.002,da,f] + realAction = [dx, dy, -0.002, da, f] else: #print("action[0]=", str(action[0])) dv = 0.005 @@ -153,8 +154,8 @@ class KukaGymEnv(gym.Env): dy = action[1] * dv da = action[2] * 0.05 f = 0.3 - realAction = [dx,dy,-0.002,da,f] - return self.step2( realAction) + realAction = [dx, dy, -0.002, da, f] + return self.step2(realAction) def step2(self, action): for i in range(self._actionRepeat): @@ -171,11 +172,13 @@ class KukaGymEnv(gym.Env): #print(self._envStepCounter) done = self._termination() - npaction = np.array([action[3]]) #only penalize rotation until learning works well [action[0],action[1],action[3]]) - actionCost = np.linalg.norm(npaction)*10. + npaction = np.array([ + action[3] + ]) #only penalize rotation until learning works well [action[0],action[1],action[3]]) + actionCost = np.linalg.norm(npaction) * 10. #print("actionCost") #print(actionCost) - reward = self._reward()-actionCost + reward = self._reward() - actionCost #print("reward") #print(reward) @@ -187,22 +190,23 @@ class KukaGymEnv(gym.Env): if mode != "rgb_array": return np.array([]) - base_pos,orn = self._p.getBasePositionAndOrientation(self._kuka.kukaUid) - view_matrix = self._p.computeViewMatrixFromYawPitchRoll( - cameraTargetPosition=base_pos, - distance=self._cam_dist, - yaw=self._cam_yaw, - pitch=self._cam_pitch, - roll=0, - upAxisIndex=2) - proj_matrix = self._p.computeProjectionMatrixFOV( - fov=60, aspect=float(RENDER_WIDTH)/RENDER_HEIGHT, - nearVal=0.1, farVal=100.0) - (_, _, px, _, _) = self._p.getCameraImage( - width=RENDER_WIDTH, height=RENDER_HEIGHT, viewMatrix=view_matrix, - projectionMatrix=proj_matrix, renderer=self._p.ER_BULLET_HARDWARE_OPENGL) - #renderer=self._p.ER_TINY_RENDERER) - + base_pos, orn = self._p.getBasePositionAndOrientation(self._kuka.kukaUid) + view_matrix = self._p.computeViewMatrixFromYawPitchRoll(cameraTargetPosition=base_pos, + distance=self._cam_dist, + yaw=self._cam_yaw, + pitch=self._cam_pitch, + roll=0, + upAxisIndex=2) + proj_matrix = self._p.computeProjectionMatrixFOV(fov=60, + aspect=float(RENDER_WIDTH) / RENDER_HEIGHT, + nearVal=0.1, + farVal=100.0) + (_, _, px, _, _) = self._p.getCameraImage(width=RENDER_WIDTH, + height=RENDER_HEIGHT, + viewMatrix=view_matrix, + projectionMatrix=proj_matrix, + renderer=self._p.ER_BULLET_HARDWARE_OPENGL) + #renderer=self._p.ER_TINY_RENDERER) rgb_array = np.array(px, dtype=np.uint8) rgb_array = np.reshape(rgb_array, (RENDER_HEIGHT, RENDER_WIDTH, 4)) @@ -210,49 +214,47 @@ class KukaGymEnv(gym.Env): rgb_array = rgb_array[:, :, :3] return rgb_array - def _termination(self): #print (self._kuka.endEffectorPos[2]) - state = p.getLinkState(self._kuka.kukaUid,self._kuka.kukaEndEffectorIndex) + state = p.getLinkState(self._kuka.kukaUid, self._kuka.kukaEndEffectorIndex) actualEndEffectorPos = state[0] #print("self._envStepCounter") #print(self._envStepCounter) - if (self.terminated or self._envStepCounter>self._maxSteps): + if (self.terminated or self._envStepCounter > self._maxSteps): self._observation = self.getExtendedObservation() return True maxDist = 0.005 - closestPoints = p.getClosestPoints(self._kuka.trayUid, self._kuka.kukaUid,maxDist) + closestPoints = p.getClosestPoints(self._kuka.trayUid, self._kuka.kukaUid, maxDist) - if (len(closestPoints)):#(actualEndEffectorPos[2] <= -0.43): + if (len(closestPoints)): #(actualEndEffectorPos[2] <= -0.43): self.terminated = 1 #print("terminating, closing gripper, attempting grasp") #start grasp and terminate fingerAngle = 0.3 - for i in range (100): - graspAction = [0,0,0.0001,0,fingerAngle] + for i in range(100): + graspAction = [0, 0, 0.0001, 0, fingerAngle] self._kuka.applyAction(graspAction) p.stepSimulation() - fingerAngle = fingerAngle-(0.3/100.) - if (fingerAngle<0): - fingerAngle=0 + fingerAngle = fingerAngle - (0.3 / 100.) + if (fingerAngle < 0): + fingerAngle = 0 - for i in range (1000): - graspAction = [0,0,0.001,0,fingerAngle] + for i in range(1000): + graspAction = [0, 0, 0.001, 0, fingerAngle] self._kuka.applyAction(graspAction) p.stepSimulation() - blockPos,blockOrn=p.getBasePositionAndOrientation(self.blockUid) + blockPos, blockOrn = p.getBasePositionAndOrientation(self.blockUid) if (blockPos[2] > 0.23): #print("BLOCKPOS!") #print(blockPos[2]) break - state = p.getLinkState(self._kuka.kukaUid,self._kuka.kukaEndEffectorIndex) + state = p.getLinkState(self._kuka.kukaUid, self._kuka.kukaEndEffectorIndex) actualEndEffectorPos = state[0] - if (actualEndEffectorPos[2]>0.5): + if (actualEndEffectorPos[2] > 0.5): break - self._observation = self.getExtendedObservation() return True return False @@ -260,18 +262,19 @@ class KukaGymEnv(gym.Env): def _reward(self): #rewards is height of target object - blockPos,blockOrn=p.getBasePositionAndOrientation(self.blockUid) - closestPoints = p.getClosestPoints(self.blockUid,self._kuka.kukaUid,1000, -1, self._kuka.kukaEndEffectorIndex) + blockPos, blockOrn = p.getBasePositionAndOrientation(self.blockUid) + closestPoints = p.getClosestPoints(self.blockUid, self._kuka.kukaUid, 1000, -1, + self._kuka.kukaEndEffectorIndex) reward = -1000 numPt = len(closestPoints) #print(numPt) - if (numPt>0): + if (numPt > 0): #print("reward:") - reward = -closestPoints[0][8]*10 - if (blockPos[2] >0.2): - reward = reward+10000 + reward = -closestPoints[0][8] * 10 + if (blockPos[2] > 0.2): + reward = reward + 10000 print("successfully grasped a block!!!") #print("self._envStepCounter") #print(self._envStepCounter) @@ -279,7 +282,7 @@ class KukaGymEnv(gym.Env): #print(self._envStepCounter) #print("reward") #print(reward) - #print("reward") + #print("reward") #print(reward) return reward diff --git a/examples/pybullet/gym/pybullet_envs/bullet/kuka_diverse_object_gym_env.py b/examples/pybullet/gym/pybullet_envs/bullet/kuka_diverse_object_gym_env.py index c2c4d2acc..696dc268a 100644 --- a/examples/pybullet/gym/pybullet_envs/bullet/kuka_diverse_object_gym_env.py +++ b/examples/pybullet/gym/pybullet_envs/bullet/kuka_diverse_object_gym_env.py @@ -13,6 +13,7 @@ import glob from pkg_resources import parse_version import gym + class KukaDiverseObjectEnv(KukaGymEnv): """Class for Kuka environment with diverse objects. @@ -61,7 +62,7 @@ class KukaDiverseObjectEnv(KukaGymEnv): """ self._isDiscrete = isDiscrete - self._timeStep = 1./240. + self._timeStep = 1. / 240. self._urdfRoot = urdfRoot self._actionRepeat = actionRepeat self._isEnableSelfCollision = isEnableSelfCollision @@ -85,9 +86,9 @@ class KukaDiverseObjectEnv(KukaGymEnv): if self._renders: self.cid = p.connect(p.SHARED_MEMORY) - if (self.cid<0): - self.cid = p.connect(p.GUI) - p.resetDebugVisualizerCamera(1.3,180,-41,[0.52,-0.2,-0.33]) + if (self.cid < 0): + self.cid = p.connect(p.GUI) + p.resetDebugVisualizerCamera(1.3, 180, -41, [0.52, -0.2, -0.33]) else: self.cid = p.connect(p.DIRECT) self.seed() @@ -100,9 +101,7 @@ class KukaDiverseObjectEnv(KukaGymEnv): else: self.action_space = spaces.Box(low=-1, high=1, shape=(3,)) # dx, dy, da if self._removeHeightHack: - self.action_space = spaces.Box(low=-1, - high=1, - shape=(4,)) # dx, dy, dz, da + self.action_space = spaces.Box(low=-1, high=1, shape=(4,)) # dx, dy, dz, da self.viewer = None def reset(self): @@ -111,17 +110,15 @@ class KukaDiverseObjectEnv(KukaGymEnv): # Set the camera settings. look = [0.23, 0.2, 0.54] distance = 1. - pitch = -56 + self._cameraRandom*np.random.uniform(-3, 3) - yaw = 245 + self._cameraRandom*np.random.uniform(-3, 3) + pitch = -56 + self._cameraRandom * np.random.uniform(-3, 3) + yaw = 245 + self._cameraRandom * np.random.uniform(-3, 3) roll = 0 - self._view_matrix = p.computeViewMatrixFromYawPitchRoll( - look, distance, yaw, pitch, roll, 2) - fov = 20. + self._cameraRandom*np.random.uniform(-2, 2) + self._view_matrix = p.computeViewMatrixFromYawPitchRoll(look, distance, yaw, pitch, roll, 2) + fov = 20. + self._cameraRandom * np.random.uniform(-2, 2) aspect = self._width / self._height near = 0.01 far = 10 - self._proj_matrix = p.computeProjectionMatrixFOV( - fov, aspect, near, far) + self._proj_matrix = p.computeProjectionMatrixFOV(fov, aspect, near, far) self._attempted_grasp = False self._env_step = 0 @@ -130,18 +127,18 @@ class KukaDiverseObjectEnv(KukaGymEnv): p.resetSimulation() p.setPhysicsEngineParameter(numSolverIterations=150) p.setTimeStep(self._timeStep) - p.loadURDF(os.path.join(self._urdfRoot,"plane.urdf"),[0,0,-1]) + p.loadURDF(os.path.join(self._urdfRoot, "plane.urdf"), [0, 0, -1]) - p.loadURDF(os.path.join(self._urdfRoot,"table/table.urdf"), 0.5000000,0.00000,-.820000,0.000000,0.000000,0.0,1.0) + p.loadURDF(os.path.join(self._urdfRoot, "table/table.urdf"), 0.5000000, 0.00000, -.820000, + 0.000000, 0.000000, 0.0, 1.0) - p.setGravity(0,0,-10) + p.setGravity(0, 0, -10) self._kuka = kuka.Kuka(urdfRootPath=self._urdfRoot, timeStep=self._timeStep) self._envStepCounter = 0 p.stepSimulation() # Choose the objects in the bin. - urdfList = self._get_random_object( - self._numObjects, self._isTest) + urdfList = self._get_random_object(self._numObjects, self._isTest) self._objectUids = self._randomly_place_objects(urdfList) self._observation = self._get_observation() return np.array(self._observation) @@ -156,17 +153,15 @@ class KukaDiverseObjectEnv(KukaGymEnv): The list of object unique ID's. """ - # Randomize positions of each object urdf. objectUids = [] for urdf_name in urdfList: - xpos = 0.4 +self._blockRandom*random.random() - ypos = self._blockRandom*(random.random()-.5) - angle = np.pi/2 + self._blockRandom * np.pi * random.random() + xpos = 0.4 + self._blockRandom * random.random() + ypos = self._blockRandom * (random.random() - .5) + angle = np.pi / 2 + self._blockRandom * np.pi * random.random() orn = p.getQuaternionFromEuler([0, 0, angle]) urdf_path = os.path.join(self._urdfRoot, urdf_name) - uid = p.loadURDF(urdf_path, [xpos, ypos, .15], - [orn[0], orn[1], orn[2], orn[3]]) + uid = p.loadURDF(urdf_path, [xpos, ypos, .15], [orn[0], orn[1], orn[2], orn[3]]) objectUids.append(uid) # Let each object fall to the tray individual, to prevent object # intersection. @@ -178,9 +173,9 @@ class KukaDiverseObjectEnv(KukaGymEnv): """Return the observation as an image. """ img_arr = p.getCameraImage(width=self._width, - height=self._height, - viewMatrix=self._view_matrix, - projectionMatrix=self._proj_matrix) + height=self._height, + viewMatrix=self._view_matrix, + projectionMatrix=self._proj_matrix) rgb = img_arr[2] np_img_arr = np.reshape(rgb, (self._height, self._width, 4)) return np_img_arr[:, :, :3] @@ -246,8 +241,7 @@ class KukaDiverseObjectEnv(KukaGymEnv): break # If we are close to the bin, attempt grasp. - state = p.getLinkState(self._kuka.kukaUid, - self._kuka.kukaEndEffectorIndex) + state = p.getLinkState(self._kuka.kukaUid, self._kuka.kukaEndEffectorIndex) end_effector_pos = state[0] if end_effector_pos[2] <= 0.1: finger_angle = 0.3 @@ -257,7 +251,7 @@ class KukaDiverseObjectEnv(KukaGymEnv): p.stepSimulation() #if self._renders: # time.sleep(self._timeStep) - finger_angle -= 0.3/100. + finger_angle -= 0.3 / 100. if finger_angle < 0: finger_angle = 0 for _ in range(500): @@ -266,7 +260,7 @@ class KukaDiverseObjectEnv(KukaGymEnv): p.stepSimulation() if self._renders: time.sleep(self._timeStep) - finger_angle -= 0.3/100. + finger_angle -= 0.3 / 100. if finger_angle < 0: finger_angle = 0 self._attempted_grasp = True @@ -274,9 +268,7 @@ class KukaDiverseObjectEnv(KukaGymEnv): done = self._termination() reward = self._reward() - debug = { - 'grasp_success': self._graspSuccess - } + debug = {'grasp_success': self._graspSuccess} return observation, reward, done, debug def _reward(self): @@ -288,8 +280,7 @@ class KukaDiverseObjectEnv(KukaGymEnv): reward = 0 self._graspSuccess = 0 for uid in self._objectUids: - pos, _ = p.getBasePositionAndOrientation( - uid) + pos, _ = p.getBasePositionAndOrientation(uid) # If any block is above height, provide reward. if pos[2] > 0.2: self._graspSuccess += 1 @@ -319,8 +310,7 @@ class KukaDiverseObjectEnv(KukaGymEnv): urdf_pattern = os.path.join(self._urdfRoot, 'random_urdfs/*[1-9]/*.urdf') found_object_directories = glob.glob(urdf_pattern) total_num_objects = len(found_object_directories) - selected_objects = np.random.choice(np.arange(total_num_objects), - num_objects) + selected_objects = np.random.choice(np.arange(total_num_objects), num_objects) selected_objects_filenames = [] for object_index in selected_objects: selected_objects_filenames += [found_object_directories[object_index]] diff --git a/examples/pybullet/gym/pybullet_envs/bullet/minitaur.py b/examples/pybullet/gym/pybullet_envs/bullet/minitaur.py index 046370040..3f1c50a1f 100644 --- a/examples/pybullet/gym/pybullet_envs/bullet/minitaur.py +++ b/examples/pybullet/gym/pybullet_envs/bullet/minitaur.py @@ -15,9 +15,8 @@ OVERHEAT_SHUTDOWN_TORQUE = 2.45 OVERHEAT_SHUTDOWN_TIME = 1.0 LEG_POSITION = ["front_left", "back_left", "front_right", "back_right"] MOTOR_NAMES = [ - "motor_front_leftL_joint", "motor_front_leftR_joint", - "motor_back_leftL_joint", "motor_back_leftR_joint", - "motor_front_rightL_joint", "motor_front_rightR_joint", + "motor_front_leftL_joint", "motor_front_leftR_joint", "motor_back_leftL_joint", + "motor_back_leftR_joint", "motor_front_rightL_joint", "motor_front_rightR_joint", "motor_back_rightL_joint", "motor_back_rightR_joint" ] LEG_LINK_ID = [2, 3, 5, 6, 8, 9, 11, 12, 15, 16, 18, 19, 21, 22, 24, 25] @@ -33,7 +32,7 @@ class Minitaur(object): def __init__(self, pybullet_client, - urdf_root= os.path.join(os.path.dirname(__file__),"../data"), + urdf_root=os.path.join(os.path.dirname(__file__), "../data"), time_step=0.01, self_collision_enabled=False, motor_velocity_limit=np.inf, @@ -87,10 +86,9 @@ class Minitaur(object): if self._accurate_motor_model_enabled: self._kp = motor_kp self._kd = motor_kd - self._motor_model = motor.MotorModel( - torque_control_enabled=self._torque_control_enabled, - kp=self._kp, - kd=self._kd) + self._motor_model = motor.MotorModel(torque_control_enabled=self._torque_control_enabled, + kp=self._kp, + kd=self._kd) elif self._pd_control_enabled: self._kp = 8 self._kd = kd_for_pd_controllers @@ -101,15 +99,12 @@ class Minitaur(object): self.Reset() def _RecordMassInfoFromURDF(self): - self._base_mass_urdf = self._pybullet_client.getDynamicsInfo( - self.quadruped, BASE_LINK_ID)[0] + self._base_mass_urdf = self._pybullet_client.getDynamicsInfo(self.quadruped, BASE_LINK_ID)[0] self._leg_masses_urdf = [] self._leg_masses_urdf.append( - self._pybullet_client.getDynamicsInfo(self.quadruped, LEG_LINK_ID[0])[ - 0]) + self._pybullet_client.getDynamicsInfo(self.quadruped, LEG_LINK_ID[0])[0]) self._leg_masses_urdf.append( - self._pybullet_client.getDynamicsInfo(self.quadruped, MOTOR_LINK_ID[0])[ - 0]) + self._pybullet_client.getDynamicsInfo(self.quadruped, MOTOR_LINK_ID[0])[0]) def _BuildJointNameToIdDict(self): num_joints = self._pybullet_client.getNumJoints(self.quadruped) @@ -119,9 +114,7 @@ class Minitaur(object): self._joint_name_to_id[joint_info[1].decode("UTF-8")] = joint_info[0] def _BuildMotorIdList(self): - self._motor_id_list = [ - self._joint_name_to_id[motor_name] for motor_name in MOTOR_NAMES - ] + self._motor_id_list = [self._joint_name_to_id[motor_name] for motor_name in MOTOR_NAMES] def Reset(self, reload_urdf=True): """Reset the minitaur to its initial states. @@ -144,39 +137,35 @@ class Minitaur(object): self._RecordMassInfoFromURDF() self.ResetPose(add_constraint=True) if self._on_rack: - self._pybullet_client.createConstraint( - self.quadruped, -1, -1, -1, self._pybullet_client.JOINT_FIXED, - [0, 0, 0], [0, 0, 0], [0, 0, 1]) + self._pybullet_client.createConstraint(self.quadruped, -1, -1, -1, + self._pybullet_client.JOINT_FIXED, [0, 0, 0], + [0, 0, 0], [0, 0, 1]) else: - self._pybullet_client.resetBasePositionAndOrientation( - self.quadruped, INIT_POSITION, INIT_ORIENTATION) - self._pybullet_client.resetBaseVelocity(self.quadruped, [0, 0, 0], - [0, 0, 0]) + self._pybullet_client.resetBasePositionAndOrientation(self.quadruped, INIT_POSITION, + INIT_ORIENTATION) + self._pybullet_client.resetBaseVelocity(self.quadruped, [0, 0, 0], [0, 0, 0]) self.ResetPose(add_constraint=False) self._overheat_counter = np.zeros(self.num_motors) self._motor_enabled_list = [True] * self.num_motors def _SetMotorTorqueById(self, motor_id, torque): - self._pybullet_client.setJointMotorControl2( - bodyIndex=self.quadruped, - jointIndex=motor_id, - controlMode=self._pybullet_client.TORQUE_CONTROL, - force=torque) + self._pybullet_client.setJointMotorControl2(bodyIndex=self.quadruped, + jointIndex=motor_id, + controlMode=self._pybullet_client.TORQUE_CONTROL, + force=torque) def _SetDesiredMotorAngleById(self, motor_id, desired_angle): - self._pybullet_client.setJointMotorControl2( - bodyIndex=self.quadruped, - jointIndex=motor_id, - controlMode=self._pybullet_client.POSITION_CONTROL, - targetPosition=desired_angle, - positionGain=self._kp, - velocityGain=self._kd, - force=self._max_force) + self._pybullet_client.setJointMotorControl2(bodyIndex=self.quadruped, + jointIndex=motor_id, + controlMode=self._pybullet_client.POSITION_CONTROL, + targetPosition=desired_angle, + positionGain=self._kp, + velocityGain=self._kd, + force=self._max_force) def _SetDesiredMotorAngleByName(self, motor_name, desired_angle): - self._SetDesiredMotorAngleById(self._joint_name_to_id[motor_name], - desired_angle) + self._SetDesiredMotorAngleById(self._joint_name_to_id[motor_name], desired_angle) def ResetPose(self, add_constraint): """Reset the pose of the minitaur. @@ -200,59 +189,53 @@ class Minitaur(object): knee_angle = -2.1834 leg_position = LEG_POSITION[leg_id] - self._pybullet_client.resetJointState( - self.quadruped, - self._joint_name_to_id["motor_" + leg_position + "L_joint"], - self._motor_direction[2 * leg_id] * half_pi, - targetVelocity=0) - self._pybullet_client.resetJointState( - self.quadruped, - self._joint_name_to_id["knee_" + leg_position + "L_link"], - self._motor_direction[2 * leg_id] * knee_angle, - targetVelocity=0) - self._pybullet_client.resetJointState( - self.quadruped, - self._joint_name_to_id["motor_" + leg_position + "R_joint"], - self._motor_direction[2 * leg_id + 1] * half_pi, - targetVelocity=0) - self._pybullet_client.resetJointState( - self.quadruped, - self._joint_name_to_id["knee_" + leg_position + "R_link"], - self._motor_direction[2 * leg_id + 1] * knee_angle, - targetVelocity=0) + self._pybullet_client.resetJointState(self.quadruped, + self._joint_name_to_id["motor_" + leg_position + + "L_joint"], + self._motor_direction[2 * leg_id] * half_pi, + targetVelocity=0) + self._pybullet_client.resetJointState(self.quadruped, + self._joint_name_to_id["knee_" + leg_position + + "L_link"], + self._motor_direction[2 * leg_id] * knee_angle, + targetVelocity=0) + self._pybullet_client.resetJointState(self.quadruped, + self._joint_name_to_id["motor_" + leg_position + + "R_joint"], + self._motor_direction[2 * leg_id + 1] * half_pi, + targetVelocity=0) + self._pybullet_client.resetJointState(self.quadruped, + self._joint_name_to_id["knee_" + leg_position + + "R_link"], + self._motor_direction[2 * leg_id + 1] * knee_angle, + targetVelocity=0) if add_constraint: self._pybullet_client.createConstraint( - self.quadruped, self._joint_name_to_id["knee_" - + leg_position + "R_link"], - self.quadruped, self._joint_name_to_id["knee_" - + leg_position + "L_link"], - self._pybullet_client.JOINT_POINT2POINT, [0, 0, 0], - KNEE_CONSTRAINT_POINT_RIGHT, KNEE_CONSTRAINT_POINT_LEFT) + self.quadruped, self._joint_name_to_id["knee_" + leg_position + "R_link"], + self.quadruped, self._joint_name_to_id["knee_" + leg_position + "L_link"], + self._pybullet_client.JOINT_POINT2POINT, [0, 0, 0], KNEE_CONSTRAINT_POINT_RIGHT, + KNEE_CONSTRAINT_POINT_LEFT) if self._accurate_motor_model_enabled or self._pd_control_enabled: # Disable the default motor in pybullet. self._pybullet_client.setJointMotorControl2( bodyIndex=self.quadruped, - jointIndex=(self._joint_name_to_id["motor_" - + leg_position + "L_joint"]), + jointIndex=(self._joint_name_to_id["motor_" + leg_position + "L_joint"]), controlMode=self._pybullet_client.VELOCITY_CONTROL, targetVelocity=0, force=knee_friction_force) self._pybullet_client.setJointMotorControl2( bodyIndex=self.quadruped, - jointIndex=(self._joint_name_to_id["motor_" - + leg_position + "R_joint"]), + jointIndex=(self._joint_name_to_id["motor_" + leg_position + "R_joint"]), controlMode=self._pybullet_client.VELOCITY_CONTROL, targetVelocity=0, force=knee_friction_force) else: - self._SetDesiredMotorAngleByName( - "motor_" + leg_position + "L_joint", - self._motor_direction[2 * leg_id] * half_pi) + self._SetDesiredMotorAngleByName("motor_" + leg_position + "L_joint", + self._motor_direction[2 * leg_id] * half_pi) self._SetDesiredMotorAngleByName("motor_" + leg_position + "R_joint", - self._motor_direction[2 * leg_id - + 1] * half_pi) + self._motor_direction[2 * leg_id + 1] * half_pi) self._pybullet_client.setJointMotorControl2( bodyIndex=self.quadruped, @@ -273,8 +256,7 @@ class Minitaur(object): Returns: The position of minitaur's base. """ - position, _ = ( - self._pybullet_client.getBasePositionAndOrientation(self.quadruped)) + position, _ = (self._pybullet_client.getBasePositionAndOrientation(self.quadruped)) return position def GetBaseOrientation(self): @@ -283,8 +265,7 @@ class Minitaur(object): Returns: The orientation of minitaur's base. """ - _, orientation = ( - self._pybullet_client.getBasePositionAndOrientation(self.quadruped)) + _, orientation = (self._pybullet_client.getBasePositionAndOrientation(self.quadruped)) return orientation def GetActionDimension(self): @@ -304,10 +285,9 @@ class Minitaur(object): """ upper_bound = np.array([0.0] * self.GetObservationDimension()) upper_bound[0:self.num_motors] = math.pi # Joint angle. - upper_bound[self.num_motors:2 * self.num_motors] = ( - motor.MOTOR_SPEED_LIMIT) # Joint velocity. - upper_bound[2 * self.num_motors:3 * self.num_motors] = ( - motor.OBSERVED_TORQUE_LIMIT) # Joint torque. + upper_bound[self.num_motors:2 * self.num_motors] = (motor.MOTOR_SPEED_LIMIT) # Joint velocity. + upper_bound[2 * self.num_motors:3 * self.num_motors] = (motor.OBSERVED_TORQUE_LIMIT + ) # Joint torque. upper_bound[3 * self.num_motors:] = 1.0 # Quaternion of base orientation. return upper_bound @@ -354,12 +334,9 @@ class Minitaur(object): """ if self._motor_velocity_limit < np.inf: current_motor_angle = self.GetMotorAngles() - motor_commands_max = ( - current_motor_angle + self.time_step * self._motor_velocity_limit) - motor_commands_min = ( - current_motor_angle - self.time_step * self._motor_velocity_limit) - motor_commands = np.clip(motor_commands, motor_commands_min, - motor_commands_max) + motor_commands_max = (current_motor_angle + self.time_step * self._motor_velocity_limit) + motor_commands_min = (current_motor_angle - self.time_step * self._motor_velocity_limit) + motor_commands = np.clip(motor_commands, motor_commands_min, motor_commands_max) if self._accurate_motor_model_enabled or self._pd_control_enabled: q = self.GetMotorAngles() @@ -373,8 +350,7 @@ class Minitaur(object): self._overheat_counter[i] += 1 else: self._overheat_counter[i] = 0 - if (self._overheat_counter[i] > - OVERHEAT_SHUTDOWN_TIME / self.time_step): + if (self._overheat_counter[i] > OVERHEAT_SHUTDOWN_TIME / self.time_step): self._motor_enabled_list[i] = False # The torque is already in the observation space because we use @@ -382,12 +358,11 @@ class Minitaur(object): self._observed_motor_torques = observed_torque # Transform into the motor space when applying the torque. - self._applied_motor_torque = np.multiply(actual_torque, - self._motor_direction) + self._applied_motor_torque = np.multiply(actual_torque, self._motor_direction) - for motor_id, motor_torque, motor_enabled in zip( - self._motor_id_list, self._applied_motor_torque, - self._motor_enabled_list): + for motor_id, motor_torque, motor_enabled in zip(self._motor_id_list, + self._applied_motor_torque, + self._motor_enabled_list): if motor_enabled: self._SetMotorTorqueById(motor_id, motor_torque) else: @@ -403,14 +378,12 @@ class Minitaur(object): self._applied_motor_torques = np.multiply(self._observed_motor_torques, self._motor_direction) - for motor_id, motor_torque in zip(self._motor_id_list, - self._applied_motor_torques): + for motor_id, motor_torque in zip(self._motor_id_list, self._applied_motor_torques): self._SetMotorTorqueById(motor_id, motor_torque) else: - motor_commands_with_direction = np.multiply(motor_commands, - self._motor_direction) - for motor_id, motor_command_with_direction in zip( - self._motor_id_list, motor_commands_with_direction): + motor_commands_with_direction = np.multiply(motor_commands, self._motor_direction) + for motor_id, motor_command_with_direction in zip(self._motor_id_list, + motor_commands_with_direction): self._SetDesiredMotorAngleById(motor_id, motor_command_with_direction) def GetMotorAngles(self): @@ -471,13 +444,13 @@ class Minitaur(object): quater_pi = math.pi / 4 for i in range(self.num_motors): action_idx = i // 2 - forward_backward_component = (-scale_for_singularity * quater_pi * ( - actions[action_idx + half_num_motors] + offset_for_singularity)) + forward_backward_component = ( + -scale_for_singularity * quater_pi * + (actions[action_idx + half_num_motors] + offset_for_singularity)) extension_component = (-1)**i * quater_pi * actions[action_idx] if i >= half_num_motors: extension_component = -extension_component - motor_angle[i] = ( - math.pi + forward_backward_component + extension_component) + motor_angle[i] = (math.pi + forward_backward_component + extension_component) return motor_angle def GetBaseMassFromURDF(self): @@ -489,8 +462,7 @@ class Minitaur(object): return self._leg_masses_urdf def SetBaseMass(self, base_mass): - self._pybullet_client.changeDynamics( - self.quadruped, BASE_LINK_ID, mass=base_mass) + self._pybullet_client.changeDynamics(self.quadruped, BASE_LINK_ID, mass=base_mass) def SetLegMasses(self, leg_masses): """Set the mass of the legs. @@ -504,11 +476,9 @@ class Minitaur(object): leg_masses[1] is the mass of the motor. """ for link_id in LEG_LINK_ID: - self._pybullet_client.changeDynamics( - self.quadruped, link_id, mass=leg_masses[0]) + self._pybullet_client.changeDynamics(self.quadruped, link_id, mass=leg_masses[0]) for link_id in MOTOR_LINK_ID: - self._pybullet_client.changeDynamics( - self.quadruped, link_id, mass=leg_masses[1]) + self._pybullet_client.changeDynamics(self.quadruped, link_id, mass=leg_masses[1]) def SetFootFriction(self, foot_friction): """Set the lateral friction of the feet. @@ -518,8 +488,7 @@ class Minitaur(object): shared by all four feet. """ for link_id in FOOT_LINK_ID: - self._pybullet_client.changeDynamics( - self.quadruped, link_id, lateralFriction=foot_friction) + self._pybullet_client.changeDynamics(self.quadruped, link_id, lateralFriction=foot_friction) def SetBatteryVoltage(self, voltage): if self._accurate_motor_model_enabled: diff --git a/examples/pybullet/gym/pybullet_envs/bullet/minitaur_duck_gym_env.py b/examples/pybullet/gym/pybullet_envs/bullet/minitaur_duck_gym_env.py index d2fb0c859..88d605f11 100644 --- a/examples/pybullet/gym/pybullet_envs/bullet/minitaur_duck_gym_env.py +++ b/examples/pybullet/gym/pybullet_envs/bullet/minitaur_duck_gym_env.py @@ -6,8 +6,7 @@ import os import inspect currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe()))) parentdir = os.path.dirname(os.path.dirname(currentdir)) -os.sys.path.insert(0,parentdir) - +os.sys.path.insert(0, parentdir) import math import time @@ -34,8 +33,9 @@ OBSERVATION_EPS = 0.01 RENDER_HEIGHT = 720 RENDER_WIDTH = 960 -duckStartPos = [0,0,0.25] -duckStartOrn = [0.5,0.5,0.5,0.5] +duckStartPos = [0, 0, 0.25] +duckStartOrn = [0.5, 0.5, 0.5, 0.5] + class MinitaurBulletDuckEnv(gym.Env): """The gym environment for the minitaur. @@ -47,34 +47,32 @@ class MinitaurBulletDuckEnv(gym.Env): expenditure. """ - metadata = { - "render.modes": ["human", "rgb_array"], - "video.frames_per_second": 50 - } - - def __init__(self, - urdf_root=pybullet_data.getDataPath(), - action_repeat=1, - distance_weight=1.0, - energy_weight=0.005, - shake_weight=0.0, - drift_weight=0.0, - distance_limit=float("inf"), - observation_noise_stdev=0.0, - self_collision_enabled=True, - motor_velocity_limit=np.inf, - pd_control_enabled=False,#not needed to be true if accurate motor model is enabled (has its own better PD) - leg_model_enabled=True, - accurate_motor_model_enabled=True, - motor_kp=1.0, - motor_kd=0.02, - torque_control_enabled=False, - motor_overheat_protection=True, - hard_reset=True, - on_rack=False, - render=False, - kd_for_pd_controllers=0.3, - env_randomizer=minitaur_env_randomizer.MinitaurEnvRandomizer()): + metadata = {"render.modes": ["human", "rgb_array"], "video.frames_per_second": 50} + + def __init__( + self, + urdf_root=pybullet_data.getDataPath(), + action_repeat=1, + distance_weight=1.0, + energy_weight=0.005, + shake_weight=0.0, + drift_weight=0.0, + distance_limit=float("inf"), + observation_noise_stdev=0.0, + self_collision_enabled=True, + motor_velocity_limit=np.inf, + pd_control_enabled=False, #not needed to be true if accurate motor model is enabled (has its own better PD) + leg_model_enabled=True, + accurate_motor_model_enabled=True, + motor_kp=1.0, + motor_kd=0.02, + torque_control_enabled=False, + motor_overheat_protection=True, + hard_reset=True, + on_rack=False, + render=False, + kd_for_pd_controllers=0.3, + env_randomizer=minitaur_env_randomizer.MinitaurEnvRandomizer()): """Initialize the minitaur gym environment. Args: @@ -152,17 +150,14 @@ class MinitaurBulletDuckEnv(gym.Env): self._action_repeat *= NUM_SUBSTEPS if self._is_render: - self._pybullet_client = bullet_client.BulletClient( - connection_mode=pybullet.GUI) + self._pybullet_client = bullet_client.BulletClient(connection_mode=pybullet.GUI) else: self._pybullet_client = bullet_client.BulletClient() self.seed() self.reset() - observation_high = ( - self.minitaur.GetObservationUpperBound() + OBSERVATION_EPS) - observation_low = ( - self.minitaur.GetObservationLowerBound() - OBSERVATION_EPS) + observation_high = (self.minitaur.GetObservationUpperBound() + OBSERVATION_EPS) + observation_low = (self.minitaur.GetObservationLowerBound() - OBSERVATION_EPS) action_dim = 8 action_high = np.array([self._action_bound] * action_dim) self.action_space = spaces.Box(-action_high, action_high, dtype=np.float32) @@ -183,35 +178,36 @@ class MinitaurBulletDuckEnv(gym.Env): numSolverIterations=int(self._num_bullet_solver_iterations)) self._pybullet_client.setTimeStep(self._time_step) self._groundId = self._pybullet_client.loadURDF("%s/plane.urdf" % self._urdf_root) - self._duckId = self._pybullet_client.loadURDF("%s/duck_vhacd.urdf" % self._urdf_root,duckStartPos,duckStartOrn) + self._duckId = self._pybullet_client.loadURDF("%s/duck_vhacd.urdf" % self._urdf_root, + duckStartPos, duckStartOrn) self._pybullet_client.setGravity(0, 0, -10) acc_motor = self._accurate_motor_model_enabled motor_protect = self._motor_overheat_protection - self.minitaur = (minitaur.Minitaur( - pybullet_client=self._pybullet_client, - urdf_root=self._urdf_root, - time_step=self._time_step, - self_collision_enabled=self._self_collision_enabled, - motor_velocity_limit=self._motor_velocity_limit, - pd_control_enabled=self._pd_control_enabled, - accurate_motor_model_enabled=acc_motor, - motor_kp=self._motor_kp, - motor_kd=self._motor_kd, - torque_control_enabled=self._torque_control_enabled, - motor_overheat_protection=motor_protect, - on_rack=self._on_rack, - kd_for_pd_controllers=self._kd_for_pd_controllers)) + self.minitaur = (minitaur.Minitaur(pybullet_client=self._pybullet_client, + urdf_root=self._urdf_root, + time_step=self._time_step, + self_collision_enabled=self._self_collision_enabled, + motor_velocity_limit=self._motor_velocity_limit, + pd_control_enabled=self._pd_control_enabled, + accurate_motor_model_enabled=acc_motor, + motor_kp=self._motor_kp, + motor_kd=self._motor_kd, + torque_control_enabled=self._torque_control_enabled, + motor_overheat_protection=motor_protect, + on_rack=self._on_rack, + kd_for_pd_controllers=self._kd_for_pd_controllers)) else: self.minitaur.Reset(reload_urdf=False) - self._pybullet_client.resetBasePositionAndOrientation(self._duckId,duckStartPos,duckStartOrn) + self._pybullet_client.resetBasePositionAndOrientation(self._duckId, duckStartPos, + duckStartOrn) if self._env_randomizer is not None: self._env_randomizer.randomize_env(self) self._env_step_counter = 0 self._last_base_position = [0, 0, 0] self._objectives = [] - self._pybullet_client.resetDebugVisualizerCamera( - self._cam_dist, self._cam_yaw, self._cam_pitch, [0, 0, 0]) + self._pybullet_client.resetDebugVisualizerCamera(self._cam_dist, self._cam_yaw, + self._cam_pitch, [0, 0, 0]) if not self._torque_control_enabled: for _ in range(100): if self._pd_control_enabled or self._accurate_motor_model_enabled: @@ -228,8 +224,7 @@ class MinitaurBulletDuckEnv(gym.Env): for i, action_component in enumerate(action): if not (-self._action_bound - ACTION_EPS <= action_component <= self._action_bound + ACTION_EPS): - raise ValueError( - "{}th action {} out of bounds.".format(i, action_component)) + raise ValueError("{}th action {} out of bounds.".format(i, action_component)) action = self.minitaur.ConvertFromLegModel(action) return action @@ -258,8 +253,8 @@ class MinitaurBulletDuckEnv(gym.Env): if time_to_sleep > 0: time.sleep(time_to_sleep) base_pos = self.minitaur.GetBasePosition() - self._pybullet_client.resetDebugVisualizerCamera( - self._cam_dist, self._cam_yaw, self._cam_pitch, base_pos) + self._pybullet_client.resetDebugVisualizerCamera(self._cam_dist, self._cam_yaw, + self._cam_pitch, base_pos) action = self._transform_action_to_motor_command(action) for _ in range(self._action_repeat): self.minitaur.ApplyAction(action) @@ -281,12 +276,17 @@ class MinitaurBulletDuckEnv(gym.Env): pitch=self._cam_pitch, roll=0, upAxisIndex=2) - proj_matrix = self._pybullet_client.computeProjectionMatrixFOV( - fov=60, aspect=float(RENDER_WIDTH)/RENDER_HEIGHT, - nearVal=0.1, farVal=100.0) - (_, _, px, _, _) = self._pybullet_client.getCameraImage( - width=RENDER_WIDTH, height=RENDER_HEIGHT, viewMatrix=view_matrix, - projectionMatrix=proj_matrix, renderer=pybullet.ER_BULLET_HARDWARE_OPENGL) + proj_matrix = self._pybullet_client.computeProjectionMatrixFOV(fov=60, + aspect=float(RENDER_WIDTH) / + RENDER_HEIGHT, + nearVal=0.1, + farVal=100.0) + (_, _, px, _, + _) = self._pybullet_client.getCameraImage(width=RENDER_WIDTH, + height=RENDER_HEIGHT, + viewMatrix=view_matrix, + projectionMatrix=proj_matrix, + renderer=pybullet.ER_BULLET_HARDWARE_OPENGL) rgb_array = np.array(px) rgb_array = rgb_array[:, :, :3] return rgb_array @@ -297,9 +297,8 @@ class MinitaurBulletDuckEnv(gym.Env): Returns: A numpy array of motor angles. """ - return np.array( - self._observation[MOTOR_ANGLE_OBSERVATION_INDEX: - MOTOR_ANGLE_OBSERVATION_INDEX + NUM_MOTORS]) + return np.array(self._observation[MOTOR_ANGLE_OBSERVATION_INDEX:MOTOR_ANGLE_OBSERVATION_INDEX + + NUM_MOTORS]) def get_minitaur_motor_velocities(self): """Get the minitaur's motor velocities. @@ -308,8 +307,8 @@ class MinitaurBulletDuckEnv(gym.Env): A numpy array of motor velocities. """ return np.array( - self._observation[MOTOR_VELOCITY_OBSERVATION_INDEX: - MOTOR_VELOCITY_OBSERVATION_INDEX + NUM_MOTORS]) + self._observation[MOTOR_VELOCITY_OBSERVATION_INDEX:MOTOR_VELOCITY_OBSERVATION_INDEX + + NUM_MOTORS]) def get_minitaur_motor_torques(self): """Get the minitaur's motor torques. @@ -318,8 +317,8 @@ class MinitaurBulletDuckEnv(gym.Env): A numpy array of motor torques. """ return np.array( - self._observation[MOTOR_TORQUE_OBSERVATION_INDEX: - MOTOR_TORQUE_OBSERVATION_INDEX + NUM_MOTORS]) + self._observation[MOTOR_TORQUE_OBSERVATION_INDEX:MOTOR_TORQUE_OBSERVATION_INDEX + + NUM_MOTORS]) def get_minitaur_base_orientation(self): """Get the minitaur's base orientation, represented by a quaternion. @@ -330,8 +329,8 @@ class MinitaurBulletDuckEnv(gym.Env): return np.array(self._observation[BASE_ORIENTATION_OBSERVATION_INDEX:]) def lost_duck(self): - points = self._pybullet_client.getContactPoints(self._duckId, self._groundId); - return len(points)>0 + points = self._pybullet_client.getContactPoints(self._duckId, self._groundId) + return len(points) > 0 def is_fallen(self): """Decide whether the minitaur has fallen. @@ -347,8 +346,7 @@ class MinitaurBulletDuckEnv(gym.Env): rot_mat = self._pybullet_client.getMatrixFromQuaternion(orientation) local_up = rot_mat[6:] pos = self.minitaur.GetBasePosition() - return (np.dot(np.asarray([0, 0, 1]), np.asarray(local_up)) < 0.85 or - pos[2] < 0.13) + return (np.dot(np.asarray([0, 0, 1]), np.asarray(local_up)) < 0.85 or pos[2] < 0.13) def _termination(self): position = self.minitaur.GetBasePosition() @@ -364,12 +362,9 @@ class MinitaurBulletDuckEnv(gym.Env): energy_reward = np.abs( np.dot(self.minitaur.GetMotorTorques(), self.minitaur.GetMotorVelocities())) * self._time_step - reward = ( - self._distance_weight * forward_reward - - self._energy_weight * energy_reward + self._drift_weight * drift_reward - + self._shake_weight * shake_reward) - self._objectives.append( - [forward_reward, energy_reward, drift_reward, shake_reward]) + reward = (self._distance_weight * forward_reward - self._energy_weight * energy_reward + + self._drift_weight * drift_reward + self._shake_weight * shake_reward) + self._objectives.append([forward_reward, energy_reward, drift_reward, shake_reward]) return reward def get_objectives(self): @@ -383,9 +378,9 @@ class MinitaurBulletDuckEnv(gym.Env): self._get_observation() observation = np.array(self._observation) if self._observation_noise_stdev > 0: - observation += (np.random.normal( - scale=self._observation_noise_stdev, size=observation.shape) * - self.minitaur.GetObservationUpperBound()) + observation += ( + np.random.normal(scale=self._observation_noise_stdev, size=observation.shape) * + self.minitaur.GetObservationUpperBound()) return observation if parse_version(gym.__version__) < parse_version('0.9.6'): diff --git a/examples/pybullet/gym/pybullet_envs/bullet/minitaur_env_randomizer.py b/examples/pybullet/gym/pybullet_envs/bullet/minitaur_env_randomizer.py index db139f3b0..cf2646683 100644 --- a/examples/pybullet/gym/pybullet_envs/bullet/minitaur_env_randomizer.py +++ b/examples/pybullet/gym/pybullet_envs/bullet/minitaur_env_randomizer.py @@ -45,24 +45,20 @@ class MinitaurEnvRandomizer(env_randomizer_base.EnvRandomizerBase): minitaur.SetBaseMass(randomized_base_mass) leg_masses = minitaur.GetLegMassesFromURDF() - leg_masses_lower_bound = np.array(leg_masses) * ( - 1.0 + self._minitaur_leg_mass_err_range[0]) - leg_masses_upper_bound = np.array(leg_masses) * ( - 1.0 + self._minitaur_leg_mass_err_range[1]) + leg_masses_lower_bound = np.array(leg_masses) * (1.0 + self._minitaur_leg_mass_err_range[0]) + leg_masses_upper_bound = np.array(leg_masses) * (1.0 + self._minitaur_leg_mass_err_range[1]) randomized_leg_masses = [ np.random.uniform(leg_masses_lower_bound[i], leg_masses_upper_bound[i]) for i in range(len(leg_masses)) ] minitaur.SetLegMasses(randomized_leg_masses) - randomized_battery_voltage = random.uniform(BATTERY_VOLTAGE_RANGE[0], - BATTERY_VOLTAGE_RANGE[1]) + randomized_battery_voltage = random.uniform(BATTERY_VOLTAGE_RANGE[0], BATTERY_VOLTAGE_RANGE[1]) minitaur.SetBatteryVoltage(randomized_battery_voltage) randomized_motor_damping = random.uniform(MOTOR_VISCOUS_DAMPING_RANGE[0], MOTOR_VISCOUS_DAMPING_RANGE[1]) minitaur.SetMotorViscousDamping(randomized_motor_damping) - randomized_foot_friction = random.uniform(MINITAUR_LEG_FRICTION[0], - MINITAUR_LEG_FRICTION[1]) + randomized_foot_friction = random.uniform(MINITAUR_LEG_FRICTION[0], MINITAUR_LEG_FRICTION[1]) minitaur.SetFootFriction(randomized_foot_friction) diff --git a/examples/pybullet/gym/pybullet_envs/bullet/minitaur_gym_env.py b/examples/pybullet/gym/pybullet_envs/bullet/minitaur_gym_env.py index 030840640..7a595b47d 100644 --- a/examples/pybullet/gym/pybullet_envs/bullet/minitaur_gym_env.py +++ b/examples/pybullet/gym/pybullet_envs/bullet/minitaur_gym_env.py @@ -5,8 +5,7 @@ import os, inspect currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe()))) parentdir = os.path.dirname(os.path.dirname(currentdir)) -os.sys.path.insert(0,parentdir) - +os.sys.path.insert(0, parentdir) import math import time @@ -33,6 +32,7 @@ OBSERVATION_EPS = 0.01 RENDER_HEIGHT = 720 RENDER_WIDTH = 960 + class MinitaurBulletEnv(gym.Env): """The gym environment for the minitaur. @@ -43,34 +43,32 @@ class MinitaurBulletEnv(gym.Env): expenditure. """ - metadata = { - "render.modes": ["human", "rgb_array"], - "video.frames_per_second": 50 - } - - def __init__(self, - urdf_root=pybullet_data.getDataPath(), - action_repeat=1, - distance_weight=1.0, - energy_weight=0.005, - shake_weight=0.0, - drift_weight=0.0, - distance_limit=float("inf"), - observation_noise_stdev=0.0, - self_collision_enabled=True, - motor_velocity_limit=np.inf, - pd_control_enabled=False,#not needed to be true if accurate motor model is enabled (has its own better PD) - leg_model_enabled=True, - accurate_motor_model_enabled=True, - motor_kp=1.0, - motor_kd=0.02, - torque_control_enabled=False, - motor_overheat_protection=True, - hard_reset=True, - on_rack=False, - render=False, - kd_for_pd_controllers=0.3, - env_randomizer=minitaur_env_randomizer.MinitaurEnvRandomizer()): + metadata = {"render.modes": ["human", "rgb_array"], "video.frames_per_second": 50} + + def __init__( + self, + urdf_root=pybullet_data.getDataPath(), + action_repeat=1, + distance_weight=1.0, + energy_weight=0.005, + shake_weight=0.0, + drift_weight=0.0, + distance_limit=float("inf"), + observation_noise_stdev=0.0, + self_collision_enabled=True, + motor_velocity_limit=np.inf, + pd_control_enabled=False, #not needed to be true if accurate motor model is enabled (has its own better PD) + leg_model_enabled=True, + accurate_motor_model_enabled=True, + motor_kp=1.0, + motor_kd=0.02, + torque_control_enabled=False, + motor_overheat_protection=True, + hard_reset=True, + on_rack=False, + render=False, + kd_for_pd_controllers=0.3, + env_randomizer=minitaur_env_randomizer.MinitaurEnvRandomizer()): """Initialize the minitaur gym environment. Args: @@ -147,17 +145,14 @@ class MinitaurBulletEnv(gym.Env): self._action_repeat *= NUM_SUBSTEPS if self._is_render: - self._pybullet_client = bullet_client.BulletClient( - connection_mode=pybullet.GUI) + self._pybullet_client = bullet_client.BulletClient(connection_mode=pybullet.GUI) else: self._pybullet_client = bullet_client.BulletClient() self.seed() self.reset() - observation_high = ( - self.minitaur.GetObservationUpperBound() + OBSERVATION_EPS) - observation_low = ( - self.minitaur.GetObservationLowerBound() - OBSERVATION_EPS) + observation_high = (self.minitaur.GetObservationUpperBound() + OBSERVATION_EPS) + observation_low = (self.minitaur.GetObservationLowerBound() - OBSERVATION_EPS) action_dim = 8 action_high = np.array([self._action_bound] * action_dim) self.action_space = spaces.Box(-action_high, action_high, dtype=np.float32) @@ -178,25 +173,25 @@ class MinitaurBulletEnv(gym.Env): numSolverIterations=int(self._num_bullet_solver_iterations)) self._pybullet_client.setTimeStep(self._time_step) plane = self._pybullet_client.loadURDF("%s/plane.urdf" % self._urdf_root) - self._pybullet_client.changeVisualShape(plane,-1,rgbaColor=[1,1,1,0.9]) - self._pybullet_client.configureDebugVisualizer(self._pybullet_client.COV_ENABLE_PLANAR_REFLECTION,0) + self._pybullet_client.changeVisualShape(plane, -1, rgbaColor=[1, 1, 1, 0.9]) + self._pybullet_client.configureDebugVisualizer( + self._pybullet_client.COV_ENABLE_PLANAR_REFLECTION, 0) self._pybullet_client.setGravity(0, 0, -10) acc_motor = self._accurate_motor_model_enabled motor_protect = self._motor_overheat_protection - self.minitaur = (minitaur.Minitaur( - pybullet_client=self._pybullet_client, - urdf_root=self._urdf_root, - time_step=self._time_step, - self_collision_enabled=self._self_collision_enabled, - motor_velocity_limit=self._motor_velocity_limit, - pd_control_enabled=self._pd_control_enabled, - accurate_motor_model_enabled=acc_motor, - motor_kp=self._motor_kp, - motor_kd=self._motor_kd, - torque_control_enabled=self._torque_control_enabled, - motor_overheat_protection=motor_protect, - on_rack=self._on_rack, - kd_for_pd_controllers=self._kd_for_pd_controllers)) + self.minitaur = (minitaur.Minitaur(pybullet_client=self._pybullet_client, + urdf_root=self._urdf_root, + time_step=self._time_step, + self_collision_enabled=self._self_collision_enabled, + motor_velocity_limit=self._motor_velocity_limit, + pd_control_enabled=self._pd_control_enabled, + accurate_motor_model_enabled=acc_motor, + motor_kp=self._motor_kp, + motor_kd=self._motor_kd, + torque_control_enabled=self._torque_control_enabled, + motor_overheat_protection=motor_protect, + on_rack=self._on_rack, + kd_for_pd_controllers=self._kd_for_pd_controllers)) else: self.minitaur.Reset(reload_urdf=False) @@ -206,8 +201,8 @@ class MinitaurBulletEnv(gym.Env): self._env_step_counter = 0 self._last_base_position = [0, 0, 0] self._objectives = [] - self._pybullet_client.resetDebugVisualizerCamera( - self._cam_dist, self._cam_yaw, self._cam_pitch, [0, 0, 0]) + self._pybullet_client.resetDebugVisualizerCamera(self._cam_dist, self._cam_yaw, + self._cam_pitch, [0, 0, 0]) if not self._torque_control_enabled: for _ in range(100): if self._pd_control_enabled or self._accurate_motor_model_enabled: @@ -224,8 +219,7 @@ class MinitaurBulletEnv(gym.Env): for i, action_component in enumerate(action): if not (-self._action_bound - ACTION_EPS <= action_component <= self._action_bound + ACTION_EPS): - raise ValueError( - "{}th action {} out of bounds.".format(i, action_component)) + raise ValueError("{}th action {} out of bounds.".format(i, action_component)) action = self.minitaur.ConvertFromLegModel(action) return action @@ -256,14 +250,15 @@ class MinitaurBulletEnv(gym.Env): base_pos = self.minitaur.GetBasePosition() camInfo = self._pybullet_client.getDebugVisualizerCamera() curTargetPos = camInfo[11] - distance=camInfo[10] + distance = camInfo[10] yaw = camInfo[8] - pitch=camInfo[9] - targetPos = [0.95*curTargetPos[0]+0.05*base_pos[0],0.95*curTargetPos[1]+0.05*base_pos[1],curTargetPos[2]] + pitch = camInfo[9] + targetPos = [ + 0.95 * curTargetPos[0] + 0.05 * base_pos[0], 0.95 * curTargetPos[1] + 0.05 * base_pos[1], + curTargetPos[2] + ] - - self._pybullet_client.resetDebugVisualizerCamera( - distance, yaw, pitch, base_pos) + self._pybullet_client.resetDebugVisualizerCamera(distance, yaw, pitch, base_pos) action = self._transform_action_to_motor_command(action) for _ in range(self._action_repeat): self.minitaur.ApplyAction(action) @@ -285,12 +280,17 @@ class MinitaurBulletEnv(gym.Env): pitch=self._cam_pitch, roll=0, upAxisIndex=2) - proj_matrix = self._pybullet_client.computeProjectionMatrixFOV( - fov=60, aspect=float(RENDER_WIDTH)/RENDER_HEIGHT, - nearVal=0.1, farVal=100.0) - (_, _, px, _, _) = self._pybullet_client.getCameraImage( - width=RENDER_WIDTH, height=RENDER_HEIGHT, viewMatrix=view_matrix, - projectionMatrix=proj_matrix, renderer=pybullet.ER_BULLET_HARDWARE_OPENGL) + proj_matrix = self._pybullet_client.computeProjectionMatrixFOV(fov=60, + aspect=float(RENDER_WIDTH) / + RENDER_HEIGHT, + nearVal=0.1, + farVal=100.0) + (_, _, px, _, + _) = self._pybullet_client.getCameraImage(width=RENDER_WIDTH, + height=RENDER_HEIGHT, + viewMatrix=view_matrix, + projectionMatrix=proj_matrix, + renderer=pybullet.ER_BULLET_HARDWARE_OPENGL) rgb_array = np.array(px) rgb_array = rgb_array[:, :, :3] return rgb_array @@ -301,9 +301,8 @@ class MinitaurBulletEnv(gym.Env): Returns: A numpy array of motor angles. """ - return np.array( - self._observation[MOTOR_ANGLE_OBSERVATION_INDEX: - MOTOR_ANGLE_OBSERVATION_INDEX + NUM_MOTORS]) + return np.array(self._observation[MOTOR_ANGLE_OBSERVATION_INDEX:MOTOR_ANGLE_OBSERVATION_INDEX + + NUM_MOTORS]) def get_minitaur_motor_velocities(self): """Get the minitaur's motor velocities. @@ -312,8 +311,8 @@ class MinitaurBulletEnv(gym.Env): A numpy array of motor velocities. """ return np.array( - self._observation[MOTOR_VELOCITY_OBSERVATION_INDEX: - MOTOR_VELOCITY_OBSERVATION_INDEX + NUM_MOTORS]) + self._observation[MOTOR_VELOCITY_OBSERVATION_INDEX:MOTOR_VELOCITY_OBSERVATION_INDEX + + NUM_MOTORS]) def get_minitaur_motor_torques(self): """Get the minitaur's motor torques. @@ -322,8 +321,8 @@ class MinitaurBulletEnv(gym.Env): A numpy array of motor torques. """ return np.array( - self._observation[MOTOR_TORQUE_OBSERVATION_INDEX: - MOTOR_TORQUE_OBSERVATION_INDEX + NUM_MOTORS]) + self._observation[MOTOR_TORQUE_OBSERVATION_INDEX:MOTOR_TORQUE_OBSERVATION_INDEX + + NUM_MOTORS]) def get_minitaur_base_orientation(self): """Get the minitaur's base orientation, represented by a quaternion. @@ -347,8 +346,7 @@ class MinitaurBulletEnv(gym.Env): rot_mat = self._pybullet_client.getMatrixFromQuaternion(orientation) local_up = rot_mat[6:] pos = self.minitaur.GetBasePosition() - return (np.dot(np.asarray([0, 0, 1]), np.asarray(local_up)) < 0.85 or - pos[2] < 0.13) + return (np.dot(np.asarray([0, 0, 1]), np.asarray(local_up)) < 0.85 or pos[2] < 0.13) def _termination(self): position = self.minitaur.GetBasePosition() @@ -364,12 +362,9 @@ class MinitaurBulletEnv(gym.Env): energy_reward = np.abs( np.dot(self.minitaur.GetMotorTorques(), self.minitaur.GetMotorVelocities())) * self._time_step - reward = ( - self._distance_weight * forward_reward - - self._energy_weight * energy_reward + self._drift_weight * drift_reward - + self._shake_weight * shake_reward) - self._objectives.append( - [forward_reward, energy_reward, drift_reward, shake_reward]) + reward = (self._distance_weight * forward_reward - self._energy_weight * energy_reward + + self._drift_weight * drift_reward + self._shake_weight * shake_reward) + self._objectives.append([forward_reward, energy_reward, drift_reward, shake_reward]) return reward def get_objectives(self): @@ -383,9 +378,9 @@ class MinitaurBulletEnv(gym.Env): self._get_observation() observation = np.array(self._observation) if self._observation_noise_stdev > 0: - observation += (np.random.normal( - scale=self._observation_noise_stdev, size=observation.shape) * - self.minitaur.GetObservationUpperBound()) + observation += ( + np.random.normal(scale=self._observation_noise_stdev, size=observation.shape) * + self.minitaur.GetObservationUpperBound()) return observation if parse_version(gym.__version__) < parse_version('0.9.6'): diff --git a/examples/pybullet/gym/pybullet_envs/bullet/motor.py b/examples/pybullet/gym/pybullet_envs/bullet/motor.py index fa6b4f2da..db048c487 100644 --- a/examples/pybullet/gym/pybullet_envs/bullet/motor.py +++ b/examples/pybullet/gym/pybullet_envs/bullet/motor.py @@ -7,8 +7,7 @@ MOTOR_VOLTAGE = 16.0 MOTOR_RESISTANCE = 0.186 MOTOR_TORQUE_CONSTANT = 0.0954 MOTOR_VISCOUS_DAMPING = 0 -MOTOR_SPEED_LIMIT = MOTOR_VOLTAGE / (MOTOR_VISCOUS_DAMPING - + MOTOR_TORQUE_CONSTANT) +MOTOR_SPEED_LIMIT = MOTOR_VOLTAGE / (MOTOR_VISCOUS_DAMPING + MOTOR_TORQUE_CONSTANT) class MotorModel(object): @@ -24,10 +23,7 @@ class MotorModel(object): pd gains, viscous friction, back-EMF voltage and current-torque profile. """ - def __init__(self, - torque_control_enabled=False, - kp=1.2, - kd=0): + def __init__(self, torque_control_enabled=False, kp=1.2, kd=0): self._torque_control_enabled = torque_control_enabled self._kp = kp self._kd = kd @@ -50,8 +46,7 @@ class MotorModel(object): def get_viscous_dampling(self): return self._viscous_damping - def convert_to_torque(self, motor_commands, current_motor_angle, - current_motor_velocity): + def convert_to_torque(self, motor_commands, current_motor_angle, current_motor_velocity): """Convert the commands (position control or torque control) to torque. Args: @@ -66,8 +61,8 @@ class MotorModel(object): if self._torque_control_enabled: pwm = motor_commands else: - pwm = (-self._kp * (current_motor_angle - motor_commands) - - self._kd * current_motor_velocity) + pwm = (-self._kp * (current_motor_angle - motor_commands) - + self._kd * current_motor_velocity) pwm = np.clip(pwm, -1.0, 1.0) return self._convert_to_torque_from_pwm(pwm, current_motor_velocity) @@ -81,21 +76,19 @@ class MotorModel(object): actual_torque: The torque that needs to be applied to the motor. observed_torque: The torque observed by the sensor. """ - observed_torque = np.clip( - self._torque_constant * (pwm * self._voltage / self._resistance), - -OBSERVED_TORQUE_LIMIT, OBSERVED_TORQUE_LIMIT) + observed_torque = np.clip(self._torque_constant * (pwm * self._voltage / self._resistance), + -OBSERVED_TORQUE_LIMIT, OBSERVED_TORQUE_LIMIT) # Net voltage is clipped at 50V by diodes on the motor controller. - voltage_net = np.clip(pwm * self._voltage - - (self._torque_constant + self._viscous_damping) - * current_motor_velocity, - -VOLTAGE_CLIPPING, VOLTAGE_CLIPPING) + voltage_net = np.clip( + pwm * self._voltage - + (self._torque_constant + self._viscous_damping) * current_motor_velocity, + -VOLTAGE_CLIPPING, VOLTAGE_CLIPPING) current = voltage_net / self._resistance current_sign = np.sign(current) current_magnitude = np.absolute(current) # Saturate torque based on empirical current relation. - actual_torque = np.interp(current_magnitude, self._current_table, - self._torque_table) + actual_torque = np.interp(current_magnitude, self._current_table, self._torque_table) actual_torque = np.multiply(current_sign, actual_torque) return actual_torque, observed_torque diff --git a/examples/pybullet/gym/pybullet_envs/bullet/racecar.py b/examples/pybullet/gym/pybullet_envs/bullet/racecar.py index c4a47208a..7c1908f4f 100644 --- a/examples/pybullet/gym/pybullet_envs/bullet/racecar.py +++ b/examples/pybullet/gym/pybullet_envs/bullet/racecar.py @@ -4,80 +4,150 @@ import math import numpy as np + class Racecar: - def __init__(self, bullet_client, urdfRootPath='', timeStep=0.01): - self.urdfRootPath = urdfRootPath - self.timeStep = timeStep - self._p = bullet_client - self.reset() - - def reset(self): - car = self._p.loadURDF(os.path.join(self.urdfRootPath,"racecar/racecar_differential.urdf"), [0,0,.2],useFixedBase=False) - self.racecarUniqueId = car - #for i in range (self._p.getNumJoints(car)): - # print (self._p.getJointInfo(car,i)) - for wheel in range(self._p.getNumJoints(car)): - self._p.setJointMotorControl2(car,wheel,self._p.VELOCITY_CONTROL,targetVelocity=0,force=0) - self._p.getJointInfo(car,wheel) - - #self._p.setJointMotorControl2(car,10,self._p.VELOCITY_CONTROL,targetVelocity=1,force=10) - c = self._p.createConstraint(car,9,car,11,jointType=self._p.JOINT_GEAR,jointAxis =[0,1,0],parentFramePosition=[0,0,0],childFramePosition=[0,0,0]) - self._p.changeConstraint(c,gearRatio=1, maxForce=10000) - - c = self._p.createConstraint(car,10,car,13,jointType=self._p.JOINT_GEAR,jointAxis =[0,1,0],parentFramePosition=[0,0,0],childFramePosition=[0,0,0]) - self._p.changeConstraint(c,gearRatio=-1, maxForce=10000) - - c = self._p.createConstraint(car,9,car,13,jointType=self._p.JOINT_GEAR,jointAxis =[0,1,0],parentFramePosition=[0,0,0],childFramePosition=[0,0,0]) - self._p.changeConstraint(c,gearRatio=-1, maxForce=10000) - - c = self._p.createConstraint(car,16,car,18,jointType=self._p.JOINT_GEAR,jointAxis =[0,1,0],parentFramePosition=[0,0,0],childFramePosition=[0,0,0]) - self._p.changeConstraint(c,gearRatio=1, maxForce=10000) - - c = self._p.createConstraint(car,16,car,19,jointType=self._p.JOINT_GEAR,jointAxis =[0,1,0],parentFramePosition=[0,0,0],childFramePosition=[0,0,0]) - self._p.changeConstraint(c,gearRatio=-1, maxForce=10000) - - c = self._p.createConstraint(car,17,car,19,jointType=self._p.JOINT_GEAR,jointAxis =[0,1,0],parentFramePosition=[0,0,0],childFramePosition=[0,0,0]) - self._p.changeConstraint(c,gearRatio=-1, maxForce=10000) - - c = self._p.createConstraint(car,1,car,18,jointType=self._p.JOINT_GEAR,jointAxis =[0,1,0],parentFramePosition=[0,0,0],childFramePosition=[0,0,0]) - self._p.changeConstraint(c,gearRatio=-1, gearAuxLink = 15, maxForce=10000) - c = self._p.createConstraint(car,3,car,19,jointType=self._p.JOINT_GEAR,jointAxis =[0,1,0],parentFramePosition=[0,0,0],childFramePosition=[0,0,0]) - self._p.changeConstraint(c,gearRatio=-1, gearAuxLink = 15,maxForce=10000) - - self.steeringLinks = [0,2] - self.maxForce = 20 - self.nMotors = 2 - self.motorizedwheels=[8,15] - self.speedMultiplier = 20. - self.steeringMultiplier = 0.5 - - def getActionDimension(self): - return self.nMotors - - def getObservationDimension(self): - return len(self.getObservation()) - - def getObservation(self): - observation = [] - pos,orn=self._p.getBasePositionAndOrientation(self.racecarUniqueId) - - observation.extend(list(pos)) - observation.extend(list(orn)) - - return observation - - def applyAction(self, motorCommands): - targetVelocity=motorCommands[0]*self.speedMultiplier - #print("targetVelocity") - #print(targetVelocity) - steeringAngle = motorCommands[1]*self.steeringMultiplier - #print("steeringAngle") - #print(steeringAngle) - #print("maxForce") - #print(self.maxForce) - - for motor in self.motorizedwheels: - self._p.setJointMotorControl2(self.racecarUniqueId,motor,self._p.VELOCITY_CONTROL,targetVelocity=targetVelocity,force=self.maxForce) - for steer in self.steeringLinks: - self._p.setJointMotorControl2(self.racecarUniqueId,steer,self._p.POSITION_CONTROL,targetPosition=steeringAngle) + def __init__(self, bullet_client, urdfRootPath='', timeStep=0.01): + self.urdfRootPath = urdfRootPath + self.timeStep = timeStep + self._p = bullet_client + self.reset() + + def reset(self): + car = self._p.loadURDF(os.path.join(self.urdfRootPath, "racecar/racecar_differential.urdf"), + [0, 0, .2], + useFixedBase=False) + self.racecarUniqueId = car + #for i in range (self._p.getNumJoints(car)): + # print (self._p.getJointInfo(car,i)) + for wheel in range(self._p.getNumJoints(car)): + self._p.setJointMotorControl2(car, + wheel, + self._p.VELOCITY_CONTROL, + targetVelocity=0, + force=0) + self._p.getJointInfo(car, wheel) + + #self._p.setJointMotorControl2(car,10,self._p.VELOCITY_CONTROL,targetVelocity=1,force=10) + c = self._p.createConstraint(car, + 9, + car, + 11, + jointType=self._p.JOINT_GEAR, + jointAxis=[0, 1, 0], + parentFramePosition=[0, 0, 0], + childFramePosition=[0, 0, 0]) + self._p.changeConstraint(c, gearRatio=1, maxForce=10000) + + c = self._p.createConstraint(car, + 10, + car, + 13, + jointType=self._p.JOINT_GEAR, + jointAxis=[0, 1, 0], + parentFramePosition=[0, 0, 0], + childFramePosition=[0, 0, 0]) + self._p.changeConstraint(c, gearRatio=-1, maxForce=10000) + + c = self._p.createConstraint(car, + 9, + car, + 13, + jointType=self._p.JOINT_GEAR, + jointAxis=[0, 1, 0], + parentFramePosition=[0, 0, 0], + childFramePosition=[0, 0, 0]) + self._p.changeConstraint(c, gearRatio=-1, maxForce=10000) + + c = self._p.createConstraint(car, + 16, + car, + 18, + jointType=self._p.JOINT_GEAR, + jointAxis=[0, 1, 0], + parentFramePosition=[0, 0, 0], + childFramePosition=[0, 0, 0]) + self._p.changeConstraint(c, gearRatio=1, maxForce=10000) + + c = self._p.createConstraint(car, + 16, + car, + 19, + jointType=self._p.JOINT_GEAR, + jointAxis=[0, 1, 0], + parentFramePosition=[0, 0, 0], + childFramePosition=[0, 0, 0]) + self._p.changeConstraint(c, gearRatio=-1, maxForce=10000) + + c = self._p.createConstraint(car, + 17, + car, + 19, + jointType=self._p.JOINT_GEAR, + jointAxis=[0, 1, 0], + parentFramePosition=[0, 0, 0], + childFramePosition=[0, 0, 0]) + self._p.changeConstraint(c, gearRatio=-1, maxForce=10000) + + c = self._p.createConstraint(car, + 1, + car, + 18, + jointType=self._p.JOINT_GEAR, + jointAxis=[0, 1, 0], + parentFramePosition=[0, 0, 0], + childFramePosition=[0, 0, 0]) + self._p.changeConstraint(c, gearRatio=-1, gearAuxLink=15, maxForce=10000) + c = self._p.createConstraint(car, + 3, + car, + 19, + jointType=self._p.JOINT_GEAR, + jointAxis=[0, 1, 0], + parentFramePosition=[0, 0, 0], + childFramePosition=[0, 0, 0]) + self._p.changeConstraint(c, gearRatio=-1, gearAuxLink=15, maxForce=10000) + + self.steeringLinks = [0, 2] + self.maxForce = 20 + self.nMotors = 2 + self.motorizedwheels = [8, 15] + self.speedMultiplier = 20. + self.steeringMultiplier = 0.5 + + def getActionDimension(self): + return self.nMotors + + def getObservationDimension(self): + return len(self.getObservation()) + + def getObservation(self): + observation = [] + pos, orn = self._p.getBasePositionAndOrientation(self.racecarUniqueId) + + observation.extend(list(pos)) + observation.extend(list(orn)) + + return observation + + def applyAction(self, motorCommands): + targetVelocity = motorCommands[0] * self.speedMultiplier + #print("targetVelocity") + #print(targetVelocity) + steeringAngle = motorCommands[1] * self.steeringMultiplier + #print("steeringAngle") + #print(steeringAngle) + #print("maxForce") + #print(self.maxForce) + + for motor in self.motorizedwheels: + self._p.setJointMotorControl2(self.racecarUniqueId, + motor, + self._p.VELOCITY_CONTROL, + targetVelocity=targetVelocity, + force=self.maxForce) + for steer in self.steeringLinks: + self._p.setJointMotorControl2(self.racecarUniqueId, + steer, + self._p.POSITION_CONTROL, + targetPosition=steeringAngle) diff --git a/examples/pybullet/gym/pybullet_envs/bullet/racecarGymEnv.py b/examples/pybullet/gym/pybullet_envs/bullet/racecarGymEnv.py index 63d9148d3..a0118d668 100644 --- a/examples/pybullet/gym/pybullet_envs/bullet/racecarGymEnv.py +++ b/examples/pybullet/gym/pybullet_envs/bullet/racecarGymEnv.py @@ -1,7 +1,7 @@ -import os, inspect +import os, inspect currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe()))) parentdir = os.path.dirname(os.path.dirname(currentdir)) -os.sys.path.insert(0,parentdir) +os.sys.path.insert(0, parentdir) import math import gym @@ -19,11 +19,9 @@ from pkg_resources import parse_version RENDER_HEIGHT = 720 RENDER_WIDTH = 960 + class RacecarGymEnv(gym.Env): - metadata = { - 'render.modes': ['human', 'rgb_array'], - 'video.frames_per_second' : 50 - } + metadata = {'render.modes': ['human', 'rgb_array'], 'video.frames_per_second': 50} def __init__(self, urdfRoot=pybullet_data.getDataPath(), @@ -42,25 +40,24 @@ class RacecarGymEnv(gym.Env): self._renders = renders self._isDiscrete = isDiscrete if self._renders: - self._p = bullet_client.BulletClient( - connection_mode=pybullet.GUI) + self._p = bullet_client.BulletClient(connection_mode=pybullet.GUI) else: self._p = bullet_client.BulletClient() self.seed() #self.reset() - observationDim = 2 #len(self.getExtendedObservation()) + observationDim = 2 #len(self.getExtendedObservation()) #print("observationDim") #print(observationDim) # observation_high = np.array([np.finfo(np.float32).max] * observationDim) - observation_high = np.ones(observationDim) * 1000 #np.inf + observation_high = np.ones(observationDim) * 1000 #np.inf if (isDiscrete): self.action_space = spaces.Discrete(9) else: - action_dim = 2 - self._action_bound = 1 - action_high = np.array([self._action_bound] * action_dim) - self.action_space = spaces.Box(-action_high, action_high, dtype=np.float32) + action_dim = 2 + self._action_bound = 1 + action_high = np.array([self._action_bound] * action_dim) + self.action_space = spaces.Box(-action_high, action_high, dtype=np.float32) self.observation_space = spaces.Box(-observation_high, observation_high, dtype=np.float32) self.viewer = None @@ -69,23 +66,24 @@ class RacecarGymEnv(gym.Env): #p.setPhysicsEngineParameter(numSolverIterations=300) self._p.setTimeStep(self._timeStep) #self._p.loadURDF(os.path.join(self._urdfRoot,"plane.urdf")) - stadiumobjects = self._p.loadSDF(os.path.join(self._urdfRoot,"stadium.sdf")) + stadiumobjects = self._p.loadSDF(os.path.join(self._urdfRoot, "stadium.sdf")) #move the stadium objects slightly above 0 #for i in stadiumobjects: # pos,orn = self._p.getBasePositionAndOrientation(i) # newpos = [pos[0],pos[1],pos[2]-0.1] # self._p.resetBasePositionAndOrientation(i,newpos,orn) - dist = 5 +2.*random.random() - ang = 2.*3.1415925438*random.random() + dist = 5 + 2. * random.random() + ang = 2. * 3.1415925438 * random.random() ballx = dist * math.sin(ang) bally = dist * math.cos(ang) ballz = 1 - self._ballUniqueId = self._p.loadURDF(os.path.join(self._urdfRoot,"sphere2.urdf"),[ballx,bally,ballz]) - self._p.setGravity(0,0,-10) - self._racecar = racecar.Racecar(self._p,urdfRootPath=self._urdfRoot, timeStep=self._timeStep) + self._ballUniqueId = self._p.loadURDF(os.path.join(self._urdfRoot, "sphere2.urdf"), + [ballx, bally, ballz]) + self._p.setGravity(0, 0, -10) + self._racecar = racecar.Racecar(self._p, urdfRootPath=self._urdfRoot, timeStep=self._timeStep) self._envStepCounter = 0 for i in range(100): self._p.stepSimulation() @@ -100,26 +98,26 @@ class RacecarGymEnv(gym.Env): return [seed] def getExtendedObservation(self): - self._observation = [] #self._racecar.getObservation() - carpos,carorn = self._p.getBasePositionAndOrientation(self._racecar.racecarUniqueId) - ballpos,ballorn = self._p.getBasePositionAndOrientation(self._ballUniqueId) - invCarPos,invCarOrn = self._p.invertTransform(carpos,carorn) - ballPosInCar,ballOrnInCar = self._p.multiplyTransforms(invCarPos,invCarOrn,ballpos,ballorn) + self._observation = [] #self._racecar.getObservation() + carpos, carorn = self._p.getBasePositionAndOrientation(self._racecar.racecarUniqueId) + ballpos, ballorn = self._p.getBasePositionAndOrientation(self._ballUniqueId) + invCarPos, invCarOrn = self._p.invertTransform(carpos, carorn) + ballPosInCar, ballOrnInCar = self._p.multiplyTransforms(invCarPos, invCarOrn, ballpos, ballorn) - self._observation.extend([ballPosInCar[0],ballPosInCar[1]]) - return self._observation + self._observation.extend([ballPosInCar[0], ballPosInCar[1]]) + return self._observation def step(self, action): if (self._renders): - basePos,orn = self._p.getBasePositionAndOrientation(self._racecar.racecarUniqueId) + basePos, orn = self._p.getBasePositionAndOrientation(self._racecar.racecarUniqueId) #self._p.resetDebugVisualizerCamera(1, 30, -40, basePos) if (self._isDiscrete): - fwd = [-1,-1,-1,0,0,0,1,1,1] - steerings = [-0.6,0,0.6,-0.6,0,0.6,-0.6,0,0.6] - forward = fwd[action] - steer = steerings[action] - realaction = [forward,steer] + fwd = [-1, -1, -1, 0, 0, 0, 1, 1, 1] + steerings = [-0.6, 0, 0.6, -0.6, 0, 0.6, -0.6, 0, 0.6] + forward = fwd[action] + steer = steerings[action] + realaction = [forward, steer] else: realaction = action @@ -142,35 +140,37 @@ class RacecarGymEnv(gym.Env): def render(self, mode='human', close=False): if mode != "rgb_array": return np.array([]) - base_pos,orn = self._p.getBasePositionAndOrientation(self._racecar.racecarUniqueId) - view_matrix = self._p.computeViewMatrixFromYawPitchRoll( - cameraTargetPosition=base_pos, - distance=self._cam_dist, - yaw=self._cam_yaw, - pitch=self._cam_pitch, - roll=0, - upAxisIndex=2) - proj_matrix = self._p.computeProjectionMatrixFOV( - fov=60, aspect=float(RENDER_WIDTH)/RENDER_HEIGHT, - nearVal=0.1, farVal=100.0) - (_, _, px, _, _) = self._p.getCameraImage( - width=RENDER_WIDTH, height=RENDER_HEIGHT, viewMatrix=view_matrix, - projectionMatrix=proj_matrix, renderer=pybullet.ER_BULLET_HARDWARE_OPENGL) + base_pos, orn = self._p.getBasePositionAndOrientation(self._racecar.racecarUniqueId) + view_matrix = self._p.computeViewMatrixFromYawPitchRoll(cameraTargetPosition=base_pos, + distance=self._cam_dist, + yaw=self._cam_yaw, + pitch=self._cam_pitch, + roll=0, + upAxisIndex=2) + proj_matrix = self._p.computeProjectionMatrixFOV(fov=60, + aspect=float(RENDER_WIDTH) / RENDER_HEIGHT, + nearVal=0.1, + farVal=100.0) + (_, _, px, _, _) = self._p.getCameraImage(width=RENDER_WIDTH, + height=RENDER_HEIGHT, + viewMatrix=view_matrix, + projectionMatrix=proj_matrix, + renderer=pybullet.ER_BULLET_HARDWARE_OPENGL) rgb_array = np.array(px) rgb_array = rgb_array[:, :, :3] return rgb_array - def _termination(self): - return self._envStepCounter>1000 + return self._envStepCounter > 1000 def _reward(self): - closestPoints = self._p.getClosestPoints(self._racecar.racecarUniqueId,self._ballUniqueId,10000) + closestPoints = self._p.getClosestPoints(self._racecar.racecarUniqueId, self._ballUniqueId, + 10000) numPt = len(closestPoints) - reward=-1000 + reward = -1000 #print(numPt) - if (numPt>0): + if (numPt > 0): #print("reward:") reward = -closestPoints[0][8] #print(reward) diff --git a/examples/pybullet/gym/pybullet_envs/bullet/racecarZEDGymEnv.py b/examples/pybullet/gym/pybullet_envs/bullet/racecarZEDGymEnv.py index 6a7ce3400..8137708ba 100644 --- a/examples/pybullet/gym/pybullet_envs/bullet/racecarZEDGymEnv.py +++ b/examples/pybullet/gym/pybullet_envs/bullet/racecarZEDGymEnv.py @@ -1,7 +1,7 @@ -import os, inspect +import os, inspect currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe()))) parentdir = os.path.dirname(os.path.dirname(currentdir)) -os.sys.path.insert(0,parentdir) +os.sys.path.insert(0, parentdir) import math import gym @@ -19,11 +19,9 @@ from pkg_resources import parse_version RENDER_HEIGHT = 720 RENDER_WIDTH = 960 + class RacecarZEDGymEnv(gym.Env): - metadata = { - 'render.modes': ['human', 'rgb_array'], - 'video.frames_per_second' : 50 - } + metadata = {'render.modes': ['human', 'rgb_array'], 'video.frames_per_second': 50} def __init__(self, urdfRoot=pybullet_data.getDataPath(), @@ -44,8 +42,7 @@ class RacecarZEDGymEnv(gym.Env): self._isDiscrete = isDiscrete if self._renders: - self._p = bullet_client.BulletClient( - connection_mode=pybullet.GUI) + self._p = bullet_client.BulletClient(connection_mode=pybullet.GUI) else: self._p = bullet_client.BulletClient() @@ -59,11 +56,14 @@ class RacecarZEDGymEnv(gym.Env): if (isDiscrete): self.action_space = spaces.Discrete(9) else: - action_dim = 2 - self._action_bound = 1 - action_high = np.array([self._action_bound] * action_dim) - self.action_space = spaces.Box(-action_high, action_high, dtype=np.float32) - self.observation_space = spaces.Box(low=0, high=255, shape=(self._height, self._width, 4), dtype=np.uint8) + action_dim = 2 + self._action_bound = 1 + action_high = np.array([self._action_bound] * action_dim) + self.action_space = spaces.Box(-action_high, action_high, dtype=np.float32) + self.observation_space = spaces.Box(low=0, + high=255, + shape=(self._height, self._width, 4), + dtype=np.uint8) self.viewer = None @@ -72,23 +72,24 @@ class RacecarZEDGymEnv(gym.Env): #p.setPhysicsEngineParameter(numSolverIterations=300) self._p.setTimeStep(self._timeStep) #self._p.loadURDF(os.path.join(os.path.dirname(__file__),"../data","plane.urdf")) - stadiumobjects = self._p.loadSDF(os.path.join(self._urdfRoot,"stadium.sdf")) + stadiumobjects = self._p.loadSDF(os.path.join(self._urdfRoot, "stadium.sdf")) #move the stadium objects slightly above 0 for i in stadiumobjects: - pos,orn = self._p.getBasePositionAndOrientation(i) - newpos = [pos[0],pos[1],pos[2]+0.1] - self._p.resetBasePositionAndOrientation(i,newpos,orn) + pos, orn = self._p.getBasePositionAndOrientation(i) + newpos = [pos[0], pos[1], pos[2] + 0.1] + self._p.resetBasePositionAndOrientation(i, newpos, orn) - dist = 5 +2.*random.random() - ang = 2.*3.1415925438*random.random() + dist = 5 + 2. * random.random() + ang = 2. * 3.1415925438 * random.random() ballx = dist * math.sin(ang) bally = dist * math.cos(ang) ballz = 1 - self._ballUniqueId = self._p.loadURDF(os.path.join(self._urdfRoot,"sphere2red.urdf"),[ballx,bally,ballz]) - self._p.setGravity(0,0,-10) - self._racecar = racecar.Racecar(self._p,urdfRootPath=self._urdfRoot, timeStep=self._timeStep) + self._ballUniqueId = self._p.loadURDF(os.path.join(self._urdfRoot, "sphere2red.urdf"), + [ballx, bally, ballz]) + self._p.setGravity(0, 0, -10) + self._racecar = racecar.Racecar(self._p, urdfRootPath=self._urdfRoot, timeStep=self._timeStep) self._envStepCounter = 0 for i in range(100): self._p.stepSimulation() @@ -103,38 +104,50 @@ class RacecarZEDGymEnv(gym.Env): return [seed] def getExtendedObservation(self): - carpos,carorn = self._p.getBasePositionAndOrientation(self._racecar.racecarUniqueId) - carmat = self._p.getMatrixFromQuaternion(carorn) - ballpos,ballorn = self._p.getBasePositionAndOrientation(self._ballUniqueId) - invCarPos,invCarOrn = self._p.invertTransform(carpos,carorn) - ballPosInCar,ballOrnInCar = self._p.multiplyTransforms(invCarPos,invCarOrn,ballpos,ballorn) - dist0 = 0.3 - dist1 = 1. - eyePos = [carpos[0]+dist0*carmat[0],carpos[1]+dist0*carmat[3],carpos[2]+dist0*carmat[6]+0.3] - targetPos = [carpos[0]+dist1*carmat[0],carpos[1]+dist1*carmat[3],carpos[2]+dist1*carmat[6]+0.3] - up = [carmat[2],carmat[5],carmat[8]] - viewMat = self._p.computeViewMatrix(eyePos,targetPos,up) - #viewMat = self._p.computeViewMatrixFromYawPitchRoll(carpos,1,0,0,0,2) - #print("projectionMatrix:") - #print(self._p.getDebugVisualizerCamera()[3]) - projMatrix = [0.7499999403953552, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, -1.0000200271606445, -1.0, 0.0, 0.0, -0.02000020071864128, 0.0] - img_arr = self._p.getCameraImage(width=self._width,height=self._height,viewMatrix=viewMat,projectionMatrix=projMatrix) - rgb=img_arr[2] - np_img_arr = np.reshape(rgb, (self._height, self._width, 4)) - self._observation = np_img_arr - return self._observation + carpos, carorn = self._p.getBasePositionAndOrientation(self._racecar.racecarUniqueId) + carmat = self._p.getMatrixFromQuaternion(carorn) + ballpos, ballorn = self._p.getBasePositionAndOrientation(self._ballUniqueId) + invCarPos, invCarOrn = self._p.invertTransform(carpos, carorn) + ballPosInCar, ballOrnInCar = self._p.multiplyTransforms(invCarPos, invCarOrn, ballpos, ballorn) + dist0 = 0.3 + dist1 = 1. + eyePos = [ + carpos[0] + dist0 * carmat[0], carpos[1] + dist0 * carmat[3], + carpos[2] + dist0 * carmat[6] + 0.3 + ] + targetPos = [ + carpos[0] + dist1 * carmat[0], carpos[1] + dist1 * carmat[3], + carpos[2] + dist1 * carmat[6] + 0.3 + ] + up = [carmat[2], carmat[5], carmat[8]] + viewMat = self._p.computeViewMatrix(eyePos, targetPos, up) + #viewMat = self._p.computeViewMatrixFromYawPitchRoll(carpos,1,0,0,0,2) + #print("projectionMatrix:") + #print(self._p.getDebugVisualizerCamera()[3]) + projMatrix = [ + 0.7499999403953552, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, -1.0000200271606445, -1.0, + 0.0, 0.0, -0.02000020071864128, 0.0 + ] + img_arr = self._p.getCameraImage(width=self._width, + height=self._height, + viewMatrix=viewMat, + projectionMatrix=projMatrix) + rgb = img_arr[2] + np_img_arr = np.reshape(rgb, (self._height, self._width, 4)) + self._observation = np_img_arr + return self._observation def step(self, action): if (self._renders): - basePos,orn = self._p.getBasePositionAndOrientation(self._racecar.racecarUniqueId) + basePos, orn = self._p.getBasePositionAndOrientation(self._racecar.racecarUniqueId) #self._p.resetDebugVisualizerCamera(1, 30, -40, basePos) if (self._isDiscrete): - fwd = [-1,-1,-1,0,0,0,1,1,1] - steerings = [-0.6,0,0.6,-0.6,0,0.6,-0.6,0,0.6] - forward = fwd[action] - steer = steerings[action] - realaction = [forward,steer] + fwd = [-1, -1, -1, 0, 0, 0, 1, 1, 1] + steerings = [-0.6, 0, 0.6, -0.6, 0, 0.6, -0.6, 0, 0.6] + forward = fwd[action] + steer = steerings[action] + realaction = [forward, steer] else: realaction = action @@ -157,35 +170,37 @@ class RacecarZEDGymEnv(gym.Env): def render(self, mode='human', close=False): if mode != "rgb_array": return np.array([]) - base_pos,orn = self._p.getBasePositionAndOrientation(self._racecar.racecarUniqueId) - view_matrix = self._p.computeViewMatrixFromYawPitchRoll( - cameraTargetPosition=base_pos, - distance=self._cam_dist, - yaw=self._cam_yaw, - pitch=self._cam_pitch, - roll=0, - upAxisIndex=2) - proj_matrix = self._p.computeProjectionMatrixFOV( - fov=60, aspect=float(RENDER_WIDTH)/RENDER_HEIGHT, - nearVal=0.1, farVal=100.0) - (_, _, px, _, _) = self._p.getCameraImage( - width=RENDER_WIDTH, height=RENDER_HEIGHT, viewMatrix=view_matrix, - projectionMatrix=proj_matrix, renderer=pybullet.ER_BULLET_HARDWARE_OPENGL) + base_pos, orn = self._p.getBasePositionAndOrientation(self._racecar.racecarUniqueId) + view_matrix = self._p.computeViewMatrixFromYawPitchRoll(cameraTargetPosition=base_pos, + distance=self._cam_dist, + yaw=self._cam_yaw, + pitch=self._cam_pitch, + roll=0, + upAxisIndex=2) + proj_matrix = self._p.computeProjectionMatrixFOV(fov=60, + aspect=float(RENDER_WIDTH) / RENDER_HEIGHT, + nearVal=0.1, + farVal=100.0) + (_, _, px, _, _) = self._p.getCameraImage(width=RENDER_WIDTH, + height=RENDER_HEIGHT, + viewMatrix=view_matrix, + projectionMatrix=proj_matrix, + renderer=pybullet.ER_BULLET_HARDWARE_OPENGL) rgb_array = np.array(px) rgb_array = rgb_array[:, :, :3] return rgb_array - def _termination(self): - return self._envStepCounter>1000 + return self._envStepCounter > 1000 def _reward(self): - closestPoints = self._p.getClosestPoints(self._racecar.racecarUniqueId,self._ballUniqueId,10000) + closestPoints = self._p.getClosestPoints(self._racecar.racecarUniqueId, self._ballUniqueId, + 10000) numPt = len(closestPoints) - reward=-1000 + reward = -1000 #print(numPt) - if (numPt>0): + if (numPt > 0): #print("reward:") reward = -closestPoints[0][8] #print(reward) diff --git a/examples/pybullet/gym/pybullet_envs/deep_mimic/DeepMimic_Optimizer.py b/examples/pybullet/gym/pybullet_envs/deep_mimic/DeepMimic_Optimizer.py index 9aaeb2b90..b6e9e6607 100644 --- a/examples/pybullet/gym/pybullet_envs/deep_mimic/DeepMimic_Optimizer.py +++ b/examples/pybullet/gym/pybullet_envs/deep_mimic/DeepMimic_Optimizer.py @@ -4,11 +4,10 @@ import os import inspect currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe()))) parentdir = os.path.dirname(os.path.dirname(currentdir)) -os.sys.path.insert(0,parentdir) -print("parentdir=",parentdir) +os.sys.path.insert(0, parentdir) +print("parentdir=", parentdir) - -from pybullet_envs.deep_mimic.env.pybullet_deep_mimic_env import PyBulletDeepMimicEnv +from pybullet_envs.deep_mimic.env.pybullet_deep_mimic_env import PyBulletDeepMimicEnv from pybullet_envs.deep_mimic.learning.rl_world import RLWorld from pybullet_utils.logger import Logger from pybullet_envs.deep_mimic.testrl import update_world, update_timestep, build_world @@ -17,36 +16,40 @@ import pybullet_utils.mpi_util as MPIUtil args = [] world = None + def run(): - global update_timestep - global world + global update_timestep + global world + + done = False + while not (done): + update_world(world, update_timestep) - done = False - while not (done): - update_world(world, update_timestep) + return - return def shutdown(): - global world + global world + + Logger.print2('Shutting down...') + world.shutdown() + return - Logger.print2('Shutting down...') - world.shutdown() - return def main(): - global args - global world + global args + global world + + # Command line arguments + args = sys.argv[1:] + enable_draw = False + world = build_world(args, enable_draw) - # Command line arguments - args = sys.argv[1:] - enable_draw = False - world = build_world(args, enable_draw) + run() + shutdown() - run() - shutdown() + return - return if __name__ == '__main__': - main() + main() diff --git a/examples/pybullet/gym/pybullet_envs/deep_mimic/env/action_space.py b/examples/pybullet/gym/pybullet_envs/deep_mimic/env/action_space.py index 1014f7497..f28621417 100644 --- a/examples/pybullet/gym/pybullet_envs/deep_mimic/env/action_space.py +++ b/examples/pybullet/gym/pybullet_envs/deep_mimic/env/action_space.py @@ -1,6 +1,7 @@ from enum import Enum + class ActionSpace(Enum): - Null = 0 - Continuous = 1 - Discrete = 2
\ No newline at end of file + Null = 0 + Continuous = 1 + Discrete = 2 diff --git a/examples/pybullet/gym/pybullet_envs/deep_mimic/env/env.py b/examples/pybullet/gym/pybullet_envs/deep_mimic/env/env.py index c8c17df5d..900702bdd 100644 --- a/examples/pybullet/gym/pybullet_envs/deep_mimic/env/env.py +++ b/examples/pybullet/gym/pybullet_envs/deep_mimic/env/env.py @@ -1,19 +1,21 @@ -from abc import abstractmethod +from abc import abstractmethod import sys, abc if sys.version_info >= (3, 4): - ABC = abc.ABC + ABC = abc.ABC else: - ABC = abc.ABCMeta('ABC', (), {}) + ABC = abc.ABCMeta('ABC', (), {}) import numpy as np from enum import Enum + class Env(ABC): - class Terminate(Enum): - Null = 0 - Fail = 1 - Succ = 2 - - def __init__(self, args, enable_draw): - self.enable_draw = enable_draw - return + + class Terminate(Enum): + Null = 0 + Fail = 1 + Succ = 2 + + def __init__(self, args, enable_draw): + self.enable_draw = enable_draw + return diff --git a/examples/pybullet/gym/pybullet_envs/deep_mimic/env/humanoid_pose_interpolator.py b/examples/pybullet/gym/pybullet_envs/deep_mimic/env/humanoid_pose_interpolator.py index af0870ff5..15839ccf1 100644 --- a/examples/pybullet/gym/pybullet_envs/deep_mimic/env/humanoid_pose_interpolator.py +++ b/examples/pybullet/gym/pybullet_envs/deep_mimic/env/humanoid_pose_interpolator.py @@ -1,36 +1,61 @@ from pybullet_utils import bullet_client import math - + + class HumanoidPoseInterpolator(object): + def __init__(self): pass - - def Reset(self,basePos=[0,0,0], baseOrn=[0,0,0,1],chestRot=[0,0,0,1], neckRot=[0,0,0,1],rightHipRot= [0,0,0,1], rightKneeRot=[0],rightAnkleRot = [0,0,0,1], - rightShoulderRot = [0,0,0,1],rightElbowRot = [0], leftHipRot = [0,0,0,1], leftKneeRot = [0],leftAnkleRot = [0,0,0,1], - leftShoulderRot = [0,0,0,1] ,leftElbowRot = [0], - baseLinVel = [0,0,0],baseAngVel = [0,0,0], chestVel = [0,0,0],neckVel = [0,0,0],rightHipVel = [0,0,0],rightKneeVel = [0], - rightAnkleVel = [0,0,0],rightShoulderVel = [0,0,0],rightElbowVel = [0],leftHipVel = [0,0,0],leftKneeVel = [0],leftAnkleVel = [0,0,0],leftShoulderVel = [0,0,0],leftElbowVel = [0] - ): - + + def Reset(self, + basePos=[0, 0, 0], + baseOrn=[0, 0, 0, 1], + chestRot=[0, 0, 0, 1], + neckRot=[0, 0, 0, 1], + rightHipRot=[0, 0, 0, 1], + rightKneeRot=[0], + rightAnkleRot=[0, 0, 0, 1], + rightShoulderRot=[0, 0, 0, 1], + rightElbowRot=[0], + leftHipRot=[0, 0, 0, 1], + leftKneeRot=[0], + leftAnkleRot=[0, 0, 0, 1], + leftShoulderRot=[0, 0, 0, 1], + leftElbowRot=[0], + baseLinVel=[0, 0, 0], + baseAngVel=[0, 0, 0], + chestVel=[0, 0, 0], + neckVel=[0, 0, 0], + rightHipVel=[0, 0, 0], + rightKneeVel=[0], + rightAnkleVel=[0, 0, 0], + rightShoulderVel=[0, 0, 0], + rightElbowVel=[0], + leftHipVel=[0, 0, 0], + leftKneeVel=[0], + leftAnkleVel=[0, 0, 0], + leftShoulderVel=[0, 0, 0], + leftElbowVel=[0]): + self._basePos = basePos self._baseLinVel = baseLinVel #print("HumanoidPoseInterpolator.Reset: baseLinVel = ", baseLinVel) - self._baseOrn =baseOrn + self._baseOrn = baseOrn self._baseAngVel = baseAngVel - + self._chestRot = chestRot - self._chestVel =chestVel + self._chestVel = chestVel self._neckRot = neckRot self._neckVel = neckVel - + self._rightHipRot = rightHipRot self._rightHipVel = rightHipVel - self._rightKneeRot =rightKneeRot + self._rightKneeRot = rightKneeRot self._rightKneeVel = rightKneeVel self._rightAnkleRot = rightAnkleRot self._rightAnkleVel = rightAnkleVel - - self._rightShoulderRot =rightShoulderRot + + self._rightShoulderRot = rightShoulderRot self._rightShoulderVel = rightShoulderVel self._rightElbowRot = rightElbowRot self._rightElbowVel = rightElbowVel @@ -39,225 +64,253 @@ class HumanoidPoseInterpolator(object): self._leftHipVel = leftHipVel self._leftKneeRot = leftKneeRot self._leftKneeVel = leftKneeVel - self._leftAnkleRot =leftAnkleRot + self._leftAnkleRot = leftAnkleRot self._leftAnkleVel = leftAnkleVel - + self._leftShoulderRot = leftShoulderRot self._leftShoulderVel = leftShoulderVel - self._leftElbowRot =leftElbowRot + self._leftElbowRot = leftElbowRot self._leftElbowVel = leftElbowVel - def ComputeLinVel(self,posStart, posEnd, deltaTime): - vel = [(posEnd[0]-posStart[0])/deltaTime,(posEnd[1]-posStart[1])/deltaTime,(posEnd[2]-posStart[2])/deltaTime] + def ComputeLinVel(self, posStart, posEnd, deltaTime): + vel = [(posEnd[0] - posStart[0]) / deltaTime, (posEnd[1] - posStart[1]) / deltaTime, + (posEnd[2] - posStart[2]) / deltaTime] return vel - - def ComputeAngVel(self,ornStart, ornEnd, deltaTime, bullet_client): - dorn = bullet_client.getDifferenceQuaternion(ornStart,ornEnd) - axis,angle = bullet_client.getAxisAngleFromQuaternion(dorn) - angVel = [(axis[0]*angle)/deltaTime,(axis[1]*angle)/deltaTime,(axis[2]*angle)/deltaTime] + + def ComputeAngVel(self, ornStart, ornEnd, deltaTime, bullet_client): + dorn = bullet_client.getDifferenceQuaternion(ornStart, ornEnd) + axis, angle = bullet_client.getAxisAngleFromQuaternion(dorn) + angVel = [(axis[0] * angle) / deltaTime, (axis[1] * angle) / deltaTime, + (axis[2] * angle) / deltaTime] return angVel - - def ComputeAngVelRel(self,ornStart, ornEnd, deltaTime, bullet_client): - ornStartConjugate = [-ornStart[0],-ornStart[1],-ornStart[2],ornStart[3]] - pos_diff, q_diff =bullet_client.multiplyTransforms([0,0,0], ornStartConjugate, [0,0,0], ornEnd) - axis,angle = bullet_client.getAxisAngleFromQuaternion(q_diff) - angVel = [(axis[0]*angle)/deltaTime,(axis[1]*angle)/deltaTime,(axis[2]*angle)/deltaTime] + + def ComputeAngVelRel(self, ornStart, ornEnd, deltaTime, bullet_client): + ornStartConjugate = [-ornStart[0], -ornStart[1], -ornStart[2], ornStart[3]] + pos_diff, q_diff = bullet_client.multiplyTransforms([0, 0, 0], ornStartConjugate, [0, 0, 0], + ornEnd) + axis, angle = bullet_client.getAxisAngleFromQuaternion(q_diff) + angVel = [(axis[0] * angle) / deltaTime, (axis[1] * angle) / deltaTime, + (axis[2] * angle) / deltaTime] return angVel - - + def NormalizeVector(self, vec): - length2 = orn[0]*orn[0]+orn[1]*orn[1]+orn[2]*orn[2] - if (length2>0): + length2 = orn[0] * orn[0] + orn[1] * orn[1] + orn[2] * orn[2] + if (length2 > 0): length = math.sqrt(length2) - + def NormalizeQuaternion(self, orn): - length2 = orn[0]*orn[0]+orn[1]*orn[1]+orn[2]*orn[2]+orn[3]*orn[3] - if (length2>0): + length2 = orn[0] * orn[0] + orn[1] * orn[1] + orn[2] * orn[2] + orn[3] * orn[3] + if (length2 > 0): length = math.sqrt(length2) - orn[0]/=length - orn[1]/=length - orn[2]/=length - orn[3]/=length + orn[0] /= length + orn[1] /= length + orn[2] /= length + orn[3] /= length return orn - + #print("Normalize? length=",length) - def PostProcessMotionData(self, frameData): - baseOrn1Start = [frameData[5],frameData[6], frameData[7],frameData[4]] - - chestRotStart = [frameData[9],frameData[10],frameData[11],frameData[8]] - - neckRotStart = [frameData[13],frameData[14],frameData[15],frameData[12]] - rightHipRotStart = [frameData[17],frameData[18],frameData[19],frameData[16]] - rightAnkleRotStart = [frameData[22],frameData[23],frameData[24],frameData[21]] - rightShoulderRotStart = [frameData[26],frameData[27],frameData[28],frameData[25]] - leftHipRotStart = [frameData[31],frameData[32],frameData[33],frameData[30]] - leftAnkleRotStart = [frameData[36],frameData[37],frameData[38],frameData[35]] - leftShoulderRotStart = [frameData[40],frameData[41],frameData[42],frameData[39]] - - + baseOrn1Start = [frameData[5], frameData[6], frameData[7], frameData[4]] + + chestRotStart = [frameData[9], frameData[10], frameData[11], frameData[8]] + + neckRotStart = [frameData[13], frameData[14], frameData[15], frameData[12]] + rightHipRotStart = [frameData[17], frameData[18], frameData[19], frameData[16]] + rightAnkleRotStart = [frameData[22], frameData[23], frameData[24], frameData[21]] + rightShoulderRotStart = [frameData[26], frameData[27], frameData[28], frameData[25]] + leftHipRotStart = [frameData[31], frameData[32], frameData[33], frameData[30]] + leftAnkleRotStart = [frameData[36], frameData[37], frameData[38], frameData[35]] + leftShoulderRotStart = [frameData[40], frameData[41], frameData[42], frameData[39]] + def GetPose(self): - pose = [ self._basePos[0],self._basePos[1],self._basePos[2], - self._baseOrn[0],self._baseOrn[1],self._baseOrn[2],self._baseOrn[3], - self._chestRot[0],self._chestRot[1],self._chestRot[2],self._chestRot[3], - self._neckRot[0],self._neckRot[1],self._neckRot[2],self._neckRot[3], - self._rightHipRot[0],self._rightHipRot[1],self._rightHipRot[2],self._rightHipRot[3], - self._rightKneeRot[0], - self._rightAnkleRot[0],self._rightAnkleRot[1],self._rightAnkleRot[2],self._rightAnkleRot[3], - self._rightShoulderRot[0],self._rightShoulderRot[1],self._rightShoulderRot[2],self._rightShoulderRot[3], - self._rightElbowRot[0], - self._leftHipRot[0],self._leftHipRot[1],self._leftHipRot[2],self._leftHipRot[3], - self._leftKneeRot[0], - self._leftAnkleRot[0],self._leftAnkleRot[1],self._leftAnkleRot[2],self._leftAnkleRot[3], - self._leftShoulderRot[0],self._leftShoulderRot[1],self._leftShoulderRot[2],self._leftShoulderRot[3], - self._leftElbowRot[0] ] - return pose - - def Slerp(self, frameFraction, frameData, frameDataNext,bullet_client ): + pose = [ + self._basePos[0], self._basePos[1], self._basePos[2], self._baseOrn[0], self._baseOrn[1], + self._baseOrn[2], self._baseOrn[3], self._chestRot[0], self._chestRot[1], + self._chestRot[2], self._chestRot[3], self._neckRot[0], self._neckRot[1], self._neckRot[2], + self._neckRot[3], self._rightHipRot[0], self._rightHipRot[1], self._rightHipRot[2], + self._rightHipRot[3], self._rightKneeRot[0], self._rightAnkleRot[0], + self._rightAnkleRot[1], self._rightAnkleRot[2], self._rightAnkleRot[3], + self._rightShoulderRot[0], self._rightShoulderRot[1], self._rightShoulderRot[2], + self._rightShoulderRot[3], self._rightElbowRot[0], self._leftHipRot[0], + self._leftHipRot[1], self._leftHipRot[2], self._leftHipRot[3], self._leftKneeRot[0], + self._leftAnkleRot[0], self._leftAnkleRot[1], self._leftAnkleRot[2], self._leftAnkleRot[3], + self._leftShoulderRot[0], self._leftShoulderRot[1], self._leftShoulderRot[2], + self._leftShoulderRot[3], self._leftElbowRot[0] + ] + return pose + + def Slerp(self, frameFraction, frameData, frameDataNext, bullet_client): keyFrameDuration = frameData[0] - basePos1Start = [frameData[1],frameData[2],frameData[3]] - basePos1End = [frameDataNext[1],frameDataNext[2],frameDataNext[3]] - self._basePos = [basePos1Start[0]+frameFraction*(basePos1End[0]-basePos1Start[0]), - basePos1Start[1]+frameFraction*(basePos1End[1]-basePos1Start[1]), - basePos1Start[2]+frameFraction*(basePos1End[2]-basePos1Start[2])] - self._baseLinVel = self.ComputeLinVel(basePos1Start,basePos1End, keyFrameDuration) - baseOrn1Start = [frameData[5],frameData[6], frameData[7],frameData[4]] - baseOrn1Next = [frameDataNext[5],frameDataNext[6], frameDataNext[7],frameDataNext[4]] - self._baseOrn = bullet_client.getQuaternionSlerp(baseOrn1Start,baseOrn1Next,frameFraction) - self._baseAngVel = self.ComputeAngVel(baseOrn1Start,baseOrn1Next, keyFrameDuration, bullet_client) - + basePos1Start = [frameData[1], frameData[2], frameData[3]] + basePos1End = [frameDataNext[1], frameDataNext[2], frameDataNext[3]] + self._basePos = [ + basePos1Start[0] + frameFraction * (basePos1End[0] - basePos1Start[0]), + basePos1Start[1] + frameFraction * (basePos1End[1] - basePos1Start[1]), + basePos1Start[2] + frameFraction * (basePos1End[2] - basePos1Start[2]) + ] + self._baseLinVel = self.ComputeLinVel(basePos1Start, basePos1End, keyFrameDuration) + baseOrn1Start = [frameData[5], frameData[6], frameData[7], frameData[4]] + baseOrn1Next = [frameDataNext[5], frameDataNext[6], frameDataNext[7], frameDataNext[4]] + self._baseOrn = bullet_client.getQuaternionSlerp(baseOrn1Start, baseOrn1Next, frameFraction) + self._baseAngVel = self.ComputeAngVel(baseOrn1Start, baseOrn1Next, keyFrameDuration, + bullet_client) + ##pre-rotate to make z-up #y2zPos=[0,0,0.0] #y2zOrn = p.getQuaternionFromEuler([1.57,0,0]) #basePos,baseOrn = p.multiplyTransforms(y2zPos, y2zOrn,basePos1,baseOrn1) - chestRotStart = [frameData[9],frameData[10],frameData[11],frameData[8]] - chestRotEnd = [frameDataNext[9],frameDataNext[10],frameDataNext[11],frameDataNext[8]] - self._chestRot = bullet_client.getQuaternionSlerp(chestRotStart,chestRotEnd,frameFraction) - self._chestVel = self.ComputeAngVelRel(chestRotStart,chestRotEnd,keyFrameDuration,bullet_client) - - neckRotStart = [frameData[13],frameData[14],frameData[15],frameData[12]] - neckRotEnd= [frameDataNext[13],frameDataNext[14],frameDataNext[15],frameDataNext[12]] - self._neckRot = bullet_client.getQuaternionSlerp(neckRotStart,neckRotEnd,frameFraction) - self._neckVel = self.ComputeAngVelRel(neckRotStart,neckRotEnd,keyFrameDuration,bullet_client) - - rightHipRotStart = [frameData[17],frameData[18],frameData[19],frameData[16]] - rightHipRotEnd = [frameDataNext[17],frameDataNext[18],frameDataNext[19],frameDataNext[16]] - self._rightHipRot = bullet_client.getQuaternionSlerp(rightHipRotStart,rightHipRotEnd,frameFraction) - self._rightHipVel = self.ComputeAngVelRel(rightHipRotStart,rightHipRotEnd,keyFrameDuration,bullet_client) - + chestRotStart = [frameData[9], frameData[10], frameData[11], frameData[8]] + chestRotEnd = [frameDataNext[9], frameDataNext[10], frameDataNext[11], frameDataNext[8]] + self._chestRot = bullet_client.getQuaternionSlerp(chestRotStart, chestRotEnd, frameFraction) + self._chestVel = self.ComputeAngVelRel(chestRotStart, chestRotEnd, keyFrameDuration, + bullet_client) + + neckRotStart = [frameData[13], frameData[14], frameData[15], frameData[12]] + neckRotEnd = [frameDataNext[13], frameDataNext[14], frameDataNext[15], frameDataNext[12]] + self._neckRot = bullet_client.getQuaternionSlerp(neckRotStart, neckRotEnd, frameFraction) + self._neckVel = self.ComputeAngVelRel(neckRotStart, neckRotEnd, keyFrameDuration, + bullet_client) + + rightHipRotStart = [frameData[17], frameData[18], frameData[19], frameData[16]] + rightHipRotEnd = [frameDataNext[17], frameDataNext[18], frameDataNext[19], frameDataNext[16]] + self._rightHipRot = bullet_client.getQuaternionSlerp(rightHipRotStart, rightHipRotEnd, + frameFraction) + self._rightHipVel = self.ComputeAngVelRel(rightHipRotStart, rightHipRotEnd, keyFrameDuration, + bullet_client) + rightKneeRotStart = [frameData[20]] rightKneeRotEnd = [frameDataNext[20]] - self._rightKneeRot = [rightKneeRotStart[0]+frameFraction*(rightKneeRotEnd[0]-rightKneeRotStart[0])] - self._rightKneeVel = [(rightKneeRotEnd[0]-rightKneeRotStart[0])/keyFrameDuration] - - rightAnkleRotStart = [frameData[22],frameData[23],frameData[24],frameData[21]] - rightAnkleRotEnd = [frameDataNext[22],frameDataNext[23],frameDataNext[24],frameDataNext[21]] - self._rightAnkleRot = bullet_client.getQuaternionSlerp(rightAnkleRotStart,rightAnkleRotEnd,frameFraction) - self._rightAnkleVel = self.ComputeAngVelRel(rightAnkleRotStart,rightAnkleRotEnd,keyFrameDuration,bullet_client) - - rightShoulderRotStart = [frameData[26],frameData[27],frameData[28],frameData[25]] - rightShoulderRotEnd = [frameDataNext[26],frameDataNext[27],frameDataNext[28],frameDataNext[25]] - self._rightShoulderRot = bullet_client.getQuaternionSlerp(rightShoulderRotStart,rightShoulderRotEnd,frameFraction) - self._rightShoulderVel = self.ComputeAngVelRel(rightShoulderRotStart,rightShoulderRotEnd, keyFrameDuration,bullet_client) - + self._rightKneeRot = [ + rightKneeRotStart[0] + frameFraction * (rightKneeRotEnd[0] - rightKneeRotStart[0]) + ] + self._rightKneeVel = [(rightKneeRotEnd[0] - rightKneeRotStart[0]) / keyFrameDuration] + + rightAnkleRotStart = [frameData[22], frameData[23], frameData[24], frameData[21]] + rightAnkleRotEnd = [frameDataNext[22], frameDataNext[23], frameDataNext[24], frameDataNext[21]] + self._rightAnkleRot = bullet_client.getQuaternionSlerp(rightAnkleRotStart, rightAnkleRotEnd, + frameFraction) + self._rightAnkleVel = self.ComputeAngVelRel(rightAnkleRotStart, rightAnkleRotEnd, + keyFrameDuration, bullet_client) + + rightShoulderRotStart = [frameData[26], frameData[27], frameData[28], frameData[25]] + rightShoulderRotEnd = [ + frameDataNext[26], frameDataNext[27], frameDataNext[28], frameDataNext[25] + ] + self._rightShoulderRot = bullet_client.getQuaternionSlerp(rightShoulderRotStart, + rightShoulderRotEnd, frameFraction) + self._rightShoulderVel = self.ComputeAngVelRel(rightShoulderRotStart, rightShoulderRotEnd, + keyFrameDuration, bullet_client) + rightElbowRotStart = [frameData[29]] rightElbowRotEnd = [frameDataNext[29]] - self._rightElbowRot = [rightElbowRotStart[0]+frameFraction*(rightElbowRotEnd[0]-rightElbowRotStart[0])] - self._rightElbowVel = [(rightElbowRotEnd[0]-rightElbowRotStart[0])/keyFrameDuration] - - leftHipRotStart = [frameData[31],frameData[32],frameData[33],frameData[30]] - leftHipRotEnd = [frameDataNext[31],frameDataNext[32],frameDataNext[33],frameDataNext[30]] - self._leftHipRot = bullet_client.getQuaternionSlerp(leftHipRotStart,leftHipRotEnd,frameFraction) - self._leftHipVel = self.ComputeAngVelRel(leftHipRotStart, leftHipRotEnd,keyFrameDuration,bullet_client) - + self._rightElbowRot = [ + rightElbowRotStart[0] + frameFraction * (rightElbowRotEnd[0] - rightElbowRotStart[0]) + ] + self._rightElbowVel = [(rightElbowRotEnd[0] - rightElbowRotStart[0]) / keyFrameDuration] + + leftHipRotStart = [frameData[31], frameData[32], frameData[33], frameData[30]] + leftHipRotEnd = [frameDataNext[31], frameDataNext[32], frameDataNext[33], frameDataNext[30]] + self._leftHipRot = bullet_client.getQuaternionSlerp(leftHipRotStart, leftHipRotEnd, + frameFraction) + self._leftHipVel = self.ComputeAngVelRel(leftHipRotStart, leftHipRotEnd, keyFrameDuration, + bullet_client) + leftKneeRotStart = [frameData[34]] leftKneeRotEnd = [frameDataNext[34]] - self._leftKneeRot = [leftKneeRotStart[0] +frameFraction*(leftKneeRotEnd[0]-leftKneeRotStart[0]) ] - self._leftKneeVel = [(leftKneeRotEnd[0]-leftKneeRotStart[0])/keyFrameDuration] - - leftAnkleRotStart = [frameData[36],frameData[37],frameData[38],frameData[35]] - leftAnkleRotEnd = [frameDataNext[36],frameDataNext[37],frameDataNext[38],frameDataNext[35]] - self._leftAnkleRot = bullet_client.getQuaternionSlerp(leftAnkleRotStart,leftAnkleRotEnd,frameFraction) - self._leftAnkleVel = self.ComputeAngVelRel(leftAnkleRotStart,leftAnkleRotEnd,keyFrameDuration,bullet_client) - - leftShoulderRotStart = [frameData[40],frameData[41],frameData[42],frameData[39]] - leftShoulderRotEnd = [frameDataNext[40],frameDataNext[41],frameDataNext[42],frameDataNext[39]] - self._leftShoulderRot = bullet_client.getQuaternionSlerp(leftShoulderRotStart,leftShoulderRotEnd,frameFraction) - self._leftShoulderVel = self.ComputeAngVelRel(leftShoulderRotStart,leftShoulderRotEnd,keyFrameDuration,bullet_client) + self._leftKneeRot = [ + leftKneeRotStart[0] + frameFraction * (leftKneeRotEnd[0] - leftKneeRotStart[0]) + ] + self._leftKneeVel = [(leftKneeRotEnd[0] - leftKneeRotStart[0]) / keyFrameDuration] + + leftAnkleRotStart = [frameData[36], frameData[37], frameData[38], frameData[35]] + leftAnkleRotEnd = [frameDataNext[36], frameDataNext[37], frameDataNext[38], frameDataNext[35]] + self._leftAnkleRot = bullet_client.getQuaternionSlerp(leftAnkleRotStart, leftAnkleRotEnd, + frameFraction) + self._leftAnkleVel = self.ComputeAngVelRel(leftAnkleRotStart, leftAnkleRotEnd, + keyFrameDuration, bullet_client) + + leftShoulderRotStart = [frameData[40], frameData[41], frameData[42], frameData[39]] + leftShoulderRotEnd = [ + frameDataNext[40], frameDataNext[41], frameDataNext[42], frameDataNext[39] + ] + self._leftShoulderRot = bullet_client.getQuaternionSlerp(leftShoulderRotStart, + leftShoulderRotEnd, frameFraction) + self._leftShoulderVel = self.ComputeAngVelRel(leftShoulderRotStart, leftShoulderRotEnd, + keyFrameDuration, bullet_client) leftElbowRotStart = [frameData[43]] leftElbowRotEnd = [frameDataNext[43]] - self._leftElbowRot = [leftElbowRotStart[0]+frameFraction*(leftElbowRotEnd[0]-leftElbowRotStart[0])] - self._leftElbowVel = [(leftElbowRotEnd[0]-leftElbowRotStart[0])/keyFrameDuration] + self._leftElbowRot = [ + leftElbowRotStart[0] + frameFraction * (leftElbowRotEnd[0] - leftElbowRotStart[0]) + ] + self._leftElbowVel = [(leftElbowRotEnd[0] - leftElbowRotStart[0]) / keyFrameDuration] pose = self.GetPose() return pose - + def ConvertFromAction(self, pybullet_client, action): #turn action into pose - - self.Reset()#?? needed? - index=0 + + self.Reset() #?? needed? + index = 0 angle = action[index] - axis = [action[index+1],action[index+2],action[index+3]] - index+=4 - self._chestRot = pybullet_client.getQuaternionFromAxisAngle(axis,angle) + axis = [action[index + 1], action[index + 2], action[index + 3]] + index += 4 + self._chestRot = pybullet_client.getQuaternionFromAxisAngle(axis, angle) #print("pose._chestRot=",pose._chestRot) angle = action[index] - axis = [action[index+1],action[index+2],action[index+3]] - index+=4 - self._neckRot = pybullet_client.getQuaternionFromAxisAngle(axis,angle) - + axis = [action[index + 1], action[index + 2], action[index + 3]] + index += 4 + self._neckRot = pybullet_client.getQuaternionFromAxisAngle(axis, angle) + angle = action[index] - axis = [action[index+1],action[index+2],action[index+3]] - index+=4 - self._rightHipRot = pybullet_client.getQuaternionFromAxisAngle(axis,angle) - + axis = [action[index + 1], action[index + 2], action[index + 3]] + index += 4 + self._rightHipRot = pybullet_client.getQuaternionFromAxisAngle(axis, angle) + angle = action[index] - index+=1 + index += 1 self._rightKneeRot = [angle] - + angle = action[index] - axis = [action[index+1],action[index+2],action[index+3]] - index+=4 - self._rightAnkleRot = pybullet_client.getQuaternionFromAxisAngle(axis,angle) - + axis = [action[index + 1], action[index + 2], action[index + 3]] + index += 4 + self._rightAnkleRot = pybullet_client.getQuaternionFromAxisAngle(axis, angle) + angle = action[index] - axis = [action[index+1],action[index+2],action[index+3]] - index+=4 - self._rightShoulderRot = pybullet_client.getQuaternionFromAxisAngle(axis,angle) - + axis = [action[index + 1], action[index + 2], action[index + 3]] + index += 4 + self._rightShoulderRot = pybullet_client.getQuaternionFromAxisAngle(axis, angle) + angle = action[index] - index+=1 + index += 1 self._rightElbowRot = [angle] - + angle = action[index] - axis = [action[index+1],action[index+2],action[index+3]] - index+=4 - self._leftHipRot = pybullet_client.getQuaternionFromAxisAngle(axis,angle) - + axis = [action[index + 1], action[index + 2], action[index + 3]] + index += 4 + self._leftHipRot = pybullet_client.getQuaternionFromAxisAngle(axis, angle) + angle = action[index] - index+=1 + index += 1 self._leftKneeRot = [angle] - - + angle = action[index] - axis = [action[index+1],action[index+2],action[index+3]] - index+=4 - self._leftAnkleRot = pybullet_client.getQuaternionFromAxisAngle(axis,angle) - + axis = [action[index + 1], action[index + 2], action[index + 3]] + index += 4 + self._leftAnkleRot = pybullet_client.getQuaternionFromAxisAngle(axis, angle) + angle = action[index] - axis = [action[index+1],action[index+2],action[index+3]] - index+=4 - self._leftShoulderRot = pybullet_client.getQuaternionFromAxisAngle(axis,angle) - + axis = [action[index + 1], action[index + 2], action[index + 3]] + index += 4 + self._leftShoulderRot = pybullet_client.getQuaternionFromAxisAngle(axis, angle) + angle = action[index] - index+=1 + index += 1 self._leftElbowRot = [angle] - + pose = self.GetPose() return pose - diff --git a/examples/pybullet/gym/pybullet_envs/deep_mimic/env/humanoid_stable_pd.py b/examples/pybullet/gym/pybullet_envs/deep_mimic/env/humanoid_stable_pd.py index 9418dd4bd..1a39a91cb 100644 --- a/examples/pybullet/gym/pybullet_envs/deep_mimic/env/humanoid_stable_pd.py +++ b/examples/pybullet/gym/pybullet_envs/deep_mimic/env/humanoid_stable_pd.py @@ -1,144 +1,222 @@ - -from pybullet_utils import pd_controller_stable +from pybullet_utils import pd_controller_stable from pybullet_envs.deep_mimic.env import humanoid_pose_interpolator import math -chest=1 -neck=2 -rightHip=3 -rightKnee=4 -rightAnkle=5 -rightShoulder=6 -rightElbow=7 -leftHip=9 -leftKnee=10 -leftAnkle=11 -leftShoulder=12 -leftElbow=13 +chest = 1 +neck = 2 +rightHip = 3 +rightKnee = 4 +rightAnkle = 5 +rightShoulder = 6 +rightElbow = 7 +leftHip = 9 +leftKnee = 10 +leftAnkle = 11 +leftShoulder = 12 +leftElbow = 13 jointFrictionForce = 0 + class HumanoidStablePD(object): + def __init__(self, pybullet_client, mocap_data, timeStep, useFixedBase=True): self._pybullet_client = pybullet_client self._mocap_data = mocap_data print("LOADING humanoid!") - + self._sim_model = self._pybullet_client.loadURDF( - "humanoid/humanoid.urdf", [0,0.889540259,0],globalScaling=0.25, useFixedBase=useFixedBase, flags=self._pybullet_client.URDF_MAINTAIN_LINK_ORDER) + "humanoid/humanoid.urdf", [0, 0.889540259, 0], + globalScaling=0.25, + useFixedBase=useFixedBase, + flags=self._pybullet_client.URDF_MAINTAIN_LINK_ORDER) #self._pybullet_client.setCollisionFilterGroupMask(self._sim_model,-1,collisionFilterGroup=0,collisionFilterMask=0) #for j in range (self._pybullet_client.getNumJoints(self._sim_model)): # self._pybullet_client.setCollisionFilterGroupMask(self._sim_model,j,collisionFilterGroup=0,collisionFilterMask=0) - - - self._end_effectors = [5,8,11,14] #ankle and wrist, both left and right - + + self._end_effectors = [5, 8, 11, 14] #ankle and wrist, both left and right + self._kin_model = self._pybullet_client.loadURDF( - "humanoid/humanoid.urdf", [0,0.85,0],globalScaling=0.25, useFixedBase=True, flags=self._pybullet_client.URDF_MAINTAIN_LINK_ORDER) - + "humanoid/humanoid.urdf", [0, 0.85, 0], + globalScaling=0.25, + useFixedBase=True, + flags=self._pybullet_client.URDF_MAINTAIN_LINK_ORDER) + self._pybullet_client.changeDynamics(self._sim_model, -1, lateralFriction=0.9) - for j in range (self._pybullet_client.getNumJoints(self._sim_model)): + for j in range(self._pybullet_client.getNumJoints(self._sim_model)): self._pybullet_client.changeDynamics(self._sim_model, j, lateralFriction=0.9) - + self._pybullet_client.changeDynamics(self._sim_model, -1, linearDamping=0, angularDamping=0) self._pybullet_client.changeDynamics(self._kin_model, -1, linearDamping=0, angularDamping=0) - + #todo: add feature to disable simulation for a particular object. Until then, disable all collisions - self._pybullet_client.setCollisionFilterGroupMask(self._kin_model,-1,collisionFilterGroup=0,collisionFilterMask=0) - self._pybullet_client.changeDynamics(self._kin_model,-1,activationState=self._pybullet_client.ACTIVATION_STATE_SLEEP+self._pybullet_client.ACTIVATION_STATE_ENABLE_SLEEPING+self._pybullet_client.ACTIVATION_STATE_DISABLE_WAKEUP) + self._pybullet_client.setCollisionFilterGroupMask(self._kin_model, + -1, + collisionFilterGroup=0, + collisionFilterMask=0) + self._pybullet_client.changeDynamics( + self._kin_model, + -1, + activationState=self._pybullet_client.ACTIVATION_STATE_SLEEP + + self._pybullet_client.ACTIVATION_STATE_ENABLE_SLEEPING + + self._pybullet_client.ACTIVATION_STATE_DISABLE_WAKEUP) alpha = 0.4 - self._pybullet_client.changeVisualShape(self._kin_model,-1, rgbaColor=[1,1,1,alpha]) - for j in range (self._pybullet_client.getNumJoints(self._kin_model)): - self._pybullet_client.setCollisionFilterGroupMask(self._kin_model,j,collisionFilterGroup=0,collisionFilterMask=0) - self._pybullet_client.changeDynamics(self._kin_model,j,activationState=self._pybullet_client.ACTIVATION_STATE_SLEEP+self._pybullet_client.ACTIVATION_STATE_ENABLE_SLEEPING+self._pybullet_client.ACTIVATION_STATE_DISABLE_WAKEUP) - self._pybullet_client.changeVisualShape(self._kin_model,j, rgbaColor=[1,1,1,alpha]) - - + self._pybullet_client.changeVisualShape(self._kin_model, -1, rgbaColor=[1, 1, 1, alpha]) + for j in range(self._pybullet_client.getNumJoints(self._kin_model)): + self._pybullet_client.setCollisionFilterGroupMask(self._kin_model, + j, + collisionFilterGroup=0, + collisionFilterMask=0) + self._pybullet_client.changeDynamics( + self._kin_model, + j, + activationState=self._pybullet_client.ACTIVATION_STATE_SLEEP + + self._pybullet_client.ACTIVATION_STATE_ENABLE_SLEEPING + + self._pybullet_client.ACTIVATION_STATE_DISABLE_WAKEUP) + self._pybullet_client.changeVisualShape(self._kin_model, j, rgbaColor=[1, 1, 1, alpha]) + self._poseInterpolator = humanoid_pose_interpolator.HumanoidPoseInterpolator() - - for i in range (self._mocap_data.NumFrames()-1): + + for i in range(self._mocap_data.NumFrames() - 1): frameData = self._mocap_data._motion_data['Frames'][i] self._poseInterpolator.PostProcessMotionData(frameData) - + self._stablePD = pd_controller_stable.PDControllerStableMultiDof(self._pybullet_client) self._timeStep = timeStep - self._kpOrg = [0,0,0,0,0,0,0,1000,1000,1000,1000,100,100,100,100,500,500,500,500,500,400,400,400,400,400,400,400,400,300,500,500,500,500,500,400,400,400,400,400,400,400,400,300] - self._kdOrg = [0,0,0,0,0,0,0,100,100,100,100,10,10,10,10,50,50,50,50,50,40,40,40,40,40,40,40,40,30,50,50,50,50,50,40,40,40,40,40,40,40,40,30] - - self._jointIndicesAll = [chest,neck, rightHip,rightKnee,rightAnkle,rightShoulder,rightElbow,leftHip,leftKnee,leftAnkle,leftShoulder,leftElbow] + self._kpOrg = [ + 0, 0, 0, 0, 0, 0, 0, 1000, 1000, 1000, 1000, 100, 100, 100, 100, 500, 500, 500, 500, 500, + 400, 400, 400, 400, 400, 400, 400, 400, 300, 500, 500, 500, 500, 500, 400, 400, 400, 400, + 400, 400, 400, 400, 300 + ] + self._kdOrg = [ + 0, 0, 0, 0, 0, 0, 0, 100, 100, 100, 100, 10, 10, 10, 10, 50, 50, 50, 50, 50, 40, 40, 40, + 40, 40, 40, 40, 40, 30, 50, 50, 50, 50, 50, 40, 40, 40, 40, 40, 40, 40, 40, 30 + ] + + self._jointIndicesAll = [ + chest, neck, rightHip, rightKnee, rightAnkle, rightShoulder, rightElbow, leftHip, leftKnee, + leftAnkle, leftShoulder, leftElbow + ] for j in self._jointIndicesAll: #self._pybullet_client.setJointMotorControlMultiDof(self._sim_model, j, self._pybullet_client.POSITION_CONTROL, force=[1,1,1]) - self._pybullet_client.setJointMotorControl2(self._sim_model, j, self._pybullet_client.POSITION_CONTROL, targetPosition=0, positionGain=0, targetVelocity=0,force=jointFrictionForce) - self._pybullet_client.setJointMotorControlMultiDof(self._sim_model, j, self._pybullet_client.POSITION_CONTROL,targetPosition=[0,0,0,1], targetVelocity=[0,0,0], positionGain=0,velocityGain=1,force=[jointFrictionForce,jointFrictionForce,jointFrictionForce]) - self._pybullet_client.setJointMotorControl2(self._kin_model, j, self._pybullet_client.POSITION_CONTROL, targetPosition=0, positionGain=0, targetVelocity=0,force=0) - self._pybullet_client.setJointMotorControlMultiDof(self._kin_model, j, self._pybullet_client.POSITION_CONTROL,targetPosition=[0,0,0,1], targetVelocity=[0,0,0], positionGain=0,velocityGain=1,force=[jointFrictionForce,jointFrictionForce,0]) - - self._jointDofCounts=[4,4,4,1,4,4,1,4,1,4,4,1] - + self._pybullet_client.setJointMotorControl2(self._sim_model, + j, + self._pybullet_client.POSITION_CONTROL, + targetPosition=0, + positionGain=0, + targetVelocity=0, + force=jointFrictionForce) + self._pybullet_client.setJointMotorControlMultiDof( + self._sim_model, + j, + self._pybullet_client.POSITION_CONTROL, + targetPosition=[0, 0, 0, 1], + targetVelocity=[0, 0, 0], + positionGain=0, + velocityGain=1, + force=[jointFrictionForce, jointFrictionForce, jointFrictionForce]) + self._pybullet_client.setJointMotorControl2(self._kin_model, + j, + self._pybullet_client.POSITION_CONTROL, + targetPosition=0, + positionGain=0, + targetVelocity=0, + force=0) + self._pybullet_client.setJointMotorControlMultiDof( + self._kin_model, + j, + self._pybullet_client.POSITION_CONTROL, + targetPosition=[0, 0, 0, 1], + targetVelocity=[0, 0, 0], + positionGain=0, + velocityGain=1, + force=[jointFrictionForce, jointFrictionForce, 0]) + + self._jointDofCounts = [4, 4, 4, 1, 4, 4, 1, 4, 1, 4, 4, 1] + #only those body parts/links are allowed to touch the ground, otherwise the episode terminates - self._allowed_body_parts=[5,11] - + self._allowed_body_parts = [5, 11] + #[x,y,z] base position and [x,y,z,w] base orientation! - self._totalDofs = 7 + self._totalDofs = 7 for dof in self._jointDofCounts: self._totalDofs += dof self.setSimTime(0) - + self.resetPose() - + def resetPose(self): #print("resetPose with self._frame=", self._frame, " and self._frameFraction=",self._frameFraction) pose = self.computePose(self._frameFraction) self.initializePose(self._poseInterpolator, self._sim_model, initBase=True) self.initializePose(self._poseInterpolator, self._kin_model, initBase=False) - - def initializePose(self, pose, phys_model,initBase, initializeVelocity = True): - + + def initializePose(self, pose, phys_model, initBase, initializeVelocity=True): + if initializeVelocity: if initBase: - self._pybullet_client.resetBasePositionAndOrientation(phys_model, pose._basePos, pose._baseOrn) + self._pybullet_client.resetBasePositionAndOrientation(phys_model, pose._basePos, + pose._baseOrn) self._pybullet_client.resetBaseVelocity(phys_model, pose._baseLinVel, pose._baseAngVel) - self._pybullet_client.resetJointStateMultiDof(phys_model,chest,pose._chestRot, pose._chestVel) - self._pybullet_client.resetJointStateMultiDof(phys_model,neck,pose._neckRot, pose._neckVel) - self._pybullet_client.resetJointStateMultiDof(phys_model,rightHip,pose._rightHipRot, pose._rightHipVel) - self._pybullet_client.resetJointStateMultiDof(phys_model,rightKnee,pose._rightKneeRot, pose._rightKneeVel) - self._pybullet_client.resetJointStateMultiDof(phys_model,rightAnkle,pose._rightAnkleRot, pose._rightAnkleVel) - self._pybullet_client.resetJointStateMultiDof(phys_model,rightShoulder,pose._rightShoulderRot, pose._rightShoulderVel) - self._pybullet_client.resetJointStateMultiDof(phys_model,rightElbow, pose._rightElbowRot, pose._rightElbowVel) - self._pybullet_client.resetJointStateMultiDof(phys_model,leftHip, pose._leftHipRot, pose._leftHipVel) - self._pybullet_client.resetJointStateMultiDof(phys_model,leftKnee, pose._leftKneeRot, pose._leftKneeVel) - self._pybullet_client.resetJointStateMultiDof(phys_model,leftAnkle, pose._leftAnkleRot, pose._leftAnkleVel) - self._pybullet_client.resetJointStateMultiDof(phys_model,leftShoulder, pose._leftShoulderRot, pose._leftShoulderVel) - self._pybullet_client.resetJointStateMultiDof(phys_model,leftElbow, pose._leftElbowRot, pose._leftElbowVel) + self._pybullet_client.resetJointStateMultiDof(phys_model, chest, pose._chestRot, + pose._chestVel) + self._pybullet_client.resetJointStateMultiDof(phys_model, neck, pose._neckRot, pose._neckVel) + self._pybullet_client.resetJointStateMultiDof(phys_model, rightHip, pose._rightHipRot, + pose._rightHipVel) + self._pybullet_client.resetJointStateMultiDof(phys_model, rightKnee, pose._rightKneeRot, + pose._rightKneeVel) + self._pybullet_client.resetJointStateMultiDof(phys_model, rightAnkle, pose._rightAnkleRot, + pose._rightAnkleVel) + self._pybullet_client.resetJointStateMultiDof(phys_model, rightShoulder, + pose._rightShoulderRot, pose._rightShoulderVel) + self._pybullet_client.resetJointStateMultiDof(phys_model, rightElbow, pose._rightElbowRot, + pose._rightElbowVel) + self._pybullet_client.resetJointStateMultiDof(phys_model, leftHip, pose._leftHipRot, + pose._leftHipVel) + self._pybullet_client.resetJointStateMultiDof(phys_model, leftKnee, pose._leftKneeRot, + pose._leftKneeVel) + self._pybullet_client.resetJointStateMultiDof(phys_model, leftAnkle, pose._leftAnkleRot, + pose._leftAnkleVel) + self._pybullet_client.resetJointStateMultiDof(phys_model, leftShoulder, + pose._leftShoulderRot, pose._leftShoulderVel) + self._pybullet_client.resetJointStateMultiDof(phys_model, leftElbow, pose._leftElbowRot, + pose._leftElbowVel) else: if initBase: - self._pybullet_client.resetBasePositionAndOrientation(phys_model, pose._basePos, pose._baseOrn) - self._pybullet_client.resetJointStateMultiDof(phys_model,chest,pose._chestRot, [0,0,0]) - self._pybullet_client.resetJointStateMultiDof(phys_model,neck,pose._neckRot, [0,0,0]) - self._pybullet_client.resetJointStateMultiDof(phys_model,rightHip,pose._rightHipRot, [0,0,0]) - self._pybullet_client.resetJointStateMultiDof(phys_model,rightKnee,pose._rightKneeRot, [0]) - self._pybullet_client.resetJointStateMultiDof(phys_model,rightAnkle,pose._rightAnkleRot, [0,0,0]) - self._pybullet_client.resetJointStateMultiDof(phys_model,rightShoulder,pose._rightShoulderRot, [0,0,0]) - self._pybullet_client.resetJointStateMultiDof(phys_model,rightElbow, pose._rightElbowRot, [0]) - self._pybullet_client.resetJointStateMultiDof(phys_model,leftHip, pose._leftHipRot, [0,0,0]) - self._pybullet_client.resetJointStateMultiDof(phys_model,leftKnee, pose._leftKneeRot, [0]) - self._pybullet_client.resetJointStateMultiDof(phys_model,leftAnkle, pose._leftAnkleRot, [0,0,0]) - self._pybullet_client.resetJointStateMultiDof(phys_model,leftShoulder, pose._leftShoulderRot, [0,0,0]) - self._pybullet_client.resetJointStateMultiDof(phys_model,leftElbow, pose._leftElbowRot, [0]) + self._pybullet_client.resetBasePositionAndOrientation(phys_model, pose._basePos, + pose._baseOrn) + self._pybullet_client.resetJointStateMultiDof(phys_model, chest, pose._chestRot, [0, 0, 0]) + self._pybullet_client.resetJointStateMultiDof(phys_model, neck, pose._neckRot, [0, 0, 0]) + self._pybullet_client.resetJointStateMultiDof(phys_model, rightHip, pose._rightHipRot, + [0, 0, 0]) + self._pybullet_client.resetJointStateMultiDof(phys_model, rightKnee, pose._rightKneeRot, [0]) + self._pybullet_client.resetJointStateMultiDof(phys_model, rightAnkle, pose._rightAnkleRot, + [0, 0, 0]) + self._pybullet_client.resetJointStateMultiDof(phys_model, rightShoulder, + pose._rightShoulderRot, [0, 0, 0]) + self._pybullet_client.resetJointStateMultiDof(phys_model, rightElbow, pose._rightElbowRot, + [0]) + self._pybullet_client.resetJointStateMultiDof(phys_model, leftHip, pose._leftHipRot, + [0, 0, 0]) + self._pybullet_client.resetJointStateMultiDof(phys_model, leftKnee, pose._leftKneeRot, [0]) + self._pybullet_client.resetJointStateMultiDof(phys_model, leftAnkle, pose._leftAnkleRot, + [0, 0, 0]) + self._pybullet_client.resetJointStateMultiDof(phys_model, leftShoulder, + pose._leftShoulderRot, [0, 0, 0]) + self._pybullet_client.resetJointStateMultiDof(phys_model, leftElbow, pose._leftElbowRot, [0]) def calcCycleCount(self, simTime, cycleTime): - phases = simTime / cycleTime; + phases = simTime / cycleTime count = math.floor(phases) loop = True #count = (loop) ? count : cMathUtil::Clamp(count, 0, 1); return count - def getCycleTime(self): keyFrameDuration = self._mocap_data.KeyFrameDuraction() - cycleTime = keyFrameDuration*(self._mocap_data.NumFrames()-1) + cycleTime = keyFrameDuration * (self._mocap_data.NumFrames() - 1) return cycleTime - + def setSimTime(self, t): self._simTime = t #print("SetTimeTime time =",t) @@ -147,163 +225,266 @@ class HumanoidStablePD(object): #print("self._motion_data.NumFrames()=",self._mocap_data.NumFrames()) self._cycleCount = self.calcCycleCount(t, cycleTime) #print("cycles=",cycles) - frameTime = t - self._cycleCount*cycleTime - if (frameTime<0): + frameTime = t - self._cycleCount * cycleTime + if (frameTime < 0): frameTime += cycleTime - - #print("keyFrameDuration=",keyFrameDuration) + + #print("keyFrameDuration=",keyFrameDuration) #print("frameTime=",frameTime) - self._frame = int(frameTime/keyFrameDuration) + self._frame = int(frameTime / keyFrameDuration) #print("self._frame=",self._frame) - - self._frameNext = self._frame+1 - if (self._frameNext >= self._mocap_data.NumFrames()): + + self._frameNext = self._frame + 1 + if (self._frameNext >= self._mocap_data.NumFrames()): self._frameNext = self._frame - self._frameFraction = (frameTime - self._frame*keyFrameDuration)/(keyFrameDuration) - - + self._frameFraction = (frameTime - self._frame * keyFrameDuration) / (keyFrameDuration) + def computeCycleOffset(self): - firstFrame=0 - lastFrame = self._mocap_data.NumFrames()-1 + firstFrame = 0 + lastFrame = self._mocap_data.NumFrames() - 1 frameData = self._mocap_data._motion_data['Frames'][0] frameDataNext = self._mocap_data._motion_data['Frames'][lastFrame] - - basePosStart = [frameData[1],frameData[2],frameData[3]] - basePosEnd = [frameDataNext[1],frameDataNext[2],frameDataNext[3]] - self._cycleOffset = [basePosEnd[0]-basePosStart[0],basePosEnd[1]-basePosStart[1],basePosEnd[2]-basePosStart[2]] + + basePosStart = [frameData[1], frameData[2], frameData[3]] + basePosEnd = [frameDataNext[1], frameDataNext[2], frameDataNext[3]] + self._cycleOffset = [ + basePosEnd[0] - basePosStart[0], basePosEnd[1] - basePosStart[1], + basePosEnd[2] - basePosStart[2] + ] return self._cycleOffset - + def computePose(self, frameFraction): frameData = self._mocap_data._motion_data['Frames'][self._frame] frameDataNext = self._mocap_data._motion_data['Frames'][self._frameNext] - + self._poseInterpolator.Slerp(frameFraction, frameData, frameDataNext, self._pybullet_client) #print("self._poseInterpolator.Slerp(", frameFraction,")=", pose) self.computeCycleOffset() oldPos = self._poseInterpolator._basePos - self._poseInterpolator._basePos = [oldPos[0]+self._cycleCount*self._cycleOffset[0],oldPos[1]+self._cycleCount*self._cycleOffset[1],oldPos[2]+self._cycleCount*self._cycleOffset[2]] + self._poseInterpolator._basePos = [ + oldPos[0] + self._cycleCount * self._cycleOffset[0], + oldPos[1] + self._cycleCount * self._cycleOffset[1], + oldPos[2] + self._cycleCount * self._cycleOffset[2] + ] pose = self._poseInterpolator.GetPose() - - return pose + return pose def convertActionToPose(self, action): pose = self._poseInterpolator.ConvertFromAction(self._pybullet_client, action) - return pose + return pose def computePDForces(self, desiredPositions, desiredVelocities, maxForces): - if desiredVelocities==None: - desiredVelocities = [0]*self._totalDofs - - - taus = self._stablePD.computePD(bodyUniqueId=self._sim_model, - jointIndices = self._jointIndicesAll, - desiredPositions = desiredPositions, - desiredVelocities = desiredVelocities, - kps = self._kpOrg, - kds = self._kdOrg, - maxForces = maxForces, - timeStep=self._timeStep) + if desiredVelocities == None: + desiredVelocities = [0] * self._totalDofs + + taus = self._stablePD.computePD(bodyUniqueId=self._sim_model, + jointIndices=self._jointIndicesAll, + desiredPositions=desiredPositions, + desiredVelocities=desiredVelocities, + kps=self._kpOrg, + kds=self._kdOrg, + maxForces=maxForces, + timeStep=self._timeStep) return taus def applyPDForces(self, taus): - dofIndex=7 + dofIndex = 7 scaling = 1 - for index in range (len(self._jointIndicesAll)): + for index in range(len(self._jointIndicesAll)): jointIndex = self._jointIndicesAll[index] - if self._jointDofCounts[index]==4: - force=[scaling*taus[dofIndex+0],scaling*taus[dofIndex+1],scaling*taus[dofIndex+2]] - #print("force[", jointIndex,"]=",force) - self._pybullet_client.setJointMotorControlMultiDof(self._sim_model,jointIndex,self._pybullet_client.TORQUE_CONTROL,force=force) - if self._jointDofCounts[index]==1: - force=[scaling*taus[dofIndex]] - #print("force[", jointIndex,"]=",force) - self._pybullet_client.setJointMotorControlMultiDof(self._sim_model, jointIndex, controlMode=self._pybullet_client.TORQUE_CONTROL, force=force) - dofIndex+=self._jointDofCounts[index] - + if self._jointDofCounts[index] == 4: + force = [ + scaling * taus[dofIndex + 0], scaling * taus[dofIndex + 1], + scaling * taus[dofIndex + 2] + ] + #print("force[", jointIndex,"]=",force) + self._pybullet_client.setJointMotorControlMultiDof(self._sim_model, + jointIndex, + self._pybullet_client.TORQUE_CONTROL, + force=force) + if self._jointDofCounts[index] == 1: + force = [scaling * taus[dofIndex]] + #print("force[", jointIndex,"]=",force) + self._pybullet_client.setJointMotorControlMultiDof( + self._sim_model, + jointIndex, + controlMode=self._pybullet_client.TORQUE_CONTROL, + force=force) + dofIndex += self._jointDofCounts[index] + def setJointMotors(self, desiredPositions, maxForces): controlMode = self._pybullet_client.POSITION_CONTROL - startIndex=7 - chest=1 - neck=2 - rightHip=3 - rightKnee=4 - rightAnkle=5 - rightShoulder=6 - rightElbow=7 - leftHip=9 - leftKnee=10 - leftAnkle=11 - leftShoulder=12 - leftElbow=13 + startIndex = 7 + chest = 1 + neck = 2 + rightHip = 3 + rightKnee = 4 + rightAnkle = 5 + rightShoulder = 6 + rightElbow = 7 + leftHip = 9 + leftKnee = 10 + leftAnkle = 11 + leftShoulder = 12 + leftElbow = 13 kp = 0.2 - - forceScale=1 + + forceScale = 1 #self._jointDofCounts=[4,4,4,1,4,4,1,4,1,4,4,1] - maxForce = [forceScale*maxForces[startIndex],forceScale*maxForces[startIndex+1],forceScale*maxForces[startIndex+2],forceScale*maxForces[startIndex+3]] - startIndex+=4 - self._pybullet_client.setJointMotorControlMultiDof(self._sim_model,chest,controlMode, targetPosition=self._poseInterpolator._chestRot,positionGain=kp, force=maxForce) - maxForce = [maxForces[startIndex],maxForces[startIndex+1],maxForces[startIndex+2],maxForces[startIndex+3]] - startIndex+=4 - self._pybullet_client.setJointMotorControlMultiDof(self._sim_model,neck,controlMode,targetPosition=self._poseInterpolator._neckRot,positionGain=kp, force=maxForce) - maxForce = [maxForces[startIndex],maxForces[startIndex+1],maxForces[startIndex+2],maxForces[startIndex+3]] - startIndex+=4 - self._pybullet_client.setJointMotorControlMultiDof(self._sim_model,rightHip,controlMode,targetPosition=self._poseInterpolator._rightHipRot,positionGain=kp, force=maxForce) - maxForce = [forceScale*maxForces[startIndex]] - startIndex+=1 - self._pybullet_client.setJointMotorControlMultiDof(self._sim_model,rightKnee,controlMode,targetPosition=self._poseInterpolator._rightKneeRot,positionGain=kp, force=maxForce) - maxForce = [maxForces[startIndex],maxForces[startIndex+1],maxForces[startIndex+2],maxForces[startIndex+3]] - startIndex+=4 - self._pybullet_client.setJointMotorControlMultiDof(self._sim_model,rightAnkle,controlMode,targetPosition=self._poseInterpolator._rightAnkleRot,positionGain=kp, force=maxForce) - maxForce = [forceScale*maxForces[startIndex],forceScale*maxForces[startIndex+1],forceScale*maxForces[startIndex+2],forceScale*maxForces[startIndex+3]] - startIndex+=4 - maxForce = [forceScale*maxForces[startIndex]] - startIndex+=1 - self._pybullet_client.setJointMotorControlMultiDof(self._sim_model,rightElbow, controlMode,targetPosition=self._poseInterpolator._rightElbowRot,positionGain=kp, force=maxForce) - maxForce = [maxForces[startIndex],maxForces[startIndex+1],maxForces[startIndex+2],maxForces[startIndex+3]] - startIndex+=4 - self._pybullet_client.setJointMotorControlMultiDof(self._sim_model,leftHip, controlMode,targetPosition=self._poseInterpolator._leftHipRot,positionGain=kp, force=maxForce) - maxForce = [forceScale*maxForces[startIndex]] - startIndex+=1 - self._pybullet_client.setJointMotorControlMultiDof(self._sim_model,leftKnee, controlMode,targetPosition=self._poseInterpolator._leftKneeRot,positionGain=kp, force=maxForce) - maxForce = [maxForces[startIndex],maxForces[startIndex+1],maxForces[startIndex+2],maxForces[startIndex+3]] - startIndex+=4 - self._pybullet_client.setJointMotorControlMultiDof(self._sim_model,leftAnkle, controlMode,targetPosition=self._poseInterpolator._leftAnkleRot,positionGain=kp, force=maxForce) - maxForce = [maxForces[startIndex],maxForces[startIndex+1],maxForces[startIndex+2],maxForces[startIndex+3]] - startIndex+=4 - self._pybullet_client.setJointMotorControlMultiDof(self._sim_model,leftShoulder, controlMode,targetPosition=self._poseInterpolator._leftShoulderRot,positionGain=kp, force=maxForce) - maxForce = [forceScale*maxForces[startIndex]] - startIndex+=1 - self._pybullet_client.setJointMotorControlMultiDof(self._sim_model,leftElbow, controlMode,targetPosition=self._poseInterpolator._leftElbowRot,positionGain=kp, force=maxForce) + maxForce = [ + forceScale * maxForces[startIndex], forceScale * maxForces[startIndex + 1], + forceScale * maxForces[startIndex + 2], forceScale * maxForces[startIndex + 3] + ] + startIndex += 4 + self._pybullet_client.setJointMotorControlMultiDof( + self._sim_model, + chest, + controlMode, + targetPosition=self._poseInterpolator._chestRot, + positionGain=kp, + force=maxForce) + maxForce = [ + maxForces[startIndex], maxForces[startIndex + 1], maxForces[startIndex + 2], + maxForces[startIndex + 3] + ] + startIndex += 4 + self._pybullet_client.setJointMotorControlMultiDof( + self._sim_model, + neck, + controlMode, + targetPosition=self._poseInterpolator._neckRot, + positionGain=kp, + force=maxForce) + maxForce = [ + maxForces[startIndex], maxForces[startIndex + 1], maxForces[startIndex + 2], + maxForces[startIndex + 3] + ] + startIndex += 4 + self._pybullet_client.setJointMotorControlMultiDof( + self._sim_model, + rightHip, + controlMode, + targetPosition=self._poseInterpolator._rightHipRot, + positionGain=kp, + force=maxForce) + maxForce = [forceScale * maxForces[startIndex]] + startIndex += 1 + self._pybullet_client.setJointMotorControlMultiDof( + self._sim_model, + rightKnee, + controlMode, + targetPosition=self._poseInterpolator._rightKneeRot, + positionGain=kp, + force=maxForce) + maxForce = [ + maxForces[startIndex], maxForces[startIndex + 1], maxForces[startIndex + 2], + maxForces[startIndex + 3] + ] + startIndex += 4 + self._pybullet_client.setJointMotorControlMultiDof( + self._sim_model, + rightAnkle, + controlMode, + targetPosition=self._poseInterpolator._rightAnkleRot, + positionGain=kp, + force=maxForce) + maxForce = [ + forceScale * maxForces[startIndex], forceScale * maxForces[startIndex + 1], + forceScale * maxForces[startIndex + 2], forceScale * maxForces[startIndex + 3] + ] + startIndex += 4 + maxForce = [forceScale * maxForces[startIndex]] + startIndex += 1 + self._pybullet_client.setJointMotorControlMultiDof( + self._sim_model, + rightElbow, + controlMode, + targetPosition=self._poseInterpolator._rightElbowRot, + positionGain=kp, + force=maxForce) + maxForce = [ + maxForces[startIndex], maxForces[startIndex + 1], maxForces[startIndex + 2], + maxForces[startIndex + 3] + ] + startIndex += 4 + self._pybullet_client.setJointMotorControlMultiDof( + self._sim_model, + leftHip, + controlMode, + targetPosition=self._poseInterpolator._leftHipRot, + positionGain=kp, + force=maxForce) + maxForce = [forceScale * maxForces[startIndex]] + startIndex += 1 + self._pybullet_client.setJointMotorControlMultiDof( + self._sim_model, + leftKnee, + controlMode, + targetPosition=self._poseInterpolator._leftKneeRot, + positionGain=kp, + force=maxForce) + maxForce = [ + maxForces[startIndex], maxForces[startIndex + 1], maxForces[startIndex + 2], + maxForces[startIndex + 3] + ] + startIndex += 4 + self._pybullet_client.setJointMotorControlMultiDof( + self._sim_model, + leftAnkle, + controlMode, + targetPosition=self._poseInterpolator._leftAnkleRot, + positionGain=kp, + force=maxForce) + maxForce = [ + maxForces[startIndex], maxForces[startIndex + 1], maxForces[startIndex + 2], + maxForces[startIndex + 3] + ] + startIndex += 4 + self._pybullet_client.setJointMotorControlMultiDof( + self._sim_model, + leftShoulder, + controlMode, + targetPosition=self._poseInterpolator._leftShoulderRot, + positionGain=kp, + force=maxForce) + maxForce = [forceScale * maxForces[startIndex]] + startIndex += 1 + self._pybullet_client.setJointMotorControlMultiDof( + self._sim_model, + leftElbow, + controlMode, + targetPosition=self._poseInterpolator._leftElbowRot, + positionGain=kp, + force=maxForce) #print("startIndex=",startIndex) - + def getPhase(self): keyFrameDuration = self._mocap_data.KeyFrameDuraction() - cycleTime = keyFrameDuration*(self._mocap_data.NumFrames()-1) + cycleTime = keyFrameDuration * (self._mocap_data.NumFrames() - 1) phase = self._simTime / cycleTime - phase = math.fmod(phase,1.0) - if (phase<0): + phase = math.fmod(phase, 1.0) + if (phase < 0): phase += 1 return phase def buildHeadingTrans(self, rootOrn): #align root transform 'forward' with world-space x axis eul = self._pybullet_client.getEulerFromQuaternion(rootOrn) - refDir = [1,0,0] + refDir = [1, 0, 0] rotVec = self._pybullet_client.rotateVector(rootOrn, refDir) heading = math.atan2(-rotVec[2], rotVec[0]) - heading2=eul[1] + heading2 = eul[1] #print("heading=",heading) - headingOrn = self._pybullet_client.getQuaternionFromAxisAngle([0,1,0],-heading) + headingOrn = self._pybullet_client.getQuaternionFromAxisAngle([0, 1, 0], -heading) return headingOrn - def buildOriginTrans(self): - rootPos,rootOrn = self._pybullet_client.getBasePositionAndOrientation(self._sim_model) - + rootPos, rootOrn = self._pybullet_client.getBasePositionAndOrientation(self._sim_model) + #print("rootPos=",rootPos, " rootOrn=",rootOrn) - invRootPos=[-rootPos[0], 0, -rootPos[2]] + invRootPos = [-rootPos[0], 0, -rootPos[2]] #invOrigTransPos, invOrigTransOrn = self._pybullet_client.invertTransform(rootPos,rootOrn) headingOrn = self.buildHeadingTrans(rootOrn) #print("headingOrn=",headingOrn) @@ -311,8 +492,11 @@ class HumanoidStablePD(object): #print("headingMat=",headingMat) #dummy, rootOrnWithoutHeading = self._pybullet_client.multiplyTransforms([0,0,0],headingOrn, [0,0,0], rootOrn) #dummy, invOrigTransOrn = self._pybullet_client.multiplyTransforms([0,0,0],rootOrnWithoutHeading, invOrigTransPos, invOrigTransOrn) - - invOrigTransPos, invOrigTransOrn = self._pybullet_client.multiplyTransforms( [0,0,0],headingOrn, invRootPos,[0,0,0,1]) + + invOrigTransPos, invOrigTransOrn = self._pybullet_client.multiplyTransforms([0, 0, 0], + headingOrn, + invRootPos, + [0, 0, 0, 1]) #print("invOrigTransPos=",invOrigTransPos) #print("invOrigTransOrn=",invOrigTransOrn) invOrigTransMat = self._pybullet_client.getMatrixFromQuaternion(invOrigTransOrn) @@ -325,106 +509,115 @@ class HumanoidStablePD(object): phase = self.getPhase() #print("phase=",phase) stateVector.append(phase) - - rootTransPos, rootTransOrn=self.buildOriginTrans() - basePos,baseOrn = self._pybullet_client.getBasePositionAndOrientation(self._sim_model) - - rootPosRel, dummy = self._pybullet_client.multiplyTransforms(rootTransPos, rootTransOrn, basePos,[0,0,0,1]) + + rootTransPos, rootTransOrn = self.buildOriginTrans() + basePos, baseOrn = self._pybullet_client.getBasePositionAndOrientation(self._sim_model) + + rootPosRel, dummy = self._pybullet_client.multiplyTransforms(rootTransPos, rootTransOrn, + basePos, [0, 0, 0, 1]) #print("!!!rootPosRel =",rootPosRel ) #print("rootTransPos=",rootTransPos) #print("basePos=",basePos) - localPos,localOrn = self._pybullet_client.multiplyTransforms( rootTransPos, rootTransOrn , basePos,baseOrn ) - - localPos=[localPos[0]-rootPosRel[0],localPos[1]-rootPosRel[1],localPos[2]-rootPosRel[2]] + localPos, localOrn = self._pybullet_client.multiplyTransforms(rootTransPos, rootTransOrn, + basePos, baseOrn) + + localPos = [ + localPos[0] - rootPosRel[0], localPos[1] - rootPosRel[1], localPos[2] - rootPosRel[2] + ] #print("localPos=",localPos) - + stateVector.append(rootPosRel[1]) - + #self.pb2dmJoints=[0,1,2,9,10,11,3,4,5,12,13,14,6,7,8] - self.pb2dmJoints=[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14] - - for pbJoint in range (self._pybullet_client.getNumJoints(self._sim_model)): + self.pb2dmJoints = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14] + + for pbJoint in range(self._pybullet_client.getNumJoints(self._sim_model)): j = self.pb2dmJoints[pbJoint] #print("joint order:",j) ls = self._pybullet_client.getLinkState(self._sim_model, j, computeForwardKinematics=True) linkPos = ls[0] linkOrn = ls[1] - linkPosLocal, linkOrnLocal = self._pybullet_client.multiplyTransforms(rootTransPos, rootTransOrn, linkPos,linkOrn) - if (linkOrnLocal[3]<0): - linkOrnLocal=[-linkOrnLocal[0],-linkOrnLocal[1],-linkOrnLocal[2],-linkOrnLocal[3]] - linkPosLocal=[linkPosLocal[0]-rootPosRel[0],linkPosLocal[1]-rootPosRel[1],linkPosLocal[2]-rootPosRel[2]] + linkPosLocal, linkOrnLocal = self._pybullet_client.multiplyTransforms( + rootTransPos, rootTransOrn, linkPos, linkOrn) + if (linkOrnLocal[3] < 0): + linkOrnLocal = [-linkOrnLocal[0], -linkOrnLocal[1], -linkOrnLocal[2], -linkOrnLocal[3]] + linkPosLocal = [ + linkPosLocal[0] - rootPosRel[0], linkPosLocal[1] - rootPosRel[1], + linkPosLocal[2] - rootPosRel[2] + ] for l in linkPosLocal: stateVector.append(l) #re-order the quaternion, DeepMimic uses w,x,y,z - - if (linkOrnLocal[3]<0): - linkOrnLocal[0]*=-1 - linkOrnLocal[1]*=-1 - linkOrnLocal[2]*=-1 - linkOrnLocal[3]*=-1 - + + if (linkOrnLocal[3] < 0): + linkOrnLocal[0] *= -1 + linkOrnLocal[1] *= -1 + linkOrnLocal[2] *= -1 + linkOrnLocal[3] *= -1 + stateVector.append(linkOrnLocal[3]) stateVector.append(linkOrnLocal[0]) stateVector.append(linkOrnLocal[1]) stateVector.append(linkOrnLocal[2]) - - - for pbJoint in range (self._pybullet_client.getNumJoints(self._sim_model)): + + for pbJoint in range(self._pybullet_client.getNumJoints(self._sim_model)): j = self.pb2dmJoints[pbJoint] ls = self._pybullet_client.getLinkState(self._sim_model, j, computeLinkVelocity=True) linkLinVel = ls[6] linkAngVel = ls[7] - linkLinVelLocal , unused = self._pybullet_client.multiplyTransforms([0,0,0], rootTransOrn, linkLinVel,[0,0,0,1]) + linkLinVelLocal, unused = self._pybullet_client.multiplyTransforms([0, 0, 0], rootTransOrn, + linkLinVel, [0, 0, 0, 1]) #linkLinVelLocal=[linkLinVelLocal[0]-rootPosRel[0],linkLinVelLocal[1]-rootPosRel[1],linkLinVelLocal[2]-rootPosRel[2]] - linkAngVelLocal ,unused = self._pybullet_client.multiplyTransforms([0,0,0], rootTransOrn, linkAngVel,[0,0,0,1]) - + linkAngVelLocal, unused = self._pybullet_client.multiplyTransforms([0, 0, 0], rootTransOrn, + linkAngVel, [0, 0, 0, 1]) + for l in linkLinVelLocal: stateVector.append(l) for l in linkAngVelLocal: stateVector.append(l) - - #print("stateVector len=",len(stateVector)) + + #print("stateVector len=",len(stateVector)) #for st in range (len(stateVector)): # print("state[",st,"]=",stateVector[st]) return stateVector - + def terminates(self): #check if any non-allowed body part hits the ground - terminates=False + terminates = False pts = self._pybullet_client.getContactPoints() for p in pts: part = -1 #ignore self-collision - if (p[1]==p[2]): + if (p[1] == p[2]): continue - if (p[1]==self._sim_model): - part=p[3] - if (p[2]==self._sim_model): - part=p[4] - if (part >=0 and part not in self._allowed_body_parts): + if (p[1] == self._sim_model): + part = p[3] + if (p[2] == self._sim_model): + part = p[4] + if (part >= 0 and part not in self._allowed_body_parts): #print("terminating part:", part) - terminates=True - + terminates = True + return terminates - + def quatMul(self, q1, q2): - return [ q1[3] * q2[0] + q1[0] * q2[3] + q1[1] * q2[2] - q1[2] * q2[1], - q1[3] * q2[1] + q1[1] * q2[3] + q1[2] * q2[0] - q1[0] * q2[2], - q1[3] * q2[2] + q1[2] * q2[3] + q1[0] * q2[1] - q1[1] * q2[0], - q1[3] * q2[3] - q1[0] * q2[0] - q1[1] * q2[1] - q1[2] * q2[2]] - + return [ + q1[3] * q2[0] + q1[0] * q2[3] + q1[1] * q2[2] - q1[2] * q2[1], + q1[3] * q2[1] + q1[1] * q2[3] + q1[2] * q2[0] - q1[0] * q2[2], + q1[3] * q2[2] + q1[2] * q2[3] + q1[0] * q2[1] - q1[1] * q2[0], + q1[3] * q2[3] - q1[0] * q2[0] - q1[1] * q2[1] - q1[2] * q2[2] + ] + def calcRootAngVelErr(self, vel0, vel1): - diff = [vel0[0]-vel1[0],vel0[1]-vel1[1], vel0[2]-vel1[2]] - return diff[0]*diff[0]+diff[1]*diff[1]+diff[2]*diff[2] + diff = [vel0[0] - vel1[0], vel0[1] - vel1[1], vel0[2] - vel1[2]] + return diff[0] * diff[0] + diff[1] * diff[1] + diff[2] * diff[2] - - - def calcRootRotDiff(self,orn0, orn1): - orn0Conj = [-orn0[0],-orn0[1],-orn0[2],orn0[3]] + def calcRootRotDiff(self, orn0, orn1): + orn0Conj = [-orn0[0], -orn0[1], -orn0[2], orn0[3]] q_diff = self.quatMul(orn1, orn0Conj) - axis,angle = self._pybullet_client.getAxisAngleFromQuaternion(q_diff) - return angle*angle - + axis, angle = self._pybullet_client.getAxisAngleFromQuaternion(q_diff) + return angle * angle + def getReward(self, pose): #from DeepMimic double cSceneImitate::CalcRewardImitate #todo: compensate for ground height in some parts, once we move to non-flat terrain @@ -432,7 +625,7 @@ class HumanoidStablePD(object): vel_w = 0.05 end_eff_w = 0.15 root_w = 0.2 - com_w = 0 #0.1 + com_w = 0 #0.1 total_w = pose_w + vel_w + end_eff_w + root_w + com_w pose_w /= total_w @@ -456,9 +649,9 @@ class HumanoidStablePD(object): root_err = 0 com_err = 0 heading_err = 0 - + #create a mimic reward, comparing the dynamics humanoid with a kinematic one - + #pose = self.InitializePoseFromMotionData() #print("self._kin_model=",self._kin_model) #print("kinematicHumanoid #joints=",self._pybullet_client.getNumJoints(self._kin_model)) @@ -487,91 +680,102 @@ class HumanoidStablePD(object): #tVector root_ang_vel0 = cKinTree::GetRootAngVel(joint_mat, vel0); #tVector root_ang_vel1 = cKinTree::GetRootAngVel(joint_mat, vel1); - mJointWeights = [0.20833,0.10416, 0.0625, 0.10416, - 0.0625, 0.041666666666666671, 0.0625, 0.0416, - 0.00, 0.10416, 0.0625, 0.0416, 0.0625, 0.0416, 0.0000] - + mJointWeights = [ + 0.20833, 0.10416, 0.0625, 0.10416, 0.0625, 0.041666666666666671, 0.0625, 0.0416, 0.00, + 0.10416, 0.0625, 0.0416, 0.0625, 0.0416, 0.0000 + ] + num_end_effs = 0 num_joints = 15 - + root_rot_w = mJointWeights[root_id] - rootPosSim,rootOrnSim = self._pybullet_client.getBasePositionAndOrientation(self._sim_model) - rootPosKin ,rootOrnKin = self._pybullet_client.getBasePositionAndOrientation(self._kin_model) + rootPosSim, rootOrnSim = self._pybullet_client.getBasePositionAndOrientation(self._sim_model) + rootPosKin, rootOrnKin = self._pybullet_client.getBasePositionAndOrientation(self._kin_model) linVelSim, angVelSim = self._pybullet_client.getBaseVelocity(self._sim_model) linVelKin, angVelKin = self._pybullet_client.getBaseVelocity(self._kin_model) - - - root_rot_err = self.calcRootRotDiff(rootOrnSim,rootOrnKin) + + root_rot_err = self.calcRootRotDiff(rootOrnSim, rootOrnKin) pose_err += root_rot_w * root_rot_err - - - root_vel_diff = [linVelSim[0]-linVelKin[0],linVelSim[1]-linVelKin[1],linVelSim[2]-linVelKin[2]] - root_vel_err = root_vel_diff[0]*root_vel_diff[0]+root_vel_diff[1]*root_vel_diff[1]+root_vel_diff[2]*root_vel_diff[2] - - root_ang_vel_err = self.calcRootAngVelErr( angVelSim, angVelKin) + + root_vel_diff = [ + linVelSim[0] - linVelKin[0], linVelSim[1] - linVelKin[1], linVelSim[2] - linVelKin[2] + ] + root_vel_err = root_vel_diff[0] * root_vel_diff[0] + root_vel_diff[1] * root_vel_diff[ + 1] + root_vel_diff[2] * root_vel_diff[2] + + root_ang_vel_err = self.calcRootAngVelErr(angVelSim, angVelKin) vel_err += root_rot_w * root_ang_vel_err - for j in range (num_joints): + for j in range(num_joints): curr_pose_err = 0 curr_vel_err = 0 - w = mJointWeights[j]; - + w = mJointWeights[j] + simJointInfo = self._pybullet_client.getJointStateMultiDof(self._sim_model, j) - + #print("simJointInfo.pos=",simJointInfo[0]) #print("simJointInfo.vel=",simJointInfo[1]) - kinJointInfo = self._pybullet_client.getJointStateMultiDof(self._kin_model,j) + kinJointInfo = self._pybullet_client.getJointStateMultiDof(self._kin_model, j) #print("kinJointInfo.pos=",kinJointInfo[0]) #print("kinJointInfo.vel=",kinJointInfo[1]) - if (len(simJointInfo[0])==1): - angle = simJointInfo[0][0]-kinJointInfo[0][0] - curr_pose_err = angle*angle - velDiff = simJointInfo[1][0]-kinJointInfo[1][0] - curr_vel_err = velDiff*velDiff - if (len(simJointInfo[0])==4): + if (len(simJointInfo[0]) == 1): + angle = simJointInfo[0][0] - kinJointInfo[0][0] + curr_pose_err = angle * angle + velDiff = simJointInfo[1][0] - kinJointInfo[1][0] + curr_vel_err = velDiff * velDiff + if (len(simJointInfo[0]) == 4): #print("quaternion diff") - diffQuat = self._pybullet_client.getDifferenceQuaternion(simJointInfo[0],kinJointInfo[0]) - axis,angle = self._pybullet_client.getAxisAngleFromQuaternion(diffQuat) - curr_pose_err = angle*angle - diffVel = [simJointInfo[1][0]-kinJointInfo[1][0],simJointInfo[1][1]-kinJointInfo[1][1],simJointInfo[1][2]-kinJointInfo[1][2]] - curr_vel_err = diffVel[0]*diffVel[0]+diffVel[1]*diffVel[1]+diffVel[2]*diffVel[2] - - + diffQuat = self._pybullet_client.getDifferenceQuaternion(simJointInfo[0], kinJointInfo[0]) + axis, angle = self._pybullet_client.getAxisAngleFromQuaternion(diffQuat) + curr_pose_err = angle * angle + diffVel = [ + simJointInfo[1][0] - kinJointInfo[1][0], simJointInfo[1][1] - kinJointInfo[1][1], + simJointInfo[1][2] - kinJointInfo[1][2] + ] + curr_vel_err = diffVel[0] * diffVel[0] + diffVel[1] * diffVel[1] + diffVel[2] * diffVel[2] + pose_err += w * curr_pose_err vel_err += w * curr_vel_err - + is_end_eff = j in self._end_effectors if is_end_eff: - + linkStateSim = self._pybullet_client.getLinkState(self._sim_model, j) linkStateKin = self._pybullet_client.getLinkState(self._kin_model, j) linkPosSim = linkStateSim[0] linkPosKin = linkStateKin[0] - linkPosDiff = [linkPosSim[0]-linkPosKin[0],linkPosSim[1]-linkPosKin[1],linkPosSim[2]-linkPosKin[2]] - curr_end_err = linkPosDiff[0]*linkPosDiff[0]+linkPosDiff[1]*linkPosDiff[1]+linkPosDiff[2]*linkPosDiff[2] + linkPosDiff = [ + linkPosSim[0] - linkPosKin[0], linkPosSim[1] - linkPosKin[1], + linkPosSim[2] - linkPosKin[2] + ] + curr_end_err = linkPosDiff[0] * linkPosDiff[0] + linkPosDiff[1] * linkPosDiff[ + 1] + linkPosDiff[2] * linkPosDiff[2] end_eff_err += curr_end_err - num_end_effs+=1 - + num_end_effs += 1 + if (num_end_effs > 0): end_eff_err /= num_end_effs - + #double root_ground_h0 = mGround->SampleHeight(sim_char.GetRootPos()) #double root_ground_h1 = kin_char.GetOriginPos()[1] #root_pos0[1] -= root_ground_h0 #root_pos1[1] -= root_ground_h1 - root_pos_diff = [rootPosSim[0]-rootPosKin[0],rootPosSim[1]-rootPosKin[1],rootPosSim[2]-rootPosKin[2]] - root_pos_err = root_pos_diff[0]*root_pos_diff[0]+root_pos_diff[1]*root_pos_diff[1]+root_pos_diff[2]*root_pos_diff[2] - # + root_pos_diff = [ + rootPosSim[0] - rootPosKin[0], rootPosSim[1] - rootPosKin[1], rootPosSim[2] - rootPosKin[2] + ] + root_pos_err = root_pos_diff[0] * root_pos_diff[0] + root_pos_diff[1] * root_pos_diff[ + 1] + root_pos_diff[2] * root_pos_diff[2] + # #root_rot_err = cMathUtil::QuatDiffTheta(root_rot0, root_rot1) #root_rot_err *= root_rot_err #root_vel_err = (root_vel1 - root_vel0).squaredNorm() #root_ang_vel_err = (root_ang_vel1 - root_ang_vel0).squaredNorm() - root_err = root_pos_err + 0.1 * root_rot_err+ 0.01 * root_vel_err+ 0.001 * root_ang_vel_err - + root_err = root_pos_err + 0.1 * root_rot_err + 0.01 * root_vel_err + 0.001 * root_ang_vel_err + #com_err = 0.1 * (com_vel1_world - com_vel0_world).squaredNorm() - + #print("pose_err=",pose_err) #print("vel_err=",vel_err) pose_reward = math.exp(-err_scale * pose_scale * pose_err) @@ -582,7 +786,6 @@ class HumanoidStablePD(object): reward = pose_w * pose_reward + vel_w * vel_reward + end_eff_w * end_eff_reward + root_w * root_reward + com_w * com_reward - # pose_reward,vel_reward,end_eff_reward, root_reward, com_reward); #print("reward=",reward) #print("pose_reward=",pose_reward) @@ -590,5 +793,5 @@ class HumanoidStablePD(object): #print("end_eff_reward=",end_eff_reward) #print("root_reward=",root_reward) #print("com_reward=",com_reward) - + return reward diff --git a/examples/pybullet/gym/pybullet_envs/deep_mimic/env/motion_capture_data.py b/examples/pybullet/gym/pybullet_envs/deep_mimic/env/motion_capture_data.py index ea9399899..f14ef9e78 100644 --- a/examples/pybullet/gym/pybullet_envs/deep_mimic/env/motion_capture_data.py +++ b/examples/pybullet/gym/pybullet_envs/deep_mimic/env/motion_capture_data.py @@ -1,7 +1,9 @@ import json import math + class MotionCaptureData(object): + def __init__(self): self.Reset() @@ -13,30 +15,33 @@ class MotionCaptureData(object): self._motion_data = json.load(f) def NumFrames(self): - return len(self._motion_data['Frames']) + return len(self._motion_data['Frames']) def KeyFrameDuraction(self): - return self._motion_data['Frames'][0][0] - + return self._motion_data['Frames'][0][0] + def getCycleTime(self): keyFrameDuration = self.KeyFrameDuraction() - cycleTime = keyFrameDuration*(self.NumFrames()-1) + cycleTime = keyFrameDuration * (self.NumFrames() - 1) return cycleTime - + def calcCycleCount(self, simTime, cycleTime): - phases = simTime / cycleTime; + phases = simTime / cycleTime count = math.floor(phases) loop = True #count = (loop) ? count : cMathUtil::Clamp(count, 0, 1); return count - + def computeCycleOffset(self): - firstFrame=0 - lastFrame = self.NumFrames()-1 + firstFrame = 0 + lastFrame = self.NumFrames() - 1 frameData = self._motion_data['Frames'][0] frameDataNext = self._motion_data['Frames'][lastFrame] - - basePosStart = [frameData[1],frameData[2],frameData[3]] - basePosEnd = [frameDataNext[1],frameDataNext[2],frameDataNext[3]] - self._cycleOffset = [basePosEnd[0]-basePosStart[0],basePosEnd[1]-basePosStart[1],basePosEnd[2]-basePosStart[2]] - return self._cycleOffset
\ No newline at end of file + + basePosStart = [frameData[1], frameData[2], frameData[3]] + basePosEnd = [frameDataNext[1], frameDataNext[2], frameDataNext[3]] + self._cycleOffset = [ + basePosEnd[0] - basePosStart[0], basePosEnd[1] - basePosStart[1], + basePosEnd[2] - basePosStart[2] + ] + return self._cycleOffset diff --git a/examples/pybullet/gym/pybullet_envs/deep_mimic/env/pybullet_deep_mimic_env.py b/examples/pybullet/gym/pybullet_envs/deep_mimic/env/pybullet_deep_mimic_env.py index 68e4d613e..764d70c3d 100644 --- a/examples/pybullet/gym/pybullet_envs/deep_mimic/env/pybullet_deep_mimic_env.py +++ b/examples/pybullet/gym/pybullet_envs/deep_mimic/env/pybullet_deep_mimic_env.py @@ -9,301 +9,313 @@ from pybullet_envs.deep_mimic.env import humanoid_stable_pd import pybullet_data import pybullet as p1 import random - - + + class PyBulletDeepMimicEnv(Env): - def __init__(self, arg_parser=None, enable_draw=False, pybullet_client=None): - super().__init__(arg_parser, enable_draw) - self._num_agents = 1 - self._pybullet_client = pybullet_client - self._isInitialized = False - self._useStablePD = True - self._arg_parser = arg_parser - self.reset() - - def reset(self): - - - if not self._isInitialized: - if self.enable_draw: - self._pybullet_client = bullet_client.BulletClient(connection_mode=p1.GUI) - #disable 'GUI' since it slows down a lot on Mac OSX and some other platforms - self._pybullet_client.configureDebugVisualizer(self._pybullet_client.COV_ENABLE_GUI,0) - else: - self._pybullet_client = bullet_client.BulletClient() - - self._pybullet_client.setAdditionalSearchPath(pybullet_data.getDataPath()) - z2y = self._pybullet_client.getQuaternionFromEuler([-math.pi*0.5,0,0]) - self._planeId = self._pybullet_client.loadURDF("plane_implicit.urdf",[0,0,0],z2y, useMaximalCoordinates=True) - #print("planeId=",self._planeId) - self._pybullet_client.configureDebugVisualizer(self._pybullet_client.COV_ENABLE_Y_AXIS_UP,1) - self._pybullet_client.setGravity(0,-9.8,0) - - self._pybullet_client.setPhysicsEngineParameter(numSolverIterations=10) - self._pybullet_client.changeDynamics(self._planeId, linkIndex=-1, lateralFriction=0.9) - - self._mocapData = motion_capture_data.MotionCaptureData() - - motion_file = self._arg_parser.parse_strings('motion_file') - print("motion_file=",motion_file[0]) - - motionPath = pybullet_data.getDataPath()+"/"+motion_file[0] - #motionPath = pybullet_data.getDataPath()+"/motions/humanoid3d_backflip.txt" - self._mocapData.Load(motionPath) - timeStep = 1./600. - useFixedBase=False - self._humanoid = humanoid_stable_pd.HumanoidStablePD(self._pybullet_client, self._mocapData, timeStep, useFixedBase) - self._isInitialized = True - - self._pybullet_client.setTimeStep(timeStep) - self._pybullet_client.setPhysicsEngineParameter(numSubSteps=1) - - - selfCheck = False - if (selfCheck): - curTime = 0 - while self._pybullet_client.isConnected(): - self._humanoid.setSimTime(curTime) - state = self._humanoid.getState() - #print("state=",state) - pose = self._humanoid.computePose(self._humanoid._frameFraction) - for i in range (10): - curTime+=timeStep - #taus = self._humanoid.computePDForces(pose) - #self._humanoid.applyPDForces(taus) - #self._pybullet_client.stepSimulation() - time.sleep(timeStep) - #print("numframes = ", self._humanoid._mocap_data.NumFrames()) - #startTime = random.randint(0,self._humanoid._mocap_data.NumFrames()-2) - rnrange = 1000 - rn = random.randint(0,rnrange) - startTime = float(rn)/rnrange * self._humanoid.getCycleTime() - self.t = startTime - - self._humanoid.setSimTime(startTime) - - self._humanoid.resetPose() - #this clears the contact points. Todo: add API to explicitly clear all contact points? - #self._pybullet_client.stepSimulation() - self._humanoid.resetPose() - self.needs_update_time = self.t-1#force update - - def get_num_agents(self): - return self._num_agents - - def get_action_space(self, agent_id): - return ActionSpace(ActionSpace.Continuous) - - def get_reward_min(self, agent_id): - return 0 - - def get_reward_max(self, agent_id): - return 1 - - def get_reward_fail(self, agent_id): - return self.get_reward_min(agent_id) - - def get_reward_succ(self, agent_id): - return self.get_reward_max(agent_id) - - #scene_name == "imitate" -> cDrawSceneImitate - def get_state_size(self, agent_id): - #cCtController::GetStateSize() - #int state_size = cDeepMimicCharController::GetStateSize(); - # state_size += GetStatePoseSize();#106 - # state_size += GetStateVelSize(); #(3+3)*numBodyParts=90 - #state_size += GetStatePhaseSize();#1 - #197 - return 197 - - def build_state_norm_groups(self, agent_id): - #if (mEnablePhaseInput) - #{ - #int phase_group = gNormGroupNone; - #int phase_offset = GetStatePhaseOffset(); - #int phase_size = GetStatePhaseSize(); - #out_groups.segment(phase_offset, phase_size) = phase_group * Eigen::VectorXi::Ones(phase_size); - groups = [0]*self.get_state_size(agent_id) - groups[0] = -1 - return groups - - def build_state_offset(self, agent_id): - out_offset = [0]*self.get_state_size(agent_id) - phase_offset = -0.5 - out_offset[0] = phase_offset - return np.array(out_offset) - - def build_state_scale(self, agent_id): - out_scale = [1]*self.get_state_size(agent_id) - phase_scale = 2 - out_scale[0] = phase_scale - return np.array(out_scale) - - def get_goal_size(self, agent_id): - return 0 - - def get_action_size(self, agent_id): - ctrl_size = 43 #numDof - root_size = 7 - return ctrl_size - root_size - - def build_goal_norm_groups(self, agent_id): - return np.array([]) - - def build_goal_offset(self, agent_id): - return np.array([]) - - def build_goal_scale(self, agent_id): - return np.array([]) - - def build_action_offset(self, agent_id): - out_offset = [0] * self.get_action_size(agent_id) - out_offset = [0.0000000000,0.0000000000,0.0000000000,-0.200000000,0.0000000000,0.0000000000,0.0000000000, - -0.200000000,0.0000000000,0.0000000000, 0.00000000, -0.2000000, 1.57000000, 0.00000000, 0.00000000, - 0.00000000, -0.2000000, 0.00000000, 0.00000000, 0.00000000, -0.2000000, -1.5700000, 0.00000000, 0.00000000, - 0.00000000, -0.2000000, 1.57000000, 0.00000000, 0.00000000, 0.00000000, -0.2000000, 0.00000000, 0.00000000, - 0.00000000, -0.2000000, -1.5700000] - #see cCtCtrlUtil::BuildOffsetScalePDPrismatic and - #see cCtCtrlUtil::BuildOffsetScalePDSpherical - return np.array(out_offset) - - def build_action_scale(self, agent_id): - out_scale = [1] * self.get_action_size(agent_id) - #see cCtCtrlUtil::BuildOffsetScalePDPrismatic and - #see cCtCtrlUtil::BuildOffsetScalePDSpherical - out_scale=[ 0.20833333333333,1.00000000000000,1.00000000000000,1.00000000000000,0.25000000000000, - 1.00000000000000,1.00000000000000,1.00000000000000,0.12077294685990,1.00000000000000, - 1.000000000000, 1.000000000000, 0.159235668789, 0.159235668789, 1.000000000000, - 1.000000000000, 1.000000000000, 0.079617834394, 1.000000000000, 1.000000000000, - 1.000000000000, 0.159235668789, 0.120772946859, 1.000000000000, 1.000000000000, - 1.000000000000, 0.159235668789, 0.159235668789, 1.000000000000, 1.000000000000, - 1.000000000000, 0.107758620689, 1.000000000000, 1.000000000000, 1.000000000000, - 0.159235668789] - return np.array(out_scale) - - def build_action_bound_min(self, agent_id): - #see cCtCtrlUtil::BuildBoundsPDSpherical - out_scale = [-1] * self.get_action_size(agent_id) - out_scale = [-4.79999999999,-1.00000000000,-1.00000000000,-1.00000000000,-4.00000000000, - -1.00000000000,-1.00000000000,-1.00000000000,-7.77999999999,-1.00000000000, -1.000000000, - -1.000000000, -7.850000000, -6.280000000, -1.000000000, -1.000000000, -1.000000000, - -12.56000000, -1.000000000, -1.000000000, -1.000000000, -4.710000000, - -7.779999999, -1.000000000, -1.000000000, -1.000000000, -7.850000000, - -6.280000000, -1.000000000, -1.000000000, -1.000000000, -8.460000000, - -1.000000000, -1.000000000, -1.000000000, -4.710000000] - - return out_scale - - def build_action_bound_max(self, agent_id): - out_scale = [1] * self.get_action_size(agent_id) - out_scale=[ - 4.799999999,1.000000000,1.000000000,1.000000000,4.000000000,1.000000000, - 1.000000000,1.000000000,8.779999999,1.000000000, 1.0000000, 1.0000000, - 4.7100000, 6.2800000, 1.0000000, 1.0000000, 1.0000000, - 12.560000, 1.0000000, 1.0000000, 1.0000000, 7.8500000, - 8.7799999, 1.0000000, 1.0000000, 1.0000000, 4.7100000, - 6.2800000, 1.0000000, 1.0000000, 1.0000000, 10.100000, - 1.0000000, 1.0000000, 1.0000000, 7.8500000] - return out_scale - - def set_mode(self, mode): - self._mode = mode - - def need_new_action(self, agent_id): - if self.t>=self.needs_update_time: - self.needs_update_time = self.t + 1./30. - return True - return False - - def record_state(self, agent_id): - state = self._humanoid.getState() - - return np.array(state) - - - def record_goal(self, agent_id): - return np.array([]) - - def calc_reward(self, agent_id): - kinPose = self._humanoid.computePose(self._humanoid._frameFraction) - reward = self._humanoid.getReward(kinPose) - return reward - - def set_action(self, agent_id, action): - #print("action=",) - #for a in action: - # print(a) - np.savetxt("pb_action.csv", action, delimiter=",") - self.desiredPose = self._humanoid.convertActionToPose(action) - #we need the target root positon and orientation to be zero, to be compatible with deep mimic - self.desiredPose[0] = 0 - self.desiredPose[1] = 0 - self.desiredPose[2] = 0 - self.desiredPose[3] = 0 - self.desiredPose[4] = 0 - self.desiredPose[5] = 0 - self.desiredPose[6] = 0 - target_pose = np.array(self.desiredPose) - - - np.savetxt("pb_target_pose.csv", target_pose, delimiter=",") - - #print("set_action: desiredPose=", self.desiredPose) - - def log_val(self, agent_id, val): - pass - - - def update(self, timeStep): - #print("pybullet_deep_mimic_env:update timeStep=",timeStep," t=",self.t) + + def __init__(self, arg_parser=None, enable_draw=False, pybullet_client=None): + super().__init__(arg_parser, enable_draw) + self._num_agents = 1 + self._pybullet_client = pybullet_client + self._isInitialized = False + self._useStablePD = True + self._arg_parser = arg_parser + self.reset() + + def reset(self): + + if not self._isInitialized: + if self.enable_draw: + self._pybullet_client = bullet_client.BulletClient(connection_mode=p1.GUI) + #disable 'GUI' since it slows down a lot on Mac OSX and some other platforms + self._pybullet_client.configureDebugVisualizer(self._pybullet_client.COV_ENABLE_GUI, 0) + else: + self._pybullet_client = bullet_client.BulletClient() + + self._pybullet_client.setAdditionalSearchPath(pybullet_data.getDataPath()) + z2y = self._pybullet_client.getQuaternionFromEuler([-math.pi * 0.5, 0, 0]) + self._planeId = self._pybullet_client.loadURDF("plane_implicit.urdf", [0, 0, 0], + z2y, + useMaximalCoordinates=True) + #print("planeId=",self._planeId) + self._pybullet_client.configureDebugVisualizer(self._pybullet_client.COV_ENABLE_Y_AXIS_UP, 1) + self._pybullet_client.setGravity(0, -9.8, 0) + + self._pybullet_client.setPhysicsEngineParameter(numSolverIterations=10) + self._pybullet_client.changeDynamics(self._planeId, linkIndex=-1, lateralFriction=0.9) + + self._mocapData = motion_capture_data.MotionCaptureData() + + motion_file = self._arg_parser.parse_strings('motion_file') + print("motion_file=", motion_file[0]) + + motionPath = pybullet_data.getDataPath() + "/" + motion_file[0] + #motionPath = pybullet_data.getDataPath()+"/motions/humanoid3d_backflip.txt" + self._mocapData.Load(motionPath) + timeStep = 1. / 600. + useFixedBase = False + self._humanoid = humanoid_stable_pd.HumanoidStablePD(self._pybullet_client, self._mocapData, + timeStep, useFixedBase) + self._isInitialized = True + self._pybullet_client.setTimeStep(timeStep) - self._humanoid._timeStep = timeStep - - for i in range(1): - self.t += timeStep - self._humanoid.setSimTime(self.t) - - if self.desiredPose: - kinPose = self._humanoid.computePose(self._humanoid._frameFraction) - self._humanoid.initializePose(self._humanoid._poseInterpolator, self._humanoid._kin_model, initBase=True) - #pos,orn=self._pybullet_client.getBasePositionAndOrientation(self._humanoid._sim_model) - #self._pybullet_client.resetBasePositionAndOrientation(self._humanoid._kin_model, [pos[0]+3,pos[1],pos[2]],orn) - #print("desiredPositions=",self.desiredPose) - maxForces = [0,0,0,0,0,0,0,200,200,200,200, 50,50,50,50, 200,200,200,200, 150, 90,90,90,90, 100,100,100,100, 60, 200,200,200,200, 150, 90, 90, 90, 90, 100,100,100,100, 60] - - if self._useStablePD: - taus = self._humanoid.computePDForces(self.desiredPose, desiredVelocities=None, maxForces=maxForces) - self._humanoid.applyPDForces(taus) - else: - self._humanoid.setJointMotors(self.desiredPose, maxForces=maxForces) - - self._pybullet_client.stepSimulation() - - - def set_sample_count(self, count): - return - - def check_terminate(self, agent_id): - return Env.Terminate(self.is_episode_end()) - - def is_episode_end(self): - isEnded = self._humanoid.terminates() - #also check maximum time, 20 seconds (todo get from file) - #print("self.t=",self.t) - if (self.t>20): - isEnded = True - return isEnded - - def check_valid_episode(self): - #could check if limbs exceed velocity threshold - return true - - def getKeyboardEvents(self): - return self._pybullet_client.getKeyboardEvents() - - def isKeyTriggered(self, keys, key): - o = ord(key) - #print("ord=",o) - if o in keys: - return keys[ord(key)] & self._pybullet_client.KEY_WAS_TRIGGERED - return False + self._pybullet_client.setPhysicsEngineParameter(numSubSteps=1) + + selfCheck = False + if (selfCheck): + curTime = 0 + while self._pybullet_client.isConnected(): + self._humanoid.setSimTime(curTime) + state = self._humanoid.getState() + #print("state=",state) + pose = self._humanoid.computePose(self._humanoid._frameFraction) + for i in range(10): + curTime += timeStep + #taus = self._humanoid.computePDForces(pose) + #self._humanoid.applyPDForces(taus) + #self._pybullet_client.stepSimulation() + time.sleep(timeStep) + #print("numframes = ", self._humanoid._mocap_data.NumFrames()) + #startTime = random.randint(0,self._humanoid._mocap_data.NumFrames()-2) + rnrange = 1000 + rn = random.randint(0, rnrange) + startTime = float(rn) / rnrange * self._humanoid.getCycleTime() + self.t = startTime + + self._humanoid.setSimTime(startTime) + + self._humanoid.resetPose() + #this clears the contact points. Todo: add API to explicitly clear all contact points? + #self._pybullet_client.stepSimulation() + self._humanoid.resetPose() + self.needs_update_time = self.t - 1 #force update + + def get_num_agents(self): + return self._num_agents + + def get_action_space(self, agent_id): + return ActionSpace(ActionSpace.Continuous) + + def get_reward_min(self, agent_id): + return 0 + + def get_reward_max(self, agent_id): + return 1 + + def get_reward_fail(self, agent_id): + return self.get_reward_min(agent_id) + + def get_reward_succ(self, agent_id): + return self.get_reward_max(agent_id) + + #scene_name == "imitate" -> cDrawSceneImitate + def get_state_size(self, agent_id): + #cCtController::GetStateSize() + #int state_size = cDeepMimicCharController::GetStateSize(); + # state_size += GetStatePoseSize();#106 + # state_size += GetStateVelSize(); #(3+3)*numBodyParts=90 + #state_size += GetStatePhaseSize();#1 + #197 + return 197 + + def build_state_norm_groups(self, agent_id): + #if (mEnablePhaseInput) + #{ + #int phase_group = gNormGroupNone; + #int phase_offset = GetStatePhaseOffset(); + #int phase_size = GetStatePhaseSize(); + #out_groups.segment(phase_offset, phase_size) = phase_group * Eigen::VectorXi::Ones(phase_size); + groups = [0] * self.get_state_size(agent_id) + groups[0] = -1 + return groups + + def build_state_offset(self, agent_id): + out_offset = [0] * self.get_state_size(agent_id) + phase_offset = -0.5 + out_offset[0] = phase_offset + return np.array(out_offset) + + def build_state_scale(self, agent_id): + out_scale = [1] * self.get_state_size(agent_id) + phase_scale = 2 + out_scale[0] = phase_scale + return np.array(out_scale) + + def get_goal_size(self, agent_id): + return 0 + + def get_action_size(self, agent_id): + ctrl_size = 43 #numDof + root_size = 7 + return ctrl_size - root_size + + def build_goal_norm_groups(self, agent_id): + return np.array([]) + + def build_goal_offset(self, agent_id): + return np.array([]) + + def build_goal_scale(self, agent_id): + return np.array([]) + + def build_action_offset(self, agent_id): + out_offset = [0] * self.get_action_size(agent_id) + out_offset = [ + 0.0000000000, 0.0000000000, 0.0000000000, -0.200000000, 0.0000000000, 0.0000000000, + 0.0000000000, -0.200000000, 0.0000000000, 0.0000000000, 0.00000000, -0.2000000, 1.57000000, + 0.00000000, 0.00000000, 0.00000000, -0.2000000, 0.00000000, 0.00000000, 0.00000000, + -0.2000000, -1.5700000, 0.00000000, 0.00000000, 0.00000000, -0.2000000, 1.57000000, + 0.00000000, 0.00000000, 0.00000000, -0.2000000, 0.00000000, 0.00000000, 0.00000000, + -0.2000000, -1.5700000 + ] + #see cCtCtrlUtil::BuildOffsetScalePDPrismatic and + #see cCtCtrlUtil::BuildOffsetScalePDSpherical + return np.array(out_offset) + + def build_action_scale(self, agent_id): + out_scale = [1] * self.get_action_size(agent_id) + #see cCtCtrlUtil::BuildOffsetScalePDPrismatic and + #see cCtCtrlUtil::BuildOffsetScalePDSpherical + out_scale = [ + 0.20833333333333, 1.00000000000000, 1.00000000000000, 1.00000000000000, 0.25000000000000, + 1.00000000000000, 1.00000000000000, 1.00000000000000, 0.12077294685990, 1.00000000000000, + 1.000000000000, 1.000000000000, 0.159235668789, 0.159235668789, 1.000000000000, + 1.000000000000, 1.000000000000, 0.079617834394, 1.000000000000, 1.000000000000, + 1.000000000000, 0.159235668789, 0.120772946859, 1.000000000000, 1.000000000000, + 1.000000000000, 0.159235668789, 0.159235668789, 1.000000000000, 1.000000000000, + 1.000000000000, 0.107758620689, 1.000000000000, 1.000000000000, 1.000000000000, + 0.159235668789 + ] + return np.array(out_scale) + + def build_action_bound_min(self, agent_id): + #see cCtCtrlUtil::BuildBoundsPDSpherical + out_scale = [-1] * self.get_action_size(agent_id) + out_scale = [ + -4.79999999999, -1.00000000000, -1.00000000000, -1.00000000000, -4.00000000000, + -1.00000000000, -1.00000000000, -1.00000000000, -7.77999999999, -1.00000000000, + -1.000000000, -1.000000000, -7.850000000, -6.280000000, -1.000000000, -1.000000000, + -1.000000000, -12.56000000, -1.000000000, -1.000000000, -1.000000000, -4.710000000, + -7.779999999, -1.000000000, -1.000000000, -1.000000000, -7.850000000, -6.280000000, + -1.000000000, -1.000000000, -1.000000000, -8.460000000, -1.000000000, -1.000000000, + -1.000000000, -4.710000000 + ] + + return out_scale + + def build_action_bound_max(self, agent_id): + out_scale = [1] * self.get_action_size(agent_id) + out_scale = [ + 4.799999999, 1.000000000, 1.000000000, 1.000000000, 4.000000000, 1.000000000, 1.000000000, + 1.000000000, 8.779999999, 1.000000000, 1.0000000, 1.0000000, 4.7100000, 6.2800000, + 1.0000000, 1.0000000, 1.0000000, 12.560000, 1.0000000, 1.0000000, 1.0000000, 7.8500000, + 8.7799999, 1.0000000, 1.0000000, 1.0000000, 4.7100000, 6.2800000, 1.0000000, 1.0000000, + 1.0000000, 10.100000, 1.0000000, 1.0000000, 1.0000000, 7.8500000 + ] + return out_scale + + def set_mode(self, mode): + self._mode = mode + + def need_new_action(self, agent_id): + if self.t >= self.needs_update_time: + self.needs_update_time = self.t + 1. / 30. + return True + return False + + def record_state(self, agent_id): + state = self._humanoid.getState() + + return np.array(state) + + def record_goal(self, agent_id): + return np.array([]) + + def calc_reward(self, agent_id): + kinPose = self._humanoid.computePose(self._humanoid._frameFraction) + reward = self._humanoid.getReward(kinPose) + return reward + + def set_action(self, agent_id, action): + #print("action=",) + #for a in action: + # print(a) + np.savetxt("pb_action.csv", action, delimiter=",") + self.desiredPose = self._humanoid.convertActionToPose(action) + #we need the target root positon and orientation to be zero, to be compatible with deep mimic + self.desiredPose[0] = 0 + self.desiredPose[1] = 0 + self.desiredPose[2] = 0 + self.desiredPose[3] = 0 + self.desiredPose[4] = 0 + self.desiredPose[5] = 0 + self.desiredPose[6] = 0 + target_pose = np.array(self.desiredPose) + + np.savetxt("pb_target_pose.csv", target_pose, delimiter=",") + + #print("set_action: desiredPose=", self.desiredPose) + + def log_val(self, agent_id, val): + pass + + def update(self, timeStep): + #print("pybullet_deep_mimic_env:update timeStep=",timeStep," t=",self.t) + self._pybullet_client.setTimeStep(timeStep) + self._humanoid._timeStep = timeStep + + for i in range(1): + self.t += timeStep + self._humanoid.setSimTime(self.t) + + if self.desiredPose: + kinPose = self._humanoid.computePose(self._humanoid._frameFraction) + self._humanoid.initializePose(self._humanoid._poseInterpolator, + self._humanoid._kin_model, + initBase=True) + #pos,orn=self._pybullet_client.getBasePositionAndOrientation(self._humanoid._sim_model) + #self._pybullet_client.resetBasePositionAndOrientation(self._humanoid._kin_model, [pos[0]+3,pos[1],pos[2]],orn) + #print("desiredPositions=",self.desiredPose) + maxForces = [ + 0, 0, 0, 0, 0, 0, 0, 200, 200, 200, 200, 50, 50, 50, 50, 200, 200, 200, 200, 150, 90, + 90, 90, 90, 100, 100, 100, 100, 60, 200, 200, 200, 200, 150, 90, 90, 90, 90, 100, 100, + 100, 100, 60 + ] + + if self._useStablePD: + taus = self._humanoid.computePDForces(self.desiredPose, + desiredVelocities=None, + maxForces=maxForces) + self._humanoid.applyPDForces(taus) + else: + self._humanoid.setJointMotors(self.desiredPose, maxForces=maxForces) + + self._pybullet_client.stepSimulation() + + def set_sample_count(self, count): + return + + def check_terminate(self, agent_id): + return Env.Terminate(self.is_episode_end()) + + def is_episode_end(self): + isEnded = self._humanoid.terminates() + #also check maximum time, 20 seconds (todo get from file) + #print("self.t=",self.t) + if (self.t > 20): + isEnded = True + return isEnded + + def check_valid_episode(self): + #could check if limbs exceed velocity threshold + return true + + def getKeyboardEvents(self): + return self._pybullet_client.getKeyboardEvents() + + def isKeyTriggered(self, keys, key): + o = ord(key) + #print("ord=",o) + if o in keys: + return keys[ord(key)] & self._pybullet_client.KEY_WAS_TRIGGERED + return False diff --git a/examples/pybullet/gym/pybullet_envs/deep_mimic/env/quadrupedPoseInterpolator.py b/examples/pybullet/gym/pybullet_envs/deep_mimic/env/quadrupedPoseInterpolator.py index 9aa6d2652..49a05b003 100644 --- a/examples/pybullet/gym/pybullet_envs/deep_mimic/env/quadrupedPoseInterpolator.py +++ b/examples/pybullet/gym/pybullet_envs/deep_mimic/env/quadrupedPoseInterpolator.py @@ -1,54 +1,66 @@ from pybullet_utils import bullet_client import math - + + class QuadrupedPoseInterpolator(object): + def __init__(self): pass - - - def ComputeLinVel(self,posStart, posEnd, deltaTime): - vel = [(posEnd[0]-posStart[0])/deltaTime,(posEnd[1]-posStart[1])/deltaTime,(posEnd[2]-posStart[2])/deltaTime] + + def ComputeLinVel(self, posStart, posEnd, deltaTime): + vel = [(posEnd[0] - posStart[0]) / deltaTime, (posEnd[1] - posStart[1]) / deltaTime, + (posEnd[2] - posStart[2]) / deltaTime] return vel - - def ComputeAngVel(self,ornStart, ornEnd, deltaTime, bullet_client): - dorn = bullet_client.getDifferenceQuaternion(ornStart,ornEnd) - axis,angle = bullet_client.getAxisAngleFromQuaternion(dorn) - angVel = [(axis[0]*angle)/deltaTime,(axis[1]*angle)/deltaTime,(axis[2]*angle)/deltaTime] + + def ComputeAngVel(self, ornStart, ornEnd, deltaTime, bullet_client): + dorn = bullet_client.getDifferenceQuaternion(ornStart, ornEnd) + axis, angle = bullet_client.getAxisAngleFromQuaternion(dorn) + angVel = [(axis[0] * angle) / deltaTime, (axis[1] * angle) / deltaTime, + (axis[2] * angle) / deltaTime] return angVel - - def ComputeAngVelRel(self,ornStart, ornEnd, deltaTime, bullet_client): - ornStartConjugate = [-ornStart[0],-ornStart[1],-ornStart[2],ornStart[3]] - pos_diff, q_diff =bullet_client.multiplyTransforms([0,0,0], ornStartConjugate, [0,0,0], ornEnd) - axis,angle = bullet_client.getAxisAngleFromQuaternion(q_diff) - angVel = [(axis[0]*angle)/deltaTime,(axis[1]*angle)/deltaTime,(axis[2]*angle)/deltaTime] + + def ComputeAngVelRel(self, ornStart, ornEnd, deltaTime, bullet_client): + ornStartConjugate = [-ornStart[0], -ornStart[1], -ornStart[2], ornStart[3]] + pos_diff, q_diff = bullet_client.multiplyTransforms([0, 0, 0], ornStartConjugate, [0, 0, 0], + ornEnd) + axis, angle = bullet_client.getAxisAngleFromQuaternion(q_diff) + angVel = [(axis[0] * angle) / deltaTime, (axis[1] * angle) / deltaTime, + (axis[2] * angle) / deltaTime] return angVel - - def Slerp(self, frameFraction, frameData, frameDataNext,bullet_client ): + + def Slerp(self, frameFraction, frameData, frameDataNext, bullet_client): keyFrameDuration = frameData[0] - basePos1Start = [frameData[1],frameData[2],frameData[3]] - basePos1End = [frameDataNext[1],frameDataNext[2],frameDataNext[3]] - self._basePos = [basePos1Start[0]+frameFraction*(basePos1End[0]-basePos1Start[0]), - basePos1Start[1]+frameFraction*(basePos1End[1]-basePos1Start[1]), - basePos1Start[2]+frameFraction*(basePos1End[2]-basePos1Start[2])] - self._baseLinVel = self.ComputeLinVel(basePos1Start,basePos1End, keyFrameDuration) - baseOrn1Start = [frameData[5],frameData[6], frameData[7],frameData[4]] - baseOrn1Next = [frameDataNext[5],frameDataNext[6], frameDataNext[7],frameDataNext[4]] - self._baseOrn = bullet_client.getQuaternionSlerp(baseOrn1Start,baseOrn1Next,frameFraction) - self._baseAngVel = self.ComputeAngVel(baseOrn1Start,baseOrn1Next, keyFrameDuration, bullet_client) - - jointPositions=[self._basePos[0],self._basePos[1],self._basePos[2], - self._baseOrn[0],self._baseOrn[1],self._baseOrn[2],self._baseOrn[3]] - jointVelocities=[self._baseLinVel[0],self._baseLinVel[1],self._baseLinVel[2], - self._baseAngVel[0],self._baseAngVel[1],self._baseAngVel[2]] - - for j in range (12): - index=j+8 - jointPosStart=frameData[index] - jointPosEnd=frameDataNext[index] - jointPos=jointPosStart+frameFraction*(jointPosEnd-jointPosStart) - jointVel=(jointPosEnd-jointPosStart)/keyFrameDuration + basePos1Start = [frameData[1], frameData[2], frameData[3]] + basePos1End = [frameDataNext[1], frameDataNext[2], frameDataNext[3]] + self._basePos = [ + basePos1Start[0] + frameFraction * (basePos1End[0] - basePos1Start[0]), + basePos1Start[1] + frameFraction * (basePos1End[1] - basePos1Start[1]), + basePos1Start[2] + frameFraction * (basePos1End[2] - basePos1Start[2]) + ] + self._baseLinVel = self.ComputeLinVel(basePos1Start, basePos1End, keyFrameDuration) + baseOrn1Start = [frameData[5], frameData[6], frameData[7], frameData[4]] + baseOrn1Next = [frameDataNext[5], frameDataNext[6], frameDataNext[7], frameDataNext[4]] + self._baseOrn = bullet_client.getQuaternionSlerp(baseOrn1Start, baseOrn1Next, frameFraction) + self._baseAngVel = self.ComputeAngVel(baseOrn1Start, baseOrn1Next, keyFrameDuration, + bullet_client) + + jointPositions = [ + self._basePos[0], self._basePos[1], self._basePos[2], self._baseOrn[0], self._baseOrn[1], + self._baseOrn[2], self._baseOrn[3] + ] + jointVelocities = [ + self._baseLinVel[0], self._baseLinVel[1], self._baseLinVel[2], self._baseAngVel[0], + self._baseAngVel[1], self._baseAngVel[2] + ] + + for j in range(12): + index = j + 8 + jointPosStart = frameData[index] + jointPosEnd = frameDataNext[index] + jointPos = jointPosStart + frameFraction * (jointPosEnd - jointPosStart) + jointVel = (jointPosEnd - jointPosStart) / keyFrameDuration jointPositions.append(jointPos) jointVelocities.append(jointVel) self._jointPositions = jointPositions self._jointVelocities = jointVelocities - return jointPositions,jointVelocities + return jointPositions, jointVelocities diff --git a/examples/pybullet/gym/pybullet_envs/deep_mimic/env/quadruped_stable_pd.py b/examples/pybullet/gym/pybullet_envs/deep_mimic/env/quadruped_stable_pd.py index ce66fb462..66f2f70b2 100644 --- a/examples/pybullet/gym/pybullet_envs/deep_mimic/env/quadruped_stable_pd.py +++ b/examples/pybullet/gym/pybullet_envs/deep_mimic/env/quadruped_stable_pd.py @@ -1,127 +1,209 @@ - -from pybullet_utils import pd_controller_stable +from pybullet_utils import pd_controller_stable from pybullet_envs.deep_mimic.env import quadruped_pose_interpolator import math class QuadrupedStablePD(object): + def __init__(self, pybullet_client, mocap_data, timeStep, useFixedBase=True): self._pybullet_client = pybullet_client self._mocap_data = mocap_data print("LOADING quadruped!") - - startPos=[0.007058990464444105, 0.03149299192130908, 0.4918981912395484] - startOrn=[0.005934649695708604, 0.7065453990917289, 0.7076373820553712, -0.0027774940359030264] - self._sim_model = self._pybullet_client.loadURDF("laikago/laikago.urdf",startPos,startOrn, flags = urdfFlags,useFixedBase=False) - self._pybullet_client.resetBasePositionAndOrientation(_sim_model,startPos,startOrn) - + + startPos = [0.007058990464444105, 0.03149299192130908, 0.4918981912395484] + startOrn = [ + 0.005934649695708604, 0.7065453990917289, 0.7076373820553712, -0.0027774940359030264 + ] + self._sim_model = self._pybullet_client.loadURDF("laikago/laikago.urdf", + startPos, + startOrn, + flags=urdfFlags, + useFixedBase=False) + self._pybullet_client.resetBasePositionAndOrientation(_sim_model, startPos, startOrn) + self._end_effectors = [] #ankle and wrist, both left and right - self._kin_model = self._pybullet_client.loadURDF("laikago/laikago.urdf",startPos,startOrn,useFixedBase=True) - + self._kin_model = self._pybullet_client.loadURDF("laikago/laikago.urdf", + startPos, + startOrn, + useFixedBase=True) + self._pybullet_client.changeDynamics(self._sim_model, -1, lateralFriction=0.9) - for j in range (self._pybullet_client.getNumJoints(self._sim_model)): + for j in range(self._pybullet_client.getNumJoints(self._sim_model)): self._pybullet_client.changeDynamics(self._sim_model, j, lateralFriction=0.9) - + self._pybullet_client.changeDynamics(self._sim_model, -1, linearDamping=0, angularDamping=0) self._pybullet_client.changeDynamics(self._kin_model, -1, linearDamping=0, angularDamping=0) - + #todo: add feature to disable simulation for a particular object. Until then, disable all collisions - self._pybullet_client.setCollisionFilterGroupMask(self._kin_model,-1,collisionFilterGroup=0,collisionFilterMask=0) - self._pybullet_client.changeDynamics(self._kin_model,-1,activationState=self._pybullet_client.ACTIVATION_STATE_SLEEP+self._pybullet_client.ACTIVATION_STATE_ENABLE_SLEEPING+self._pybullet_client.ACTIVATION_STATE_DISABLE_WAKEUP) + self._pybullet_client.setCollisionFilterGroupMask(self._kin_model, + -1, + collisionFilterGroup=0, + collisionFilterMask=0) + self._pybullet_client.changeDynamics( + self._kin_model, + -1, + activationState=self._pybullet_client.ACTIVATION_STATE_SLEEP + + self._pybullet_client.ACTIVATION_STATE_ENABLE_SLEEPING + + self._pybullet_client.ACTIVATION_STATE_DISABLE_WAKEUP) alpha = 0.4 - self._pybullet_client.changeVisualShape(self._kin_model,-1, rgbaColor=[1,1,1,alpha]) - for j in range (self._pybullet_client.getNumJoints(self._kin_model)): - self._pybullet_client.setCollisionFilterGroupMask(self._kin_model,j,collisionFilterGroup=0,collisionFilterMask=0) - self._pybullet_client.changeDynamics(self._kin_model,j,activationState=self._pybullet_client.ACTIVATION_STATE_SLEEP+self._pybullet_client.ACTIVATION_STATE_ENABLE_SLEEPING+self._pybullet_client.ACTIVATION_STATE_DISABLE_WAKEUP) - self._pybullet_client.changeVisualShape(self._kin_model,j, rgbaColor=[1,1,1,alpha]) - + self._pybullet_client.changeVisualShape(self._kin_model, -1, rgbaColor=[1, 1, 1, alpha]) + for j in range(self._pybullet_client.getNumJoints(self._kin_model)): + self._pybullet_client.setCollisionFilterGroupMask(self._kin_model, + j, + collisionFilterGroup=0, + collisionFilterMask=0) + self._pybullet_client.changeDynamics( + self._kin_model, + j, + activationState=self._pybullet_client.ACTIVATION_STATE_SLEEP + + self._pybullet_client.ACTIVATION_STATE_ENABLE_SLEEPING + + self._pybullet_client.ACTIVATION_STATE_DISABLE_WAKEUP) + self._pybullet_client.changeVisualShape(self._kin_model, j, rgbaColor=[1, 1, 1, alpha]) + self._poseInterpolator = humanoid_pose_interpolator.HumanoidPoseInterpolator() - - for i in range (self._mocap_data.NumFrames()-1): + + for i in range(self._mocap_data.NumFrames() - 1): frameData = self._mocap_data._motion_data['Frames'][i] self._poseInterpolator.PostProcessMotionData(frameData) - + self._stablePD = pd_controller_stable.PDControllerStableMultiDof(self._pybullet_client) self._timeStep = timeStep #todo: kp/pd - self._kpOrg = [0,0,0,0,0,0,0,1000,1000,1000,1000,100,100,100,100,500,500,500,500,500,400,400,400,400,400,400,400,400,300,500,500,500,500,500,400,400,400,400,400,400,400,400,300] - self._kdOrg = [0,0,0,0,0,0,0,100,100,100,100,10,10,10,10,50,50,50,50,50,40,40,40,40,40,40,40,40,30,50,50,50,50,50,40,40,40,40,40,40,40,40,30] - - self._jointIndicesAll = [chest,neck, rightHip,rightKnee,rightAnkle,rightShoulder,rightElbow,leftHip,leftKnee,leftAnkle,leftShoulder,leftElbow] + self._kpOrg = [ + 0, 0, 0, 0, 0, 0, 0, 1000, 1000, 1000, 1000, 100, 100, 100, 100, 500, 500, 500, 500, 500, + 400, 400, 400, 400, 400, 400, 400, 400, 300, 500, 500, 500, 500, 500, 400, 400, 400, 400, + 400, 400, 400, 400, 300 + ] + self._kdOrg = [ + 0, 0, 0, 0, 0, 0, 0, 100, 100, 100, 100, 10, 10, 10, 10, 50, 50, 50, 50, 50, 40, 40, 40, + 40, 40, 40, 40, 40, 30, 50, 50, 50, 50, 50, 40, 40, 40, 40, 40, 40, 40, 40, 30 + ] + + self._jointIndicesAll = [ + chest, neck, rightHip, rightKnee, rightAnkle, rightShoulder, rightElbow, leftHip, leftKnee, + leftAnkle, leftShoulder, leftElbow + ] for j in self._jointIndicesAll: #self._pybullet_client.setJointMotorControlMultiDof(self._sim_model, j, self._pybullet_client.POSITION_CONTROL, force=[1,1,1]) - self._pybullet_client.setJointMotorControl2(self._sim_model, j, self._pybullet_client.POSITION_CONTROL, targetPosition=0, positionGain=0, targetVelocity=0,force=jointFrictionForce) - self._pybullet_client.setJointMotorControlMultiDof(self._sim_model, j, self._pybullet_client.POSITION_CONTROL,targetPosition=[0,0,0,1], targetVelocity=[0,0,0], positionGain=0,velocityGain=1,force=[jointFrictionForce,jointFrictionForce,jointFrictionForce]) - self._pybullet_client.setJointMotorControl2(self._kin_model, j, self._pybullet_client.POSITION_CONTROL, targetPosition=0, positionGain=0, targetVelocity=0,force=0) - self._pybullet_client.setJointMotorControlMultiDof(self._kin_model, j, self._pybullet_client.POSITION_CONTROL,targetPosition=[0,0,0,1], targetVelocity=[0,0,0], positionGain=0,velocityGain=1,force=[jointFrictionForce,jointFrictionForce,0]) - - self._jointDofCounts=[4,4,4,1,4,4,1,4,1,4,4,1] - + self._pybullet_client.setJointMotorControl2(self._sim_model, + j, + self._pybullet_client.POSITION_CONTROL, + targetPosition=0, + positionGain=0, + targetVelocity=0, + force=jointFrictionForce) + self._pybullet_client.setJointMotorControlMultiDof( + self._sim_model, + j, + self._pybullet_client.POSITION_CONTROL, + targetPosition=[0, 0, 0, 1], + targetVelocity=[0, 0, 0], + positionGain=0, + velocityGain=1, + force=[jointFrictionForce, jointFrictionForce, jointFrictionForce]) + self._pybullet_client.setJointMotorControl2(self._kin_model, + j, + self._pybullet_client.POSITION_CONTROL, + targetPosition=0, + positionGain=0, + targetVelocity=0, + force=0) + self._pybullet_client.setJointMotorControlMultiDof( + self._kin_model, + j, + self._pybullet_client.POSITION_CONTROL, + targetPosition=[0, 0, 0, 1], + targetVelocity=[0, 0, 0], + positionGain=0, + velocityGain=1, + force=[jointFrictionForce, jointFrictionForce, 0]) + + self._jointDofCounts = [4, 4, 4, 1, 4, 4, 1, 4, 1, 4, 4, 1] + #only those body parts/links are allowed to touch the ground, otherwise the episode terminates - self._allowed_body_parts=[5,11] - + self._allowed_body_parts = [5, 11] + #[x,y,z] base position and [x,y,z,w] base orientation! - self._totalDofs = 7 + self._totalDofs = 7 for dof in self._jointDofCounts: self._totalDofs += dof self.setSimTime(0) - + self.resetPose() - + def resetPose(self): #print("resetPose with self._frame=", self._frame, " and self._frameFraction=",self._frameFraction) pose = self.computePose(self._frameFraction) self.initializePose(self._poseInterpolator, self._sim_model, initBase=True) self.initializePose(self._poseInterpolator, self._kin_model, initBase=False) - - def initializePose(self, pose, phys_model,initBase, initializeVelocity = True): - + + def initializePose(self, pose, phys_model, initBase, initializeVelocity=True): + if initializeVelocity: if initBase: - self._pybullet_client.resetBasePositionAndOrientation(phys_model, pose._basePos, pose._baseOrn) + self._pybullet_client.resetBasePositionAndOrientation(phys_model, pose._basePos, + pose._baseOrn) self._pybullet_client.resetBaseVelocity(phys_model, pose._baseLinVel, pose._baseAngVel) - self._pybullet_client.resetJointStateMultiDof(phys_model,chest,pose._chestRot, pose._chestVel) - self._pybullet_client.resetJointStateMultiDof(phys_model,neck,pose._neckRot, pose._neckVel) - self._pybullet_client.resetJointStateMultiDof(phys_model,rightHip,pose._rightHipRot, pose._rightHipVel) - self._pybullet_client.resetJointStateMultiDof(phys_model,rightKnee,pose._rightKneeRot, pose._rightKneeVel) - self._pybullet_client.resetJointStateMultiDof(phys_model,rightAnkle,pose._rightAnkleRot, pose._rightAnkleVel) - self._pybullet_client.resetJointStateMultiDof(phys_model,rightShoulder,pose._rightShoulderRot, pose._rightShoulderVel) - self._pybullet_client.resetJointStateMultiDof(phys_model,rightElbow, pose._rightElbowRot, pose._rightElbowVel) - self._pybullet_client.resetJointStateMultiDof(phys_model,leftHip, pose._leftHipRot, pose._leftHipVel) - self._pybullet_client.resetJointStateMultiDof(phys_model,leftKnee, pose._leftKneeRot, pose._leftKneeVel) - self._pybullet_client.resetJointStateMultiDof(phys_model,leftAnkle, pose._leftAnkleRot, pose._leftAnkleVel) - self._pybullet_client.resetJointStateMultiDof(phys_model,leftShoulder, pose._leftShoulderRot, pose._leftShoulderVel) - self._pybullet_client.resetJointStateMultiDof(phys_model,leftElbow, pose._leftElbowRot, pose._leftElbowVel) + self._pybullet_client.resetJointStateMultiDof(phys_model, chest, pose._chestRot, + pose._chestVel) + self._pybullet_client.resetJointStateMultiDof(phys_model, neck, pose._neckRot, pose._neckVel) + self._pybullet_client.resetJointStateMultiDof(phys_model, rightHip, pose._rightHipRot, + pose._rightHipVel) + self._pybullet_client.resetJointStateMultiDof(phys_model, rightKnee, pose._rightKneeRot, + pose._rightKneeVel) + self._pybullet_client.resetJointStateMultiDof(phys_model, rightAnkle, pose._rightAnkleRot, + pose._rightAnkleVel) + self._pybullet_client.resetJointStateMultiDof(phys_model, rightShoulder, + pose._rightShoulderRot, pose._rightShoulderVel) + self._pybullet_client.resetJointStateMultiDof(phys_model, rightElbow, pose._rightElbowRot, + pose._rightElbowVel) + self._pybullet_client.resetJointStateMultiDof(phys_model, leftHip, pose._leftHipRot, + pose._leftHipVel) + self._pybullet_client.resetJointStateMultiDof(phys_model, leftKnee, pose._leftKneeRot, + pose._leftKneeVel) + self._pybullet_client.resetJointStateMultiDof(phys_model, leftAnkle, pose._leftAnkleRot, + pose._leftAnkleVel) + self._pybullet_client.resetJointStateMultiDof(phys_model, leftShoulder, + pose._leftShoulderRot, pose._leftShoulderVel) + self._pybullet_client.resetJointStateMultiDof(phys_model, leftElbow, pose._leftElbowRot, + pose._leftElbowVel) else: if initBase: - self._pybullet_client.resetBasePositionAndOrientation(phys_model, pose._basePos, pose._baseOrn) - self._pybullet_client.resetJointStateMultiDof(phys_model,chest,pose._chestRot, [0,0,0]) - self._pybullet_client.resetJointStateMultiDof(phys_model,neck,pose._neckRot, [0,0,0]) - self._pybullet_client.resetJointStateMultiDof(phys_model,rightHip,pose._rightHipRot, [0,0,0]) - self._pybullet_client.resetJointStateMultiDof(phys_model,rightKnee,pose._rightKneeRot, [0]) - self._pybullet_client.resetJointStateMultiDof(phys_model,rightAnkle,pose._rightAnkleRot, [0,0,0]) - self._pybullet_client.resetJointStateMultiDof(phys_model,rightShoulder,pose._rightShoulderRot, [0,0,0]) - self._pybullet_client.resetJointStateMultiDof(phys_model,rightElbow, pose._rightElbowRot, [0]) - self._pybullet_client.resetJointStateMultiDof(phys_model,leftHip, pose._leftHipRot, [0,0,0]) - self._pybullet_client.resetJointStateMultiDof(phys_model,leftKnee, pose._leftKneeRot, [0]) - self._pybullet_client.resetJointStateMultiDof(phys_model,leftAnkle, pose._leftAnkleRot, [0,0,0]) - self._pybullet_client.resetJointStateMultiDof(phys_model,leftShoulder, pose._leftShoulderRot, [0,0,0]) - self._pybullet_client.resetJointStateMultiDof(phys_model,leftElbow, pose._leftElbowRot, [0]) + self._pybullet_client.resetBasePositionAndOrientation(phys_model, pose._basePos, + pose._baseOrn) + self._pybullet_client.resetJointStateMultiDof(phys_model, chest, pose._chestRot, [0, 0, 0]) + self._pybullet_client.resetJointStateMultiDof(phys_model, neck, pose._neckRot, [0, 0, 0]) + self._pybullet_client.resetJointStateMultiDof(phys_model, rightHip, pose._rightHipRot, + [0, 0, 0]) + self._pybullet_client.resetJointStateMultiDof(phys_model, rightKnee, pose._rightKneeRot, [0]) + self._pybullet_client.resetJointStateMultiDof(phys_model, rightAnkle, pose._rightAnkleRot, + [0, 0, 0]) + self._pybullet_client.resetJointStateMultiDof(phys_model, rightShoulder, + pose._rightShoulderRot, [0, 0, 0]) + self._pybullet_client.resetJointStateMultiDof(phys_model, rightElbow, pose._rightElbowRot, + [0]) + self._pybullet_client.resetJointStateMultiDof(phys_model, leftHip, pose._leftHipRot, + [0, 0, 0]) + self._pybullet_client.resetJointStateMultiDof(phys_model, leftKnee, pose._leftKneeRot, [0]) + self._pybullet_client.resetJointStateMultiDof(phys_model, leftAnkle, pose._leftAnkleRot, + [0, 0, 0]) + self._pybullet_client.resetJointStateMultiDof(phys_model, leftShoulder, + pose._leftShoulderRot, [0, 0, 0]) + self._pybullet_client.resetJointStateMultiDof(phys_model, leftElbow, pose._leftElbowRot, [0]) def calcCycleCount(self, simTime, cycleTime): - phases = simTime / cycleTime; + phases = simTime / cycleTime count = math.floor(phases) loop = True #count = (loop) ? count : cMathUtil::Clamp(count, 0, 1); return count - def getCycleTime(self): keyFrameDuration = self._mocap_data.KeyFrameDuraction() - cycleTime = keyFrameDuration*(self._mocap_data.NumFrames()-1) + cycleTime = keyFrameDuration * (self._mocap_data.NumFrames() - 1) return cycleTime - + def setSimTime(self, t): self._simTime = t #print("SetTimeTime time =",t) @@ -130,163 +212,266 @@ class QuadrupedStablePD(object): #print("self._motion_data.NumFrames()=",self._mocap_data.NumFrames()) self._cycleCount = self.calcCycleCount(t, cycleTime) #print("cycles=",cycles) - frameTime = t - self._cycleCount*cycleTime - if (frameTime<0): + frameTime = t - self._cycleCount * cycleTime + if (frameTime < 0): frameTime += cycleTime - - #print("keyFrameDuration=",keyFrameDuration) + + #print("keyFrameDuration=",keyFrameDuration) #print("frameTime=",frameTime) - self._frame = int(frameTime/keyFrameDuration) + self._frame = int(frameTime / keyFrameDuration) #print("self._frame=",self._frame) - - self._frameNext = self._frame+1 - if (self._frameNext >= self._mocap_data.NumFrames()): + + self._frameNext = self._frame + 1 + if (self._frameNext >= self._mocap_data.NumFrames()): self._frameNext = self._frame - self._frameFraction = (frameTime - self._frame*keyFrameDuration)/(keyFrameDuration) - - + self._frameFraction = (frameTime - self._frame * keyFrameDuration) / (keyFrameDuration) + def computeCycleOffset(self): - firstFrame=0 - lastFrame = self._mocap_data.NumFrames()-1 + firstFrame = 0 + lastFrame = self._mocap_data.NumFrames() - 1 frameData = self._mocap_data._motion_data['Frames'][0] frameDataNext = self._mocap_data._motion_data['Frames'][lastFrame] - - basePosStart = [frameData[1],frameData[2],frameData[3]] - basePosEnd = [frameDataNext[1],frameDataNext[2],frameDataNext[3]] - self._cycleOffset = [basePosEnd[0]-basePosStart[0],basePosEnd[1]-basePosStart[1],basePosEnd[2]-basePosStart[2]] + + basePosStart = [frameData[1], frameData[2], frameData[3]] + basePosEnd = [frameDataNext[1], frameDataNext[2], frameDataNext[3]] + self._cycleOffset = [ + basePosEnd[0] - basePosStart[0], basePosEnd[1] - basePosStart[1], + basePosEnd[2] - basePosStart[2] + ] return self._cycleOffset - + def computePose(self, frameFraction): frameData = self._mocap_data._motion_data['Frames'][self._frame] frameDataNext = self._mocap_data._motion_data['Frames'][self._frameNext] - + self._poseInterpolator.Slerp(frameFraction, frameData, frameDataNext, self._pybullet_client) #print("self._poseInterpolator.Slerp(", frameFraction,")=", pose) self.computeCycleOffset() oldPos = self._poseInterpolator._basePos - self._poseInterpolator._basePos = [oldPos[0]+self._cycleCount*self._cycleOffset[0],oldPos[1]+self._cycleCount*self._cycleOffset[1],oldPos[2]+self._cycleCount*self._cycleOffset[2]] + self._poseInterpolator._basePos = [ + oldPos[0] + self._cycleCount * self._cycleOffset[0], + oldPos[1] + self._cycleCount * self._cycleOffset[1], + oldPos[2] + self._cycleCount * self._cycleOffset[2] + ] pose = self._poseInterpolator.GetPose() - - return pose + return pose def convertActionToPose(self, action): pose = self._poseInterpolator.ConvertFromAction(self._pybullet_client, action) - return pose + return pose def computePDForces(self, desiredPositions, desiredVelocities, maxForces): - if desiredVelocities==None: - desiredVelocities = [0]*self._totalDofs - - - taus = self._stablePD.computePD(bodyUniqueId=self._sim_model, - jointIndices = self._jointIndicesAll, - desiredPositions = desiredPositions, - desiredVelocities = desiredVelocities, - kps = self._kpOrg, - kds = self._kdOrg, - maxForces = maxForces, - timeStep=self._timeStep) + if desiredVelocities == None: + desiredVelocities = [0] * self._totalDofs + + taus = self._stablePD.computePD(bodyUniqueId=self._sim_model, + jointIndices=self._jointIndicesAll, + desiredPositions=desiredPositions, + desiredVelocities=desiredVelocities, + kps=self._kpOrg, + kds=self._kdOrg, + maxForces=maxForces, + timeStep=self._timeStep) return taus def applyPDForces(self, taus): - dofIndex=7 + dofIndex = 7 scaling = 1 - for index in range (len(self._jointIndicesAll)): + for index in range(len(self._jointIndicesAll)): jointIndex = self._jointIndicesAll[index] - if self._jointDofCounts[index]==4: - force=[scaling*taus[dofIndex+0],scaling*taus[dofIndex+1],scaling*taus[dofIndex+2]] - #print("force[", jointIndex,"]=",force) - self._pybullet_client.setJointMotorControlMultiDof(self._sim_model,jointIndex,self._pybullet_client.TORQUE_CONTROL,force=force) - if self._jointDofCounts[index]==1: - force=[scaling*taus[dofIndex]] - #print("force[", jointIndex,"]=",force) - self._pybullet_client.setJointMotorControlMultiDof(self._sim_model, jointIndex, controlMode=self._pybullet_client.TORQUE_CONTROL, force=force) - dofIndex+=self._jointDofCounts[index] - + if self._jointDofCounts[index] == 4: + force = [ + scaling * taus[dofIndex + 0], scaling * taus[dofIndex + 1], + scaling * taus[dofIndex + 2] + ] + #print("force[", jointIndex,"]=",force) + self._pybullet_client.setJointMotorControlMultiDof(self._sim_model, + jointIndex, + self._pybullet_client.TORQUE_CONTROL, + force=force) + if self._jointDofCounts[index] == 1: + force = [scaling * taus[dofIndex]] + #print("force[", jointIndex,"]=",force) + self._pybullet_client.setJointMotorControlMultiDof( + self._sim_model, + jointIndex, + controlMode=self._pybullet_client.TORQUE_CONTROL, + force=force) + dofIndex += self._jointDofCounts[index] + def setJointMotors(self, desiredPositions, maxForces): controlMode = self._pybullet_client.POSITION_CONTROL - startIndex=7 - chest=1 - neck=2 - rightHip=3 - rightKnee=4 - rightAnkle=5 - rightShoulder=6 - rightElbow=7 - leftHip=9 - leftKnee=10 - leftAnkle=11 - leftShoulder=12 - leftElbow=13 + startIndex = 7 + chest = 1 + neck = 2 + rightHip = 3 + rightKnee = 4 + rightAnkle = 5 + rightShoulder = 6 + rightElbow = 7 + leftHip = 9 + leftKnee = 10 + leftAnkle = 11 + leftShoulder = 12 + leftElbow = 13 kp = 0.2 - - forceScale=1 + + forceScale = 1 #self._jointDofCounts=[4,4,4,1,4,4,1,4,1,4,4,1] - maxForce = [forceScale*maxForces[startIndex],forceScale*maxForces[startIndex+1],forceScale*maxForces[startIndex+2],forceScale*maxForces[startIndex+3]] - startIndex+=4 - self._pybullet_client.setJointMotorControlMultiDof(self._sim_model,chest,controlMode, targetPosition=self._poseInterpolator._chestRot,positionGain=kp, force=maxForce) - maxForce = [maxForces[startIndex],maxForces[startIndex+1],maxForces[startIndex+2],maxForces[startIndex+3]] - startIndex+=4 - self._pybullet_client.setJointMotorControlMultiDof(self._sim_model,neck,controlMode,targetPosition=self._poseInterpolator._neckRot,positionGain=kp, force=maxForce) - maxForce = [maxForces[startIndex],maxForces[startIndex+1],maxForces[startIndex+2],maxForces[startIndex+3]] - startIndex+=4 - self._pybullet_client.setJointMotorControlMultiDof(self._sim_model,rightHip,controlMode,targetPosition=self._poseInterpolator._rightHipRot,positionGain=kp, force=maxForce) - maxForce = [forceScale*maxForces[startIndex]] - startIndex+=1 - self._pybullet_client.setJointMotorControlMultiDof(self._sim_model,rightKnee,controlMode,targetPosition=self._poseInterpolator._rightKneeRot,positionGain=kp, force=maxForce) - maxForce = [maxForces[startIndex],maxForces[startIndex+1],maxForces[startIndex+2],maxForces[startIndex+3]] - startIndex+=4 - self._pybullet_client.setJointMotorControlMultiDof(self._sim_model,rightAnkle,controlMode,targetPosition=self._poseInterpolator._rightAnkleRot,positionGain=kp, force=maxForce) - maxForce = [forceScale*maxForces[startIndex],forceScale*maxForces[startIndex+1],forceScale*maxForces[startIndex+2],forceScale*maxForces[startIndex+3]] - startIndex+=4 - maxForce = [forceScale*maxForces[startIndex]] - startIndex+=1 - self._pybullet_client.setJointMotorControlMultiDof(self._sim_model,rightElbow, controlMode,targetPosition=self._poseInterpolator._rightElbowRot,positionGain=kp, force=maxForce) - maxForce = [maxForces[startIndex],maxForces[startIndex+1],maxForces[startIndex+2],maxForces[startIndex+3]] - startIndex+=4 - self._pybullet_client.setJointMotorControlMultiDof(self._sim_model,leftHip, controlMode,targetPosition=self._poseInterpolator._leftHipRot,positionGain=kp, force=maxForce) - maxForce = [forceScale*maxForces[startIndex]] - startIndex+=1 - self._pybullet_client.setJointMotorControlMultiDof(self._sim_model,leftKnee, controlMode,targetPosition=self._poseInterpolator._leftKneeRot,positionGain=kp, force=maxForce) - maxForce = [maxForces[startIndex],maxForces[startIndex+1],maxForces[startIndex+2],maxForces[startIndex+3]] - startIndex+=4 - self._pybullet_client.setJointMotorControlMultiDof(self._sim_model,leftAnkle, controlMode,targetPosition=self._poseInterpolator._leftAnkleRot,positionGain=kp, force=maxForce) - maxForce = [maxForces[startIndex],maxForces[startIndex+1],maxForces[startIndex+2],maxForces[startIndex+3]] - startIndex+=4 - self._pybullet_client.setJointMotorControlMultiDof(self._sim_model,leftShoulder, controlMode,targetPosition=self._poseInterpolator._leftShoulderRot,positionGain=kp, force=maxForce) - maxForce = [forceScale*maxForces[startIndex]] - startIndex+=1 - self._pybullet_client.setJointMotorControlMultiDof(self._sim_model,leftElbow, controlMode,targetPosition=self._poseInterpolator._leftElbowRot,positionGain=kp, force=maxForce) + maxForce = [ + forceScale * maxForces[startIndex], forceScale * maxForces[startIndex + 1], + forceScale * maxForces[startIndex + 2], forceScale * maxForces[startIndex + 3] + ] + startIndex += 4 + self._pybullet_client.setJointMotorControlMultiDof( + self._sim_model, + chest, + controlMode, + targetPosition=self._poseInterpolator._chestRot, + positionGain=kp, + force=maxForce) + maxForce = [ + maxForces[startIndex], maxForces[startIndex + 1], maxForces[startIndex + 2], + maxForces[startIndex + 3] + ] + startIndex += 4 + self._pybullet_client.setJointMotorControlMultiDof( + self._sim_model, + neck, + controlMode, + targetPosition=self._poseInterpolator._neckRot, + positionGain=kp, + force=maxForce) + maxForce = [ + maxForces[startIndex], maxForces[startIndex + 1], maxForces[startIndex + 2], + maxForces[startIndex + 3] + ] + startIndex += 4 + self._pybullet_client.setJointMotorControlMultiDof( + self._sim_model, + rightHip, + controlMode, + targetPosition=self._poseInterpolator._rightHipRot, + positionGain=kp, + force=maxForce) + maxForce = [forceScale * maxForces[startIndex]] + startIndex += 1 + self._pybullet_client.setJointMotorControlMultiDof( + self._sim_model, + rightKnee, + controlMode, + targetPosition=self._poseInterpolator._rightKneeRot, + positionGain=kp, + force=maxForce) + maxForce = [ + maxForces[startIndex], maxForces[startIndex + 1], maxForces[startIndex + 2], + maxForces[startIndex + 3] + ] + startIndex += 4 + self._pybullet_client.setJointMotorControlMultiDof( + self._sim_model, + rightAnkle, + controlMode, + targetPosition=self._poseInterpolator._rightAnkleRot, + positionGain=kp, + force=maxForce) + maxForce = [ + forceScale * maxForces[startIndex], forceScale * maxForces[startIndex + 1], + forceScale * maxForces[startIndex + 2], forceScale * maxForces[startIndex + 3] + ] + startIndex += 4 + maxForce = [forceScale * maxForces[startIndex]] + startIndex += 1 + self._pybullet_client.setJointMotorControlMultiDof( + self._sim_model, + rightElbow, + controlMode, + targetPosition=self._poseInterpolator._rightElbowRot, + positionGain=kp, + force=maxForce) + maxForce = [ + maxForces[startIndex], maxForces[startIndex + 1], maxForces[startIndex + 2], + maxForces[startIndex + 3] + ] + startIndex += 4 + self._pybullet_client.setJointMotorControlMultiDof( + self._sim_model, + leftHip, + controlMode, + targetPosition=self._poseInterpolator._leftHipRot, + positionGain=kp, + force=maxForce) + maxForce = [forceScale * maxForces[startIndex]] + startIndex += 1 + self._pybullet_client.setJointMotorControlMultiDof( + self._sim_model, + leftKnee, + controlMode, + targetPosition=self._poseInterpolator._leftKneeRot, + positionGain=kp, + force=maxForce) + maxForce = [ + maxForces[startIndex], maxForces[startIndex + 1], maxForces[startIndex + 2], + maxForces[startIndex + 3] + ] + startIndex += 4 + self._pybullet_client.setJointMotorControlMultiDof( + self._sim_model, + leftAnkle, + controlMode, + targetPosition=self._poseInterpolator._leftAnkleRot, + positionGain=kp, + force=maxForce) + maxForce = [ + maxForces[startIndex], maxForces[startIndex + 1], maxForces[startIndex + 2], + maxForces[startIndex + 3] + ] + startIndex += 4 + self._pybullet_client.setJointMotorControlMultiDof( + self._sim_model, + leftShoulder, + controlMode, + targetPosition=self._poseInterpolator._leftShoulderRot, + positionGain=kp, + force=maxForce) + maxForce = [forceScale * maxForces[startIndex]] + startIndex += 1 + self._pybullet_client.setJointMotorControlMultiDof( + self._sim_model, + leftElbow, + controlMode, + targetPosition=self._poseInterpolator._leftElbowRot, + positionGain=kp, + force=maxForce) #print("startIndex=",startIndex) - + def getPhase(self): keyFrameDuration = self._mocap_data.KeyFrameDuraction() - cycleTime = keyFrameDuration*(self._mocap_data.NumFrames()-1) + cycleTime = keyFrameDuration * (self._mocap_data.NumFrames() - 1) phase = self._simTime / cycleTime - phase = math.fmod(phase,1.0) - if (phase<0): + phase = math.fmod(phase, 1.0) + if (phase < 0): phase += 1 return phase def buildHeadingTrans(self, rootOrn): #align root transform 'forward' with world-space x axis eul = self._pybullet_client.getEulerFromQuaternion(rootOrn) - refDir = [1,0,0] + refDir = [1, 0, 0] rotVec = self._pybullet_client.rotateVector(rootOrn, refDir) heading = math.atan2(-rotVec[2], rotVec[0]) - heading2=eul[1] + heading2 = eul[1] #print("heading=",heading) - headingOrn = self._pybullet_client.getQuaternionFromAxisAngle([0,1,0],-heading) + headingOrn = self._pybullet_client.getQuaternionFromAxisAngle([0, 1, 0], -heading) return headingOrn - def buildOriginTrans(self): - rootPos,rootOrn = self._pybullet_client.getBasePositionAndOrientation(self._sim_model) - + rootPos, rootOrn = self._pybullet_client.getBasePositionAndOrientation(self._sim_model) + #print("rootPos=",rootPos, " rootOrn=",rootOrn) - invRootPos=[-rootPos[0], 0, -rootPos[2]] + invRootPos = [-rootPos[0], 0, -rootPos[2]] #invOrigTransPos, invOrigTransOrn = self._pybullet_client.invertTransform(rootPos,rootOrn) headingOrn = self.buildHeadingTrans(rootOrn) #print("headingOrn=",headingOrn) @@ -294,8 +479,11 @@ class QuadrupedStablePD(object): #print("headingMat=",headingMat) #dummy, rootOrnWithoutHeading = self._pybullet_client.multiplyTransforms([0,0,0],headingOrn, [0,0,0], rootOrn) #dummy, invOrigTransOrn = self._pybullet_client.multiplyTransforms([0,0,0],rootOrnWithoutHeading, invOrigTransPos, invOrigTransOrn) - - invOrigTransPos, invOrigTransOrn = self._pybullet_client.multiplyTransforms( [0,0,0],headingOrn, invRootPos,[0,0,0,1]) + + invOrigTransPos, invOrigTransOrn = self._pybullet_client.multiplyTransforms([0, 0, 0], + headingOrn, + invRootPos, + [0, 0, 0, 1]) #print("invOrigTransPos=",invOrigTransPos) #print("invOrigTransOrn=",invOrigTransOrn) invOrigTransMat = self._pybullet_client.getMatrixFromQuaternion(invOrigTransOrn) @@ -308,106 +496,115 @@ class QuadrupedStablePD(object): phase = self.getPhase() #print("phase=",phase) stateVector.append(phase) - - rootTransPos, rootTransOrn=self.buildOriginTrans() - basePos,baseOrn = self._pybullet_client.getBasePositionAndOrientation(self._sim_model) - - rootPosRel, dummy = self._pybullet_client.multiplyTransforms(rootTransPos, rootTransOrn, basePos,[0,0,0,1]) + + rootTransPos, rootTransOrn = self.buildOriginTrans() + basePos, baseOrn = self._pybullet_client.getBasePositionAndOrientation(self._sim_model) + + rootPosRel, dummy = self._pybullet_client.multiplyTransforms(rootTransPos, rootTransOrn, + basePos, [0, 0, 0, 1]) #print("!!!rootPosRel =",rootPosRel ) #print("rootTransPos=",rootTransPos) #print("basePos=",basePos) - localPos,localOrn = self._pybullet_client.multiplyTransforms( rootTransPos, rootTransOrn , basePos,baseOrn ) - - localPos=[localPos[0]-rootPosRel[0],localPos[1]-rootPosRel[1],localPos[2]-rootPosRel[2]] + localPos, localOrn = self._pybullet_client.multiplyTransforms(rootTransPos, rootTransOrn, + basePos, baseOrn) + + localPos = [ + localPos[0] - rootPosRel[0], localPos[1] - rootPosRel[1], localPos[2] - rootPosRel[2] + ] #print("localPos=",localPos) - + stateVector.append(rootPosRel[1]) - + #self.pb2dmJoints=[0,1,2,9,10,11,3,4,5,12,13,14,6,7,8] - self.pb2dmJoints=[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14] - - for pbJoint in range (self._pybullet_client.getNumJoints(self._sim_model)): + self.pb2dmJoints = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14] + + for pbJoint in range(self._pybullet_client.getNumJoints(self._sim_model)): j = self.pb2dmJoints[pbJoint] #print("joint order:",j) ls = self._pybullet_client.getLinkState(self._sim_model, j, computeForwardKinematics=True) linkPos = ls[0] linkOrn = ls[1] - linkPosLocal, linkOrnLocal = self._pybullet_client.multiplyTransforms(rootTransPos, rootTransOrn, linkPos,linkOrn) - if (linkOrnLocal[3]<0): - linkOrnLocal=[-linkOrnLocal[0],-linkOrnLocal[1],-linkOrnLocal[2],-linkOrnLocal[3]] - linkPosLocal=[linkPosLocal[0]-rootPosRel[0],linkPosLocal[1]-rootPosRel[1],linkPosLocal[2]-rootPosRel[2]] + linkPosLocal, linkOrnLocal = self._pybullet_client.multiplyTransforms( + rootTransPos, rootTransOrn, linkPos, linkOrn) + if (linkOrnLocal[3] < 0): + linkOrnLocal = [-linkOrnLocal[0], -linkOrnLocal[1], -linkOrnLocal[2], -linkOrnLocal[3]] + linkPosLocal = [ + linkPosLocal[0] - rootPosRel[0], linkPosLocal[1] - rootPosRel[1], + linkPosLocal[2] - rootPosRel[2] + ] for l in linkPosLocal: stateVector.append(l) #re-order the quaternion, DeepMimic uses w,x,y,z - - if (linkOrnLocal[3]<0): - linkOrnLocal[0]*=-1 - linkOrnLocal[1]*=-1 - linkOrnLocal[2]*=-1 - linkOrnLocal[3]*=-1 - + + if (linkOrnLocal[3] < 0): + linkOrnLocal[0] *= -1 + linkOrnLocal[1] *= -1 + linkOrnLocal[2] *= -1 + linkOrnLocal[3] *= -1 + stateVector.append(linkOrnLocal[3]) stateVector.append(linkOrnLocal[0]) stateVector.append(linkOrnLocal[1]) stateVector.append(linkOrnLocal[2]) - - - for pbJoint in range (self._pybullet_client.getNumJoints(self._sim_model)): + + for pbJoint in range(self._pybullet_client.getNumJoints(self._sim_model)): j = self.pb2dmJoints[pbJoint] ls = self._pybullet_client.getLinkState(self._sim_model, j, computeLinkVelocity=True) linkLinVel = ls[6] linkAngVel = ls[7] - linkLinVelLocal , unused = self._pybullet_client.multiplyTransforms([0,0,0], rootTransOrn, linkLinVel,[0,0,0,1]) + linkLinVelLocal, unused = self._pybullet_client.multiplyTransforms([0, 0, 0], rootTransOrn, + linkLinVel, [0, 0, 0, 1]) #linkLinVelLocal=[linkLinVelLocal[0]-rootPosRel[0],linkLinVelLocal[1]-rootPosRel[1],linkLinVelLocal[2]-rootPosRel[2]] - linkAngVelLocal ,unused = self._pybullet_client.multiplyTransforms([0,0,0], rootTransOrn, linkAngVel,[0,0,0,1]) - + linkAngVelLocal, unused = self._pybullet_client.multiplyTransforms([0, 0, 0], rootTransOrn, + linkAngVel, [0, 0, 0, 1]) + for l in linkLinVelLocal: stateVector.append(l) for l in linkAngVelLocal: stateVector.append(l) - - #print("stateVector len=",len(stateVector)) + + #print("stateVector len=",len(stateVector)) #for st in range (len(stateVector)): # print("state[",st,"]=",stateVector[st]) return stateVector - + def terminates(self): #check if any non-allowed body part hits the ground - terminates=False + terminates = False pts = self._pybullet_client.getContactPoints() for p in pts: part = -1 #ignore self-collision - if (p[1]==p[2]): + if (p[1] == p[2]): continue - if (p[1]==self._sim_model): - part=p[3] - if (p[2]==self._sim_model): - part=p[4] - if (part >=0 and part not in self._allowed_body_parts): + if (p[1] == self._sim_model): + part = p[3] + if (p[2] == self._sim_model): + part = p[4] + if (part >= 0 and part not in self._allowed_body_parts): #print("terminating part:", part) - terminates=True - + terminates = True + return terminates - + def quatMul(self, q1, q2): - return [ q1[3] * q2[0] + q1[0] * q2[3] + q1[1] * q2[2] - q1[2] * q2[1], - q1[3] * q2[1] + q1[1] * q2[3] + q1[2] * q2[0] - q1[0] * q2[2], - q1[3] * q2[2] + q1[2] * q2[3] + q1[0] * q2[1] - q1[1] * q2[0], - q1[3] * q2[3] - q1[0] * q2[0] - q1[1] * q2[1] - q1[2] * q2[2]] - + return [ + q1[3] * q2[0] + q1[0] * q2[3] + q1[1] * q2[2] - q1[2] * q2[1], + q1[3] * q2[1] + q1[1] * q2[3] + q1[2] * q2[0] - q1[0] * q2[2], + q1[3] * q2[2] + q1[2] * q2[3] + q1[0] * q2[1] - q1[1] * q2[0], + q1[3] * q2[3] - q1[0] * q2[0] - q1[1] * q2[1] - q1[2] * q2[2] + ] + def calcRootAngVelErr(self, vel0, vel1): - diff = [vel0[0]-vel1[0],vel0[1]-vel1[1], vel0[2]-vel1[2]] - return diff[0]*diff[0]+diff[1]*diff[1]+diff[2]*diff[2] + diff = [vel0[0] - vel1[0], vel0[1] - vel1[1], vel0[2] - vel1[2]] + return diff[0] * diff[0] + diff[1] * diff[1] + diff[2] * diff[2] - - - def calcRootRotDiff(self,orn0, orn1): - orn0Conj = [-orn0[0],-orn0[1],-orn0[2],orn0[3]] + def calcRootRotDiff(self, orn0, orn1): + orn0Conj = [-orn0[0], -orn0[1], -orn0[2], orn0[3]] q_diff = self.quatMul(orn1, orn0Conj) - axis,angle = self._pybullet_client.getAxisAngleFromQuaternion(q_diff) - return angle*angle - + axis, angle = self._pybullet_client.getAxisAngleFromQuaternion(q_diff) + return angle * angle + def getReward(self, pose): #from DeepMimic double cSceneImitate::CalcRewardImitate #todo: compensate for ground height in some parts, once we move to non-flat terrain @@ -415,7 +612,7 @@ class QuadrupedStablePD(object): vel_w = 0.05 end_eff_w = 0.15 root_w = 0.2 - com_w = 0 #0.1 + com_w = 0 #0.1 total_w = pose_w + vel_w + end_eff_w + root_w + com_w pose_w /= total_w @@ -439,9 +636,9 @@ class QuadrupedStablePD(object): root_err = 0 com_err = 0 heading_err = 0 - + #create a mimic reward, comparing the dynamics humanoid with a kinematic one - + #pose = self.InitializePoseFromMotionData() #print("self._kin_model=",self._kin_model) #print("kinematicHumanoid #joints=",self._pybullet_client.getNumJoints(self._kin_model)) @@ -470,91 +667,102 @@ class QuadrupedStablePD(object): #tVector root_ang_vel0 = cKinTree::GetRootAngVel(joint_mat, vel0); #tVector root_ang_vel1 = cKinTree::GetRootAngVel(joint_mat, vel1); - mJointWeights = [0.20833,0.10416, 0.0625, 0.10416, - 0.0625, 0.041666666666666671, 0.0625, 0.0416, - 0.00, 0.10416, 0.0625, 0.0416, 0.0625, 0.0416, 0.0000] - + mJointWeights = [ + 0.20833, 0.10416, 0.0625, 0.10416, 0.0625, 0.041666666666666671, 0.0625, 0.0416, 0.00, + 0.10416, 0.0625, 0.0416, 0.0625, 0.0416, 0.0000 + ] + num_end_effs = 0 num_joints = 15 - + root_rot_w = mJointWeights[root_id] - rootPosSim,rootOrnSim = self._pybullet_client.getBasePositionAndOrientation(self._sim_model) - rootPosKin ,rootOrnKin = self._pybullet_client.getBasePositionAndOrientation(self._kin_model) + rootPosSim, rootOrnSim = self._pybullet_client.getBasePositionAndOrientation(self._sim_model) + rootPosKin, rootOrnKin = self._pybullet_client.getBasePositionAndOrientation(self._kin_model) linVelSim, angVelSim = self._pybullet_client.getBaseVelocity(self._sim_model) linVelKin, angVelKin = self._pybullet_client.getBaseVelocity(self._kin_model) - - - root_rot_err = self.calcRootRotDiff(rootOrnSim,rootOrnKin) + + root_rot_err = self.calcRootRotDiff(rootOrnSim, rootOrnKin) pose_err += root_rot_w * root_rot_err - - - root_vel_diff = [linVelSim[0]-linVelKin[0],linVelSim[1]-linVelKin[1],linVelSim[2]-linVelKin[2]] - root_vel_err = root_vel_diff[0]*root_vel_diff[0]+root_vel_diff[1]*root_vel_diff[1]+root_vel_diff[2]*root_vel_diff[2] - - root_ang_vel_err = self.calcRootAngVelErr( angVelSim, angVelKin) + + root_vel_diff = [ + linVelSim[0] - linVelKin[0], linVelSim[1] - linVelKin[1], linVelSim[2] - linVelKin[2] + ] + root_vel_err = root_vel_diff[0] * root_vel_diff[0] + root_vel_diff[1] * root_vel_diff[ + 1] + root_vel_diff[2] * root_vel_diff[2] + + root_ang_vel_err = self.calcRootAngVelErr(angVelSim, angVelKin) vel_err += root_rot_w * root_ang_vel_err - for j in range (num_joints): + for j in range(num_joints): curr_pose_err = 0 curr_vel_err = 0 - w = mJointWeights[j]; - + w = mJointWeights[j] + simJointInfo = self._pybullet_client.getJointStateMultiDof(self._sim_model, j) - + #print("simJointInfo.pos=",simJointInfo[0]) #print("simJointInfo.vel=",simJointInfo[1]) - kinJointInfo = self._pybullet_client.getJointStateMultiDof(self._kin_model,j) + kinJointInfo = self._pybullet_client.getJointStateMultiDof(self._kin_model, j) #print("kinJointInfo.pos=",kinJointInfo[0]) #print("kinJointInfo.vel=",kinJointInfo[1]) - if (len(simJointInfo[0])==1): - angle = simJointInfo[0][0]-kinJointInfo[0][0] - curr_pose_err = angle*angle - velDiff = simJointInfo[1][0]-kinJointInfo[1][0] - curr_vel_err = velDiff*velDiff - if (len(simJointInfo[0])==4): + if (len(simJointInfo[0]) == 1): + angle = simJointInfo[0][0] - kinJointInfo[0][0] + curr_pose_err = angle * angle + velDiff = simJointInfo[1][0] - kinJointInfo[1][0] + curr_vel_err = velDiff * velDiff + if (len(simJointInfo[0]) == 4): #print("quaternion diff") - diffQuat = self._pybullet_client.getDifferenceQuaternion(simJointInfo[0],kinJointInfo[0]) - axis,angle = self._pybullet_client.getAxisAngleFromQuaternion(diffQuat) - curr_pose_err = angle*angle - diffVel = [simJointInfo[1][0]-kinJointInfo[1][0],simJointInfo[1][1]-kinJointInfo[1][1],simJointInfo[1][2]-kinJointInfo[1][2]] - curr_vel_err = diffVel[0]*diffVel[0]+diffVel[1]*diffVel[1]+diffVel[2]*diffVel[2] - - + diffQuat = self._pybullet_client.getDifferenceQuaternion(simJointInfo[0], kinJointInfo[0]) + axis, angle = self._pybullet_client.getAxisAngleFromQuaternion(diffQuat) + curr_pose_err = angle * angle + diffVel = [ + simJointInfo[1][0] - kinJointInfo[1][0], simJointInfo[1][1] - kinJointInfo[1][1], + simJointInfo[1][2] - kinJointInfo[1][2] + ] + curr_vel_err = diffVel[0] * diffVel[0] + diffVel[1] * diffVel[1] + diffVel[2] * diffVel[2] + pose_err += w * curr_pose_err vel_err += w * curr_vel_err - + is_end_eff = j in self._end_effectors if is_end_eff: - + linkStateSim = self._pybullet_client.getLinkState(self._sim_model, j) linkStateKin = self._pybullet_client.getLinkState(self._kin_model, j) linkPosSim = linkStateSim[0] linkPosKin = linkStateKin[0] - linkPosDiff = [linkPosSim[0]-linkPosKin[0],linkPosSim[1]-linkPosKin[1],linkPosSim[2]-linkPosKin[2]] - curr_end_err = linkPosDiff[0]*linkPosDiff[0]+linkPosDiff[1]*linkPosDiff[1]+linkPosDiff[2]*linkPosDiff[2] + linkPosDiff = [ + linkPosSim[0] - linkPosKin[0], linkPosSim[1] - linkPosKin[1], + linkPosSim[2] - linkPosKin[2] + ] + curr_end_err = linkPosDiff[0] * linkPosDiff[0] + linkPosDiff[1] * linkPosDiff[ + 1] + linkPosDiff[2] * linkPosDiff[2] end_eff_err += curr_end_err - num_end_effs+=1 - + num_end_effs += 1 + if (num_end_effs > 0): end_eff_err /= num_end_effs - + #double root_ground_h0 = mGround->SampleHeight(sim_char.GetRootPos()) #double root_ground_h1 = kin_char.GetOriginPos()[1] #root_pos0[1] -= root_ground_h0 #root_pos1[1] -= root_ground_h1 - root_pos_diff = [rootPosSim[0]-rootPosKin[0],rootPosSim[1]-rootPosKin[1],rootPosSim[2]-rootPosKin[2]] - root_pos_err = root_pos_diff[0]*root_pos_diff[0]+root_pos_diff[1]*root_pos_diff[1]+root_pos_diff[2]*root_pos_diff[2] - # + root_pos_diff = [ + rootPosSim[0] - rootPosKin[0], rootPosSim[1] - rootPosKin[1], rootPosSim[2] - rootPosKin[2] + ] + root_pos_err = root_pos_diff[0] * root_pos_diff[0] + root_pos_diff[1] * root_pos_diff[ + 1] + root_pos_diff[2] * root_pos_diff[2] + # #root_rot_err = cMathUtil::QuatDiffTheta(root_rot0, root_rot1) #root_rot_err *= root_rot_err #root_vel_err = (root_vel1 - root_vel0).squaredNorm() #root_ang_vel_err = (root_ang_vel1 - root_ang_vel0).squaredNorm() - root_err = root_pos_err + 0.1 * root_rot_err+ 0.01 * root_vel_err+ 0.001 * root_ang_vel_err - + root_err = root_pos_err + 0.1 * root_rot_err + 0.01 * root_vel_err + 0.001 * root_ang_vel_err + #com_err = 0.1 * (com_vel1_world - com_vel0_world).squaredNorm() - + #print("pose_err=",pose_err) #print("vel_err=",vel_err) pose_reward = math.exp(-err_scale * pose_scale * pose_err) @@ -565,7 +773,6 @@ class QuadrupedStablePD(object): reward = pose_w * pose_reward + vel_w * vel_reward + end_eff_w * end_eff_reward + root_w * root_reward + com_w * com_reward - # pose_reward,vel_reward,end_eff_reward, root_reward, com_reward); #print("reward=",reward) #print("pose_reward=",pose_reward) @@ -573,5 +780,5 @@ class QuadrupedStablePD(object): #print("end_eff_reward=",end_eff_reward) #print("root_reward=",root_reward) #print("com_reward=",com_reward) - + return reward diff --git a/examples/pybullet/gym/pybullet_envs/deep_mimic/env/testHumanoid.py b/examples/pybullet/gym/pybullet_envs/deep_mimic/env/testHumanoid.py index d34ffe794..c689a062a 100644 --- a/examples/pybullet/gym/pybullet_envs/deep_mimic/env/testHumanoid.py +++ b/examples/pybullet/gym/pybullet_envs/deep_mimic/env/testHumanoid.py @@ -8,87 +8,89 @@ import pybullet as p1 import humanoid_pose_interpolator import numpy as np -pybullet_client = bullet_client.BulletClient(connection_mode=p1.GUI) +pybullet_client = bullet_client.BulletClient(connection_mode=p1.GUI) pybullet_client.setAdditionalSearchPath(pybullet_data.getDataPath()) -z2y = pybullet_client.getQuaternionFromEuler([-math.pi*0.5,0,0]) +z2y = pybullet_client.getQuaternionFromEuler([-math.pi * 0.5, 0, 0]) #planeId = pybullet_client.loadURDF("plane.urdf",[0,0,0],z2y) -planeId= pybullet_client.loadURDF("plane_implicit.urdf",[0,0,0],z2y, useMaximalCoordinates=True) -pybullet_client.changeDynamics(planeId, linkIndex=-1, lateralFriction=0.9) +planeId = pybullet_client.loadURDF("plane_implicit.urdf", [0, 0, 0], + z2y, + useMaximalCoordinates=True) +pybullet_client.changeDynamics(planeId, linkIndex=-1, lateralFriction=0.9) #print("planeId=",planeId) -pybullet_client.configureDebugVisualizer(pybullet_client.COV_ENABLE_Y_AXIS_UP,1) -pybullet_client.setGravity(0,-9.8,0) +pybullet_client.configureDebugVisualizer(pybullet_client.COV_ENABLE_Y_AXIS_UP, 1) +pybullet_client.setGravity(0, -9.8, 0) pybullet_client.setPhysicsEngineParameter(numSolverIterations=10) - mocapData = motion_capture_data.MotionCaptureData() #motionPath = pybullet_data.getDataPath()+"/data/motions/humanoid3d_walk.txt" -motionPath = pybullet_data.getDataPath()+"/data/motions/humanoid3d_backflip.txt" +motionPath = pybullet_data.getDataPath() + "/data/motions/humanoid3d_backflip.txt" mocapData.Load(motionPath) -timeStep = 1./600 -useFixedBase=False +timeStep = 1. / 600 +useFixedBase = False humanoid = humanoid_stable_pd.HumanoidStablePD(pybullet_client, mocapData, timeStep, useFixedBase) isInitialized = True pybullet_client.setTimeStep(timeStep) pybullet_client.setPhysicsEngineParameter(numSubSteps=2) -timeId = pybullet_client.addUserDebugParameter("time",0,10,0) +timeId = pybullet_client.addUserDebugParameter("time", 0, 10, 0) + - def isKeyTriggered(keys, key): o = ord(key) if o in keys: return keys[ord(key)] & pybullet_client.KEY_WAS_TRIGGERED return False - + + animating = False singleStep = False - -t=0 +t = 0 while (1): keys = pybullet_client.getKeyboardEvents() #print(keys) if isKeyTriggered(keys, ' '): - animating = not animating + animating = not animating if isKeyTriggered(keys, 'b'): - singleStep = True - + singleStep = True + if animating or singleStep: - - + singleStep = False #t = pybullet_client.readUserDebugParameter(timeId) #print("t=",t) - for i in range (1): + for i in range(1): - print("t=",t) + print("t=", t) humanoid.setSimTime(t) - + humanoid.computePose(humanoid._frameFraction) pose = humanoid._poseInterpolator #humanoid.initializePose(pose=pose, phys_model = humanoid._sim_model, initBase=True, initializeVelocity=True) #humanoid.resetPose() - - + desiredPose = humanoid.computePose(humanoid._frameFraction) - #desiredPose = desiredPose.GetPose() + #desiredPose = desiredPose.GetPose() #curPose = HumanoidPoseInterpolator() #curPose.reset() s = humanoid.getState() #np.savetxt("pb_record_state_s.csv", s, delimiter=",") - maxForces = [0,0,0,0,0,0,0,200,200,200,200, 50,50,50,50, 200,200,200,200, 150, 90,90,90,90, 100,100,100,100, 60, 200,200,200,200, 150, 90, 90, 90, 90, 100,100,100,100, 60] + maxForces = [ + 0, 0, 0, 0, 0, 0, 0, 200, 200, 200, 200, 50, 50, 50, 50, 200, 200, 200, 200, 150, 90, 90, + 90, 90, 100, 100, 100, 100, 60, 200, 200, 200, 200, 150, 90, 90, 90, 90, 100, 100, 100, + 100, 60 + ] taus = humanoid.computePDForces(desiredPose, desiredVelocities=None, maxForces=maxForces) - + #print("taus=",taus) humanoid.applyPDForces(taus) - + pybullet_client.stepSimulation() - t+=1./600. - - - time.sleep(1./600.)
\ No newline at end of file + t += 1. / 600. + + time.sleep(1. / 600.) diff --git a/examples/pybullet/gym/pybullet_envs/deep_mimic/env/testLaikago.py b/examples/pybullet/gym/pybullet_envs/deep_mimic/env/testLaikago.py index c7543639f..ef2b21c33 100644 --- a/examples/pybullet/gym/pybullet_envs/deep_mimic/env/testLaikago.py +++ b/examples/pybullet/gym/pybullet_envs/deep_mimic/env/testLaikago.py @@ -8,240 +8,258 @@ import motion_capture_data import quadrupedPoseInterpolator useConstraints = False - -p = bullet_client.BulletClient(connection_mode=p1.GUI) + +p = bullet_client.BulletClient(connection_mode=p1.GUI) p.setAdditionalSearchPath(pybullet_data.getDataPath()) plane = p.loadURDF("plane.urdf") -p.setGravity(0,0,-10) -timeStep=1./500 +p.setGravity(0, 0, -10) +timeStep = 1. / 500 p.setTimeStep(timeStep) #p.setDefaultContactERP(0) -#urdfFlags = p.URDF_USE_SELF_COLLISION+p.URDF_USE_SELF_COLLISION_EXCLUDE_ALL_PARENTS +#urdfFlags = p.URDF_USE_SELF_COLLISION+p.URDF_USE_SELF_COLLISION_EXCLUDE_ALL_PARENTS urdfFlags = p.URDF_USE_SELF_COLLISION - -startPos=[0.007058990464444105, 0.03149299192130908, 0.4918981912395484] -startOrn=[0.005934649695708604, 0.7065453990917289, 0.7076373820553712, -0.0027774940359030264] -quadruped = p.loadURDF("laikago/laikago.urdf",startPos,startOrn, flags = urdfFlags,useFixedBase=False) -p.resetBasePositionAndOrientation(quadruped,startPos,startOrn) +startPos = [0.007058990464444105, 0.03149299192130908, 0.4918981912395484] +startOrn = [0.005934649695708604, 0.7065453990917289, 0.7076373820553712, -0.0027774940359030264] +quadruped = p.loadURDF("laikago/laikago.urdf", + startPos, + startOrn, + flags=urdfFlags, + useFixedBase=False) +p.resetBasePositionAndOrientation(quadruped, startPos, startOrn) if not useConstraints: for j in range(p.getNumJoints(quadruped)): - p.setJointMotorControl2(quadruped,j,p.POSITION_CONTROL,force=0) - -#This cube is added as a soft constraint to keep the laikago from falling + p.setJointMotorControl2(quadruped, j, p.POSITION_CONTROL, force=0) + +#This cube is added as a soft constraint to keep the laikago from falling #since we didn't train it yet, it doesn't balance -cube = p.loadURDF("cube_no_rotation.urdf",[0,0,-0.5],[0,0.5,0.5,0]) -p.setCollisionFilterGroupMask(cube,-1,0,0) +cube = p.loadURDF("cube_no_rotation.urdf", [0, 0, -0.5], [0, 0.5, 0.5, 0]) +p.setCollisionFilterGroupMask(cube, -1, 0, 0) for j in range(p.getNumJoints(cube)): - p.setJointMotorControl2(cube,j,p.POSITION_CONTROL,force=0) - p.setCollisionFilterGroupMask(cube,j,0,0) - p.changeVisualShape(cube,j,rgbaColor=[1,0,0,0]) -cid = p.createConstraint(cube,p.getNumJoints(cube)-1,quadruped,-1,p.JOINT_FIXED,[0,0,0],[0,1,0],[0,0,0]) + p.setJointMotorControl2(cube, j, p.POSITION_CONTROL, force=0) + p.setCollisionFilterGroupMask(cube, j, 0, 0) + p.changeVisualShape(cube, j, rgbaColor=[1, 0, 0, 0]) +cid = p.createConstraint(cube, + p.getNumJoints(cube) - 1, quadruped, -1, p.JOINT_FIXED, [0, 0, 0], + [0, 1, 0], [0, 0, 0]) p.changeConstraint(cid, maxForce=10) +jointIds = [] +paramIds = [] +jointOffsets = [] +jointDirections = [-1, 1, 1, 1, 1, 1, -1, 1, 1, 1, 1, 1] +jointAngles = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] -jointIds=[] -paramIds=[] -jointOffsets=[] -jointDirections=[-1,1,1,1,1,1,-1,1,1,1,1,1] -jointAngles=[0,0,0,0,0,0,0,0,0,0,0,0] - -for i in range (4): +for i in range(4): jointOffsets.append(0) jointOffsets.append(-0.7) jointOffsets.append(0.7) -maxForceId = p.addUserDebugParameter("maxForce",0,100,20) - -for j in range (p.getNumJoints(quadruped)): - p.changeDynamics(quadruped,j,linearDamping=0, angularDamping=0) - info = p.getJointInfo(quadruped,j) - #print(info) - jointName = info[1] - jointType = info[2] - if (jointType==p.JOINT_PRISMATIC or jointType==p.JOINT_REVOLUTE): - jointIds.append(j) +maxForceId = p.addUserDebugParameter("maxForce", 0, 100, 20) - -startQ=[0.08389, 0.8482, -1.547832, -0.068933, 0.625726, -1.272086, 0.074398, 0.61135, -1.255892, -0.068262, 0.836745, -1.534517] for j in range(p.getNumJoints(quadruped)): - p.resetJointState(quadruped,jointIds[j],jointDirections[j]*startQ[j]+jointOffsets[j]) - + p.changeDynamics(quadruped, j, linearDamping=0, angularDamping=0) + info = p.getJointInfo(quadruped, j) + #print(info) + jointName = info[1] + jointType = info[2] + if (jointType == p.JOINT_PRISMATIC or jointType == p.JOINT_REVOLUTE): + jointIds.append(j) + +startQ = [ + 0.08389, 0.8482, -1.547832, -0.068933, 0.625726, -1.272086, 0.074398, 0.61135, -1.255892, + -0.068262, 0.836745, -1.534517 +] +for j in range(p.getNumJoints(quadruped)): + p.resetJointState(quadruped, jointIds[j], jointDirections[j] * startQ[j] + jointOffsets[j]) qpi = quadrupedPoseInterpolator.QuadrupedPoseInterpolator() #enable collision between lower legs -for j in range (p.getNumJoints(quadruped)): - print(p.getJointInfo(quadruped,j)) +for j in range(p.getNumJoints(quadruped)): + print(p.getJointInfo(quadruped, j)) #2,5,8 and 11 are the lower legs -lower_legs = [2,5,8,11] +lower_legs = [2, 5, 8, 11] for l0 in lower_legs: - for l1 in lower_legs: - if (l1>l0): - enableCollision = 1 - print("collision for pair",l0,l1, p.getJointInfo(quadruped,l0)[12],p.getJointInfo(quadruped,l1)[12], "enabled=",enableCollision) - p.setCollisionFilterPair(quadruped, quadruped, 2,5,enableCollision) - -jointIds=[] -paramIds=[] -jointOffsets=[] -jointDirections=[-1,1,1,1,1,1,-1,1,1,1,1,1] -jointAngles=[0,0,0,0,0,0,0,0,0,0,0,0] - -for i in range (4): - jointOffsets.append(0) - jointOffsets.append(-0.7) - jointOffsets.append(0.7) - -maxForceId = p.addUserDebugParameter("maxForce",0,100,20) - -for j in range (p.getNumJoints(quadruped)): - p.changeDynamics(quadruped,j,linearDamping=0, angularDamping=0) - info = p.getJointInfo(quadruped,j) - #print(info) - jointName = info[1] - jointType = info[2] - if (jointType==p.JOINT_PRISMATIC or jointType==p.JOINT_REVOLUTE): - jointIds.append(j) - - -p.getCameraImage(480,320) -p.setRealTimeSimulation(0) + for l1 in lower_legs: + if (l1 > l0): + enableCollision = 1 + print("collision for pair", l0, l1, + p.getJointInfo(quadruped, l0)[12], + p.getJointInfo(quadruped, l1)[12], "enabled=", enableCollision) + p.setCollisionFilterPair(quadruped, quadruped, 2, 5, enableCollision) + +jointIds = [] +paramIds = [] +jointOffsets = [] +jointDirections = [-1, 1, 1, 1, 1, 1, -1, 1, 1, 1, 1, 1] +jointAngles = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] + +for i in range(4): + jointOffsets.append(0) + jointOffsets.append(-0.7) + jointOffsets.append(0.7) + +maxForceId = p.addUserDebugParameter("maxForce", 0, 100, 20) -joints=[] +for j in range(p.getNumJoints(quadruped)): + p.changeDynamics(quadruped, j, linearDamping=0, angularDamping=0) + info = p.getJointInfo(quadruped, j) + #print(info) + jointName = info[1] + jointType = info[2] + if (jointType == p.JOINT_PRISMATIC or jointType == p.JOINT_REVOLUTE): + jointIds.append(j) + +p.getCameraImage(480, 320) +p.setRealTimeSimulation(0) +joints = [] mocapData = motion_capture_data.MotionCaptureData() -motionPath = pybullet_data.getDataPath()+"/data/motions/laikago_walk.txt" +motionPath = pybullet_data.getDataPath() + "/data/motions/laikago_walk.txt" mocapData.Load(motionPath) -print("mocapData.NumFrames=",mocapData.NumFrames()) -print("mocapData.KeyFrameDuraction=",mocapData.KeyFrameDuraction()) -print("mocapData.getCycleTime=",mocapData.getCycleTime()) -print("mocapData.computeCycleOffset=",mocapData.computeCycleOffset()) +print("mocapData.NumFrames=", mocapData.NumFrames()) +print("mocapData.KeyFrameDuraction=", mocapData.KeyFrameDuraction()) +print("mocapData.getCycleTime=", mocapData.getCycleTime()) +print("mocapData.computeCycleOffset=", mocapData.computeCycleOffset()) stablePD = pd_controller_stable.PDControllerStable(p) cycleTime = mocapData.getCycleTime() -t=0 +t = 0 -while t<10.*cycleTime: +while t < 10. * cycleTime: #get interpolated joint keyFrameDuration = mocapData.KeyFrameDuraction() cycleTime = mocapData.getCycleTime() cycleCount = mocapData.calcCycleCount(t, cycleTime) - + #print("cycleTime=",cycleTime) #print("cycleCount=",cycleCount) - + #print("cycles=",cycles) - frameTime = t - cycleCount*cycleTime + frameTime = t - cycleCount * cycleTime #print("frameTime=",frameTime) - if (frameTime<0): + if (frameTime < 0): frameTime += cycleTime - frame = int(frameTime/keyFrameDuration) - frameNext = frame+1 - if (frameNext >= mocapData.NumFrames()): + frame = int(frameTime / keyFrameDuration) + frameNext = frame + 1 + if (frameNext >= mocapData.NumFrames()): frameNext = frame - frameFraction = (frameTime - frame*keyFrameDuration)/(keyFrameDuration) + frameFraction = (frameTime - frame * keyFrameDuration) / (keyFrameDuration) #print("frame=",frame) #print("frameFraction=",frameFraction) frameData = mocapData._motion_data['Frames'][frame] frameDataNext = mocapData._motion_data['Frames'][frameNext] - - jointsStr,qdot=qpi.Slerp(frameFraction, frameData, frameDataNext, p) - + + jointsStr, qdot = qpi.Slerp(frameFraction, frameData, frameDataNext, p) + maxForce = p.readUserDebugParameter(maxForceId) - print("jointIds=",jointIds) + print("jointIds=", jointIds) if useConstraints: - for j in range (12): + for j in range(12): #skip the base positional dofs - targetPos = float(jointsStr[j+7]) - p.setJointMotorControl2(quadruped,jointIds[j],p.POSITION_CONTROL,jointDirections[j]*targetPos+jointOffsets[j], force=maxForce) - + targetPos = float(jointsStr[j + 7]) + p.setJointMotorControl2(quadruped, + jointIds[j], + p.POSITION_CONTROL, + jointDirections[j] * targetPos + jointOffsets[j], + force=maxForce) + else: - desiredPositions=[] - for j in range (7): + desiredPositions = [] + for j in range(7): targetPosUnmodified = float(jointsStr[j]) desiredPositions.append(targetPosUnmodified) - for j in range (12): - targetPosUnmodified = float(jointsStr[j+7]) - targetPos=jointDirections[j]*targetPosUnmodified+jointOffsets[j] + for j in range(12): + targetPosUnmodified = float(jointsStr[j + 7]) + targetPos = jointDirections[j] * targetPosUnmodified + jointOffsets[j] desiredPositions.append(targetPos) - numBaseDofs=6 - totalDofs=12+numBaseDofs - desiredVelocities=None - if desiredVelocities==None: - desiredVelocities = [0]*totalDofs - taus = stablePD.computePD(bodyUniqueId=quadruped, - jointIndices = jointIds, - desiredPositions = desiredPositions, - desiredVelocities = desiredVelocities, - kps = [4000]*totalDofs, - kds = [40]*totalDofs, - maxForces = [500]*totalDofs, - timeStep=timeStep) - - dofIndex=6 + numBaseDofs = 6 + totalDofs = 12 + numBaseDofs + desiredVelocities = None + if desiredVelocities == None: + desiredVelocities = [0] * totalDofs + taus = stablePD.computePD(bodyUniqueId=quadruped, + jointIndices=jointIds, + desiredPositions=desiredPositions, + desiredVelocities=desiredVelocities, + kps=[4000] * totalDofs, + kds=[40] * totalDofs, + maxForces=[500] * totalDofs, + timeStep=timeStep) + + dofIndex = 6 scaling = 1 - for index in range (len(jointIds)): + for index in range(len(jointIds)): jointIndex = jointIds[index] - force=[scaling*taus[dofIndex]] - print("force[", jointIndex,"]=",force) - p.setJointMotorControlMultiDof(quadruped, jointIndex, controlMode=p.TORQUE_CONTROL, force=force) - dofIndex+=1 - + force = [scaling * taus[dofIndex]] + print("force[", jointIndex, "]=", force) + p.setJointMotorControlMultiDof(quadruped, + jointIndex, + controlMode=p.TORQUE_CONTROL, + force=force) + dofIndex += 1 p.stepSimulation() - t+=timeStep + t += timeStep time.sleep(timeStep) -useOrgData=False +useOrgData = False if useOrgData: - with open("data1.txt","r") as filestream: - for line in filestream: - maxForce = p.readUserDebugParameter(maxForceId) - currentline = line.split(",") - frame = currentline[0] - t = currentline[1] - joints=currentline[2:14] - for j in range (12): - targetPos = float(joints[j]) - p.setJointMotorControl2(quadruped,jointIds[j],p.POSITION_CONTROL,jointDirections[j]*targetPos+jointOffsets[j], force=maxForce) - p.stepSimulation() - for lower_leg in lower_legs: - #print("points for ", quadruped, " link: ", lower_leg) - pts = p.getContactPoints(quadruped,-1, lower_leg) - #print("num points=",len(pts)) - #for pt in pts: - # print(pt[9]) - time.sleep(1./500.) - - -for j in range (p.getNumJoints(quadruped)): - p.changeDynamics(quadruped,j,linearDamping=0, angularDamping=0) - info = p.getJointInfo(quadruped,j) - js = p.getJointState(quadruped,j) - #print(info) - jointName = info[1] - jointType = info[2] - if (jointType==p.JOINT_PRISMATIC or jointType==p.JOINT_REVOLUTE): - paramIds.append(p.addUserDebugParameter(jointName.decode("utf-8"),-4,4,(js[0]-jointOffsets[j])/jointDirections[j])) + with open("data1.txt", "r") as filestream: + for line in filestream: + maxForce = p.readUserDebugParameter(maxForceId) + currentline = line.split(",") + frame = currentline[0] + t = currentline[1] + joints = currentline[2:14] + for j in range(12): + targetPos = float(joints[j]) + p.setJointMotorControl2(quadruped, + jointIds[j], + p.POSITION_CONTROL, + jointDirections[j] * targetPos + jointOffsets[j], + force=maxForce) + p.stepSimulation() + for lower_leg in lower_legs: + #print("points for ", quadruped, " link: ", lower_leg) + pts = p.getContactPoints(quadruped, -1, lower_leg) + #print("num points=",len(pts)) + #for pt in pts: + # print(pt[9]) + time.sleep(1. / 500.) +for j in range(p.getNumJoints(quadruped)): + p.changeDynamics(quadruped, j, linearDamping=0, angularDamping=0) + info = p.getJointInfo(quadruped, j) + js = p.getJointState(quadruped, j) + #print(info) + jointName = info[1] + jointType = info[2] + if (jointType == p.JOINT_PRISMATIC or jointType == p.JOINT_REVOLUTE): + paramIds.append( + p.addUserDebugParameter(jointName.decode("utf-8"), -4, 4, + (js[0] - jointOffsets[j]) / jointDirections[j])) p.setRealTimeSimulation(1) while (1): - - for i in range(len(paramIds)): - c = paramIds[i] - targetPos = p.readUserDebugParameter(c) - maxForce = p.readUserDebugParameter(maxForceId) - p.setJointMotorControl2(quadruped,jointIds[i],p.POSITION_CONTROL,jointDirections[i]*targetPos+jointOffsets[i], force=maxForce) - + + for i in range(len(paramIds)): + c = paramIds[i] + targetPos = p.readUserDebugParameter(c) + maxForce = p.readUserDebugParameter(maxForceId) + p.setJointMotorControl2(quadruped, + jointIds[i], + p.POSITION_CONTROL, + jointDirections[i] * targetPos + jointOffsets[i], + force=maxForce) diff --git a/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/agent_builder.py b/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/agent_builder.py index e2047da98..cf67210da 100644 --- a/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/agent_builder.py +++ b/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/agent_builder.py @@ -5,17 +5,18 @@ import pybullet_data AGENT_TYPE_KEY = "AgentType" + def build_agent(world, id, file): - agent = None - with open(pybullet_data.getDataPath()+"/"+file) as data_file: - json_data = json.load(data_file) - - assert AGENT_TYPE_KEY in json_data - agent_type = json_data[AGENT_TYPE_KEY] - - if (agent_type == PPOAgent.NAME): - agent = PPOAgent(world, id, json_data) - else: - assert False, 'Unsupported agent type: ' + agent_type + agent = None + with open(pybullet_data.getDataPath() + "/" + file) as data_file: + json_data = json.load(data_file) + + assert AGENT_TYPE_KEY in json_data + agent_type = json_data[AGENT_TYPE_KEY] + + if (agent_type == PPOAgent.NAME): + agent = PPOAgent(world, id, json_data) + else: + assert False, 'Unsupported agent type: ' + agent_type - return agent + return agent diff --git a/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/exp_params.py b/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/exp_params.py index 07c83e0f2..4a01dfec9 100644 --- a/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/exp_params.py +++ b/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/exp_params.py @@ -2,53 +2,54 @@ import json import numpy as np import pybullet_utils.math_util as MathUtil + class ExpParams(object): - RATE_KEY = 'Rate' - INIT_ACTION_RATE_KEY = 'InitActionRate' - NOISE_KEY = 'Noise' - NOISE_INTERNAL_KEY = 'NoiseInternal' - TEMP_KEY = 'Temp' - - def __init__(self): - self.rate = 0.2 - self.init_action_rate = 0 - self.noise = 0.1 - self.noise_internal = 0 - self.temp = 0.1 - return - - def __str__(self): - str = '' - str += '{}: {:.2f}\n'.format(self.RATE_KEY, self.rate) - str += '{}: {:.2f}\n'.format(self.INIT_ACTION_RATE_KEY, self.init_action_rate) - str += '{}: {:.2f}\n'.format(self.NOISE_KEY, self.noise) - str += '{}: {:.2f}\n'.format(self.NOISE_INTERNAL_KEY, self.noise_internal) - str += '{}: {:.2f}\n'.format(self.TEMP_KEY, self.temp) - return str - - def load(self, json_data): - if (self.RATE_KEY in json_data): - self.rate = json_data[self.RATE_KEY] - - if (self.INIT_ACTION_RATE_KEY in json_data): - self.init_action_rate = json_data[self.INIT_ACTION_RATE_KEY] - - if (self.NOISE_KEY in json_data): - self.noise = json_data[self.NOISE_KEY] - - if (self.NOISE_INTERNAL_KEY in json_data): - self.noise_internal = json_data[self.NOISE_INTERNAL_KEY] - - if (self.TEMP_KEY in json_data): - self.temp = json_data[self.TEMP_KEY] - - return - - def lerp(self, other, t): - lerp_params = ExpParams() - lerp_params.rate = MathUtil.lerp(self.rate, other.rate, t) - lerp_params.init_action_rate = MathUtil.lerp(self.init_action_rate, other.init_action_rate, t) - lerp_params.noise = MathUtil.lerp(self.noise, other.noise, t) - lerp_params.noise_internal = MathUtil.lerp(self.noise_internal, other.noise_internal, t) - lerp_params.temp = MathUtil.log_lerp(self.temp, other.temp, t) - return lerp_params
\ No newline at end of file + RATE_KEY = 'Rate' + INIT_ACTION_RATE_KEY = 'InitActionRate' + NOISE_KEY = 'Noise' + NOISE_INTERNAL_KEY = 'NoiseInternal' + TEMP_KEY = 'Temp' + + def __init__(self): + self.rate = 0.2 + self.init_action_rate = 0 + self.noise = 0.1 + self.noise_internal = 0 + self.temp = 0.1 + return + + def __str__(self): + str = '' + str += '{}: {:.2f}\n'.format(self.RATE_KEY, self.rate) + str += '{}: {:.2f}\n'.format(self.INIT_ACTION_RATE_KEY, self.init_action_rate) + str += '{}: {:.2f}\n'.format(self.NOISE_KEY, self.noise) + str += '{}: {:.2f}\n'.format(self.NOISE_INTERNAL_KEY, self.noise_internal) + str += '{}: {:.2f}\n'.format(self.TEMP_KEY, self.temp) + return str + + def load(self, json_data): + if (self.RATE_KEY in json_data): + self.rate = json_data[self.RATE_KEY] + + if (self.INIT_ACTION_RATE_KEY in json_data): + self.init_action_rate = json_data[self.INIT_ACTION_RATE_KEY] + + if (self.NOISE_KEY in json_data): + self.noise = json_data[self.NOISE_KEY] + + if (self.NOISE_INTERNAL_KEY in json_data): + self.noise_internal = json_data[self.NOISE_INTERNAL_KEY] + + if (self.TEMP_KEY in json_data): + self.temp = json_data[self.TEMP_KEY] + + return + + def lerp(self, other, t): + lerp_params = ExpParams() + lerp_params.rate = MathUtil.lerp(self.rate, other.rate, t) + lerp_params.init_action_rate = MathUtil.lerp(self.init_action_rate, other.init_action_rate, t) + lerp_params.noise = MathUtil.lerp(self.noise, other.noise, t) + lerp_params.noise_internal = MathUtil.lerp(self.noise_internal, other.noise_internal, t) + lerp_params.temp = MathUtil.log_lerp(self.temp, other.temp, t) + return lerp_params diff --git a/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/nets/__init__.py b/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/nets/__init__.py index b9742821a..1e136c6c4 100644 --- a/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/nets/__init__.py +++ b/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/nets/__init__.py @@ -1 +1 @@ -from . import *
\ No newline at end of file +from . import *
diff --git a/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/nets/fc_2layers_1024units.py b/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/nets/fc_2layers_1024units.py index b4d466124..a0f627639 100644 --- a/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/nets/fc_2layers_1024units.py +++ b/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/nets/fc_2layers_1024units.py @@ -3,11 +3,12 @@ import pybullet_envs.deep_mimic.learning.tf_util as TFUtil NAME = "fc_2layers_1024units" + def build_net(input_tfs, reuse=False): - layers = [1024, 512] - activation = tf.nn.relu + layers = [1024, 512] + activation = tf.nn.relu - input_tf = tf.concat(axis=-1, values=input_tfs) - h = TFUtil.fc_net(input_tf, layers, activation=activation, reuse=reuse) - h = activation(h) - return h
\ No newline at end of file + input_tf = tf.concat(axis=-1, values=input_tfs) + h = TFUtil.fc_net(input_tf, layers, activation=activation, reuse=reuse) + h = activation(h) + return h diff --git a/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/nets/net_builder.py b/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/nets/net_builder.py index 32328493b..1441bffd9 100644 --- a/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/nets/net_builder.py +++ b/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/nets/net_builder.py @@ -1,11 +1,12 @@ import pybullet_envs.deep_mimic.learning.nets.fc_2layers_1024units as fc_2layers_1024units + def build_net(net_name, input_tfs, reuse=False): - net = None - - if (net_name == fc_2layers_1024units.NAME): - net = fc_2layers_1024units.build_net(input_tfs, reuse) - else: - assert False, 'Unsupported net: ' + net_name - - return net
\ No newline at end of file + net = None + + if (net_name == fc_2layers_1024units.NAME): + net = fc_2layers_1024units.build_net(input_tfs, reuse) + else: + assert False, 'Unsupported net: ' + net_name + + return net diff --git a/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/normalizer.py b/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/normalizer.py index 74bf90569..8feabc7f9 100644 --- a/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/normalizer.py +++ b/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/normalizer.py @@ -3,147 +3,149 @@ import copy import pybullet_utils.mpi_util as MPIUtil from pybullet_utils.logger import Logger + class Normalizer(object): - CHECK_SYNC_COUNT = 50000 # check synchronization after a certain number of entries - - # these group IDs must be the same as those in CharController.h - NORM_GROUP_SINGLE = 0 - NORM_GROUP_NONE = -1 - - class Group(object): - def __init__(self, id, indices): - self.id = id - self.indices = indices - return - - def __init__(self, size, groups_ids=None, eps=0.02, clip=np.inf): - self.eps = eps - self.clip = clip - self.mean = np.zeros(size) - self.mean_sq = np.zeros(size) - self.std = np.ones(size) - self.count = 0 - self.groups = self._build_groups(groups_ids) - - self.new_count = 0 - self.new_sum = np.zeros_like(self.mean) - self.new_sum_sq = np.zeros_like(self.mean_sq) - return - - def record(self, x): - size = self.get_size() - is_array = isinstance(x, np.ndarray) - if not is_array: - assert(size == 1) - x = np.array([[x]]) - - assert x.shape[-1] == size, \ - Logger.print2('Normalizer shape mismatch, expecting size {:d}, but got {:d}'.format(size, x.shape[-1])) - x = np.reshape(x, [-1, size]) - - self.new_count += x.shape[0] - self.new_sum += np.sum(x, axis=0) - self.new_sum_sq += np.sum(np.square(x), axis=0) - return - - def update(self): - new_count = MPIUtil.reduce_sum(self.new_count) - new_sum = MPIUtil.reduce_sum(self.new_sum) - new_sum_sq = MPIUtil.reduce_sum(self.new_sum_sq) - - new_total = self.count + new_count - if (self.count // self.CHECK_SYNC_COUNT != new_total // self.CHECK_SYNC_COUNT): - assert self.check_synced(), Logger.print2('Normalizer parameters desynchronized') - - if new_count > 0: - new_mean = self._process_group_data(new_sum / new_count, self.mean) - new_mean_sq = self._process_group_data(new_sum_sq / new_count, self.mean_sq) - w_old = float(self.count) / new_total - w_new = float(new_count) / new_total - - self.mean = w_old * self.mean + w_new * new_mean - self.mean_sq = w_old * self.mean_sq + w_new * new_mean_sq - self.count = new_total - self.std = self.calc_std(self.mean, self.mean_sq) - - self.new_count = 0 - self.new_sum.fill(0) - self.new_sum_sq.fill(0) - - return - - def get_size(self): - return self.mean.size - - def set_mean_std(self, mean, std): - size = self.get_size() - is_array = isinstance(mean, np.ndarray) and isinstance(std, np.ndarray) - - if not is_array: - assert(size == 1) - mean = np.array([mean]) - std = np.array([std]) - - assert len(mean) == size and len(std) == size, \ - Logger.print2('Normalizer shape mismatch, expecting size {:d}, but got {:d} and {:d}'.format(size, len(mean), len(std))) - - self.mean = mean - self.std = std - self.mean_sq = self.calc_mean_sq(self.mean, self.std) - return - - def normalize(self, x): - norm_x = (x - self.mean) / self.std - norm_x = np.clip(norm_x, -self.clip, self.clip) - return norm_x - - def unnormalize(self, norm_x): - x = norm_x * self.std + self.mean - return x - - def calc_std(self, mean, mean_sq): - var = mean_sq - np.square(mean) - # some time floating point errors can lead to small negative numbers - var = np.maximum(var, 0) - std = np.sqrt(var) - std = np.maximum(std, self.eps) - return std - - def calc_mean_sq(self, mean, std): - return np.square(std) + np.square(self.mean) - - def check_synced(self): - synced = True - if MPIUtil.is_root_proc(): - vars = np.concatenate([self.mean, self.mean_sq]) - MPIUtil.bcast(vars) - else: - vars_local = np.concatenate([self.mean, self.mean_sq]) - vars_root = np.empty_like(vars_local) - MPIUtil.bcast(vars_root) - synced = (vars_local == vars_root).all() - return synced - - def _build_groups(self, groups_ids): - groups = [] - if groups_ids is None: - curr_id = self.NORM_GROUP_SINGLE - curr_list = np.arange(self.get_size()).astype(np.int32) - groups.append(self.Group(curr_id, curr_list)) - else: - ids = np.unique(groups_ids) - for id in ids: - curr_list = np.nonzero(groups_ids == id)[0].astype(np.int32) - groups.append(self.Group(id, curr_list)) - - return groups - - def _process_group_data(self, new_data, old_data): - proc_data = new_data.copy() - for group in self.groups: - if group.id == self.NORM_GROUP_NONE: - proc_data[group.indices] = old_data[group.indices] - elif group.id != self.NORM_GROUP_SINGLE: - avg = np.mean(new_data[group.indices]) - proc_data[group.indices] = avg - return proc_data
\ No newline at end of file + CHECK_SYNC_COUNT = 50000 # check synchronization after a certain number of entries + + # these group IDs must be the same as those in CharController.h + NORM_GROUP_SINGLE = 0 + NORM_GROUP_NONE = -1 + + class Group(object): + + def __init__(self, id, indices): + self.id = id + self.indices = indices + return + + def __init__(self, size, groups_ids=None, eps=0.02, clip=np.inf): + self.eps = eps + self.clip = clip + self.mean = np.zeros(size) + self.mean_sq = np.zeros(size) + self.std = np.ones(size) + self.count = 0 + self.groups = self._build_groups(groups_ids) + + self.new_count = 0 + self.new_sum = np.zeros_like(self.mean) + self.new_sum_sq = np.zeros_like(self.mean_sq) + return + + def record(self, x): + size = self.get_size() + is_array = isinstance(x, np.ndarray) + if not is_array: + assert (size == 1) + x = np.array([[x]]) + + assert x.shape[-1] == size, \ + Logger.print2('Normalizer shape mismatch, expecting size {:d}, but got {:d}'.format(size, x.shape[-1])) + x = np.reshape(x, [-1, size]) + + self.new_count += x.shape[0] + self.new_sum += np.sum(x, axis=0) + self.new_sum_sq += np.sum(np.square(x), axis=0) + return + + def update(self): + new_count = MPIUtil.reduce_sum(self.new_count) + new_sum = MPIUtil.reduce_sum(self.new_sum) + new_sum_sq = MPIUtil.reduce_sum(self.new_sum_sq) + + new_total = self.count + new_count + if (self.count // self.CHECK_SYNC_COUNT != new_total // self.CHECK_SYNC_COUNT): + assert self.check_synced(), Logger.print2('Normalizer parameters desynchronized') + + if new_count > 0: + new_mean = self._process_group_data(new_sum / new_count, self.mean) + new_mean_sq = self._process_group_data(new_sum_sq / new_count, self.mean_sq) + w_old = float(self.count) / new_total + w_new = float(new_count) / new_total + + self.mean = w_old * self.mean + w_new * new_mean + self.mean_sq = w_old * self.mean_sq + w_new * new_mean_sq + self.count = new_total + self.std = self.calc_std(self.mean, self.mean_sq) + + self.new_count = 0 + self.new_sum.fill(0) + self.new_sum_sq.fill(0) + + return + + def get_size(self): + return self.mean.size + + def set_mean_std(self, mean, std): + size = self.get_size() + is_array = isinstance(mean, np.ndarray) and isinstance(std, np.ndarray) + + if not is_array: + assert (size == 1) + mean = np.array([mean]) + std = np.array([std]) + + assert len(mean) == size and len(std) == size, \ + Logger.print2('Normalizer shape mismatch, expecting size {:d}, but got {:d} and {:d}'.format(size, len(mean), len(std))) + + self.mean = mean + self.std = std + self.mean_sq = self.calc_mean_sq(self.mean, self.std) + return + + def normalize(self, x): + norm_x = (x - self.mean) / self.std + norm_x = np.clip(norm_x, -self.clip, self.clip) + return norm_x + + def unnormalize(self, norm_x): + x = norm_x * self.std + self.mean + return x + + def calc_std(self, mean, mean_sq): + var = mean_sq - np.square(mean) + # some time floating point errors can lead to small negative numbers + var = np.maximum(var, 0) + std = np.sqrt(var) + std = np.maximum(std, self.eps) + return std + + def calc_mean_sq(self, mean, std): + return np.square(std) + np.square(self.mean) + + def check_synced(self): + synced = True + if MPIUtil.is_root_proc(): + vars = np.concatenate([self.mean, self.mean_sq]) + MPIUtil.bcast(vars) + else: + vars_local = np.concatenate([self.mean, self.mean_sq]) + vars_root = np.empty_like(vars_local) + MPIUtil.bcast(vars_root) + synced = (vars_local == vars_root).all() + return synced + + def _build_groups(self, groups_ids): + groups = [] + if groups_ids is None: + curr_id = self.NORM_GROUP_SINGLE + curr_list = np.arange(self.get_size()).astype(np.int32) + groups.append(self.Group(curr_id, curr_list)) + else: + ids = np.unique(groups_ids) + for id in ids: + curr_list = np.nonzero(groups_ids == id)[0].astype(np.int32) + groups.append(self.Group(id, curr_list)) + + return groups + + def _process_group_data(self, new_data, old_data): + proc_data = new_data.copy() + for group in self.groups: + if group.id == self.NORM_GROUP_NONE: + proc_data[group.indices] = old_data[group.indices] + elif group.id != self.NORM_GROUP_SINGLE: + avg = np.mean(new_data[group.indices]) + proc_data[group.indices] = avg + return proc_data diff --git a/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/path.py b/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/path.py index c6855491e..37ae1146c 100644 --- a/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/path.py +++ b/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/path.py @@ -1,46 +1,47 @@ import numpy as np from pybullet_envs.deep_mimic.env.env import Env + class Path(object): - def __init__(self): - self.clear() - return - - def pathlength(self): - return len(self.actions) - - def is_valid(self): - valid = True - l = self.pathlength() - valid &= len(self.states) == l + 1 - valid &= len(self.goals) == l + 1 - valid &= len(self.actions) == l - valid &= len(self.logps) == l - valid &= len(self.rewards) == l - valid &= len(self.flags) == l - - return valid - - def check_vals(self): - for vals in [self.states, self.goals, self.actions, self.logps, - self.rewards]: - for v in vals: - if not np.isfinite(v).all(): - return False - return True - - def clear(self): - self.states = [] - self.goals = [] - self.actions = [] - self.logps = [] - self.rewards = [] - self.flags = [] - self.terminate = Env.Terminate.Null - return - - def get_pathlen(self): - return len(self.rewards) - - def calc_return(self): - return sum(self.rewards)
\ No newline at end of file + + def __init__(self): + self.clear() + return + + def pathlength(self): + return len(self.actions) + + def is_valid(self): + valid = True + l = self.pathlength() + valid &= len(self.states) == l + 1 + valid &= len(self.goals) == l + 1 + valid &= len(self.actions) == l + valid &= len(self.logps) == l + valid &= len(self.rewards) == l + valid &= len(self.flags) == l + + return valid + + def check_vals(self): + for vals in [self.states, self.goals, self.actions, self.logps, self.rewards]: + for v in vals: + if not np.isfinite(v).all(): + return False + return True + + def clear(self): + self.states = [] + self.goals = [] + self.actions = [] + self.logps = [] + self.rewards = [] + self.flags = [] + self.terminate = Env.Terminate.Null + return + + def get_pathlen(self): + return len(self.rewards) + + def calc_return(self): + return sum(self.rewards) diff --git a/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/pg_agent.py b/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/pg_agent.py index 75d1bae9a..2787e2356 100644 --- a/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/pg_agent.py +++ b/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/pg_agent.py @@ -13,341 +13,343 @@ import pybullet_utils.mpi_util as MPIUtil import pybullet_utils.math_util as MathUtil from pybullet_envs.deep_mimic.env.action_space import ActionSpace from pybullet_envs.deep_mimic.env.env import Env - ''' Policy Gradient Agent ''' + class PGAgent(TFAgent): - NAME = 'PG' - - ACTOR_NET_KEY = 'ActorNet' - ACTOR_STEPSIZE_KEY = 'ActorStepsize' - ACTOR_MOMENTUM_KEY = 'ActorMomentum' - ACTOR_WEIGHT_DECAY_KEY = 'ActorWeightDecay' - ACTOR_INIT_OUTPUT_SCALE_KEY = 'ActorInitOutputScale' - - CRITIC_NET_KEY = 'CriticNet' - CRITIC_STEPSIZE_KEY = 'CriticStepsize' - CRITIC_MOMENTUM_KEY = 'CriticMomentum' - CRITIC_WEIGHT_DECAY_KEY = 'CriticWeightDecay' - - EXP_ACTION_FLAG = 1 << 0 - - def __init__(self, world, id, json_data): - self._exp_action = False - super().__init__(world, id, json_data) - return - - def reset(self): - super().reset() - self._exp_action = False - return - - def _check_action_space(self): - action_space = self.get_action_space() - return action_space == ActionSpace.Continuous - - def _load_params(self, json_data): - super()._load_params(json_data) - self.val_min, self.val_max = self._calc_val_bounds(self.discount) - self.val_fail, self.val_succ = self._calc_term_vals(self.discount) - return - - def _build_nets(self, json_data): - assert self.ACTOR_NET_KEY in json_data - assert self.CRITIC_NET_KEY in json_data - - actor_net_name = json_data[self.ACTOR_NET_KEY] - critic_net_name = json_data[self.CRITIC_NET_KEY] - actor_init_output_scale = 1 if (self.ACTOR_INIT_OUTPUT_SCALE_KEY not in json_data) else json_data[self.ACTOR_INIT_OUTPUT_SCALE_KEY] - - s_size = self.get_state_size() - g_size = self.get_goal_size() - a_size = self.get_action_size() - - # setup input tensors - self.s_tf = tf.placeholder(tf.float32, shape=[None, s_size], name="s") # observations - self.tar_val_tf = tf.placeholder(tf.float32, shape=[None], name="tar_val") # target value s - self.adv_tf = tf.placeholder(tf.float32, shape=[None], name="adv") # advantage - self.a_tf = tf.placeholder(tf.float32, shape=[None, a_size], name="a") # target actions - self.g_tf = tf.placeholder(tf.float32, shape=([None, g_size] if self.has_goal() else None), name="g") # goals - - with tf.variable_scope('main'): - with tf.variable_scope('actor'): - self.actor_tf = self._build_net_actor(actor_net_name, actor_init_output_scale) - with tf.variable_scope('critic'): - self.critic_tf = self._build_net_critic(critic_net_name) - - if (self.actor_tf != None): - Logger.print2('Built actor net: ' + actor_net_name) - - if (self.critic_tf != None): - Logger.print2('Built critic net: ' + critic_net_name) - - return - - def _build_normalizers(self): - super()._build_normalizers() - with self.sess.as_default(), self.graph.as_default(), tf.variable_scope(self.tf_scope): - with tf.variable_scope(self.RESOURCE_SCOPE): - val_offset, val_scale = self._calc_val_offset_scale(self.discount) - self.val_norm = TFNormalizer(self.sess, 'val_norm', 1) - self.val_norm.set_mean_std(-val_offset, 1.0 / val_scale) - return - - def _init_normalizers(self): - super()._init_normalizers() - with self.sess.as_default(), self.graph.as_default(): - self.val_norm.update() - return - - def _load_normalizers(self): - super()._load_normalizers() - self.val_norm.load() - return - - def _build_losses(self, json_data): - actor_weight_decay = 0 if (self.ACTOR_WEIGHT_DECAY_KEY not in json_data) else json_data[self.ACTOR_WEIGHT_DECAY_KEY] - critic_weight_decay = 0 if (self.CRITIC_WEIGHT_DECAY_KEY not in json_data) else json_data[self.CRITIC_WEIGHT_DECAY_KEY] - - norm_val_diff = self.val_norm.normalize_tf(self.tar_val_tf) - self.val_norm.normalize_tf(self.critic_tf) - self.critic_loss_tf = 0.5 * tf.reduce_mean(tf.square(norm_val_diff)) - - if (critic_weight_decay != 0): - self.critic_loss_tf += critic_weight_decay * self._weight_decay_loss('main/critic') - - norm_a_mean_tf = self.a_norm.normalize_tf(self.actor_tf) - norm_a_diff = self.a_norm.normalize_tf(self.a_tf) - norm_a_mean_tf - - self.actor_loss_tf = tf.reduce_sum(tf.square(norm_a_diff), axis=-1) - self.actor_loss_tf *= self.adv_tf - self.actor_loss_tf = 0.5 * tf.reduce_mean(self.actor_loss_tf) - - norm_a_bound_min = self.a_norm.normalize(self.a_bound_min) - norm_a_bound_max = self.a_norm.normalize(self.a_bound_max) - a_bound_loss = TFUtil.calc_bound_loss(norm_a_mean_tf, norm_a_bound_min, norm_a_bound_max) - a_bound_loss /= self.exp_params_curr.noise - self.actor_loss_tf += a_bound_loss - - if (actor_weight_decay != 0): - self.actor_loss_tf += actor_weight_decay * self._weight_decay_loss('main/actor') - - return - - def _build_solvers(self, json_data): - actor_stepsize = 0.001 if (self.ACTOR_STEPSIZE_KEY not in json_data) else json_data[self.ACTOR_STEPSIZE_KEY] - actor_momentum = 0.9 if (self.ACTOR_MOMENTUM_KEY not in json_data) else json_data[self.ACTOR_MOMENTUM_KEY] - critic_stepsize = 0.01 if (self.CRITIC_STEPSIZE_KEY not in json_data) else json_data[self.CRITIC_STEPSIZE_KEY] - critic_momentum = 0.9 if (self.CRITIC_MOMENTUM_KEY not in json_data) else json_data[self.CRITIC_MOMENTUM_KEY] - - critic_vars = self._tf_vars('main/critic') - critic_opt = tf.train.MomentumOptimizer(learning_rate=critic_stepsize, momentum=critic_momentum) - self.critic_grad_tf = tf.gradients(self.critic_loss_tf, critic_vars) - self.critic_solver = MPISolver(self.sess, critic_opt, critic_vars) - - actor_vars = self._tf_vars('main/actor') - actor_opt = tf.train.MomentumOptimizer(learning_rate=actor_stepsize, momentum=actor_momentum) - self.actor_grad_tf = tf.gradients(self.actor_loss_tf, actor_vars) - self.actor_solver = MPISolver(self.sess, actor_opt, actor_vars) - - return - - def _build_net_actor(self, net_name, init_output_scale): - norm_s_tf = self.s_norm.normalize_tf(self.s_tf) - input_tfs = [norm_s_tf] - if (self.has_goal()): - norm_g_tf = self.g_norm.normalize_tf(self.g_tf) - input_tfs += [norm_g_tf] - - h = NetBuilder.build_net(net_name, input_tfs) - norm_a_tf = tf.layers.dense(inputs=h, units=self.get_action_size(), activation=None, - kernel_initializer=tf.random_uniform_initializer(minval=-init_output_scale, maxval=init_output_scale)) - - a_tf = self.a_norm.unnormalize_tf(norm_a_tf) - return a_tf - - def _build_net_critic(self, net_name): - norm_s_tf = self.s_norm.normalize_tf(self.s_tf) - input_tfs = [norm_s_tf] - if (self.has_goal()): - norm_g_tf = self.g_norm.normalize_tf(self.g_tf) - input_tfs += [norm_g_tf] - - h = NetBuilder.build_net(net_name, input_tfs) - norm_val_tf = tf.layers.dense(inputs=h, units=1, activation=None, - kernel_initializer=TFUtil.xavier_initializer); - - norm_val_tf = tf.reshape(norm_val_tf, [-1]) - val_tf = self.val_norm.unnormalize_tf(norm_val_tf) - return val_tf - - def _initialize_vars(self): - super()._initialize_vars() - self._sync_solvers() - return - - def _sync_solvers(self): - self.actor_solver.sync() - self.critic_solver.sync() - return - - def _decide_action(self, s, g): - with self.sess.as_default(), self.graph.as_default(): - self._exp_action = False - a = self._eval_actor(s, g)[0] - logp = 0 - - if self._enable_stoch_policy(): - # epsilon-greedy - rand_action = MathUtil.flip_coin(self.exp_params_curr.rate) - if rand_action: - norm_exp_noise = np.random.randn(*a.shape) - norm_exp_noise *= self.exp_params_curr.noise - exp_noise = norm_exp_noise * self.a_norm.std - a += exp_noise - - logp = self._calc_action_logp(norm_exp_noise) - self._exp_action = True - - return a, logp - - def _enable_stoch_policy(self): - return self.enable_training and (self._mode == self.Mode.TRAIN or self._mode == self.Mode.TRAIN_END) - - def _eval_actor(self, s, g): - s = np.reshape(s, [-1, self.get_state_size()]) - g = np.reshape(g, [-1, self.get_goal_size()]) if self.has_goal() else None - - feed = { - self.s_tf : s, - self.g_tf : g - } - - a = self.actor_tf.eval(feed) - return a - - def _eval_critic(self, s, g): - with self.sess.as_default(), self.graph.as_default(): - s = np.reshape(s, [-1, self.get_state_size()]) - g = np.reshape(g, [-1, self.get_goal_size()]) if self.has_goal() else None - - feed = { - self.s_tf : s, - self.g_tf : g - } - - val = self.critic_tf.eval(feed) - return val - - def _record_flags(self): - flags = int(0) - if (self._exp_action): - flags = flags | self.EXP_ACTION_FLAG - return flags - - def _train_step(self): - super()._train_step() - - critic_loss = self._update_critic() - actor_loss = self._update_actor() - critic_loss = MPIUtil.reduce_avg(critic_loss) - actor_loss = MPIUtil.reduce_avg(actor_loss) - - critic_stepsize = self.critic_solver.get_stepsize() - actor_stepsize = self.actor_solver.get_stepsize() - - self.logger.log_tabular('Critic_Loss', critic_loss) - self.logger.log_tabular('Critic_Stepsize', critic_stepsize) - self.logger.log_tabular('Actor_Loss', actor_loss) - self.logger.log_tabular('Actor_Stepsize', actor_stepsize) - - return - - def _update_critic(self): - idx = self.replay_buffer.sample(self._local_mini_batch_size) - s = self.replay_buffer.get('states', idx) - g = self.replay_buffer.get('goals', idx) if self.has_goal() else None - - tar_V = self._calc_updated_vals(idx) - tar_V = np.clip(tar_V, self.val_min, self.val_max) - - feed = { - self.s_tf: s, - self.g_tf: g, - self.tar_val_tf: tar_V - } - - loss, grads = self.sess.run([self.critic_loss_tf, self.critic_grad_tf], feed) - self.critic_solver.update(grads) - return loss - - def _update_actor(self): - key = self.EXP_ACTION_FLAG - idx = self.replay_buffer.sample_filtered(self._local_mini_batch_size, key) - has_goal = self.has_goal() - - s = self.replay_buffer.get('states', idx) - g = self.replay_buffer.get('goals', idx) if has_goal else None - a = self.replay_buffer.get('actions', idx) - - V_new = self._calc_updated_vals(idx) - V_old = self._eval_critic(s, g) - adv = V_new - V_old - - feed = { - self.s_tf: s, - self.g_tf: g, - self.a_tf: a, - self.adv_tf: adv - } - - loss, grads = self.sess.run([self.actor_loss_tf, self.actor_grad_tf], feed) - self.actor_solver.update(grads) - - return loss - - def _calc_updated_vals(self, idx): - r = self.replay_buffer.get('rewards', idx) - - if self.discount == 0: - new_V = r - else: - next_idx = self.replay_buffer.get_next_idx(idx) - s_next = self.replay_buffer.get('states', next_idx) - g_next = self.replay_buffer.get('goals', next_idx) if self.has_goal() else None - - is_end = self.replay_buffer.is_path_end(idx) - is_fail = self.replay_buffer.check_terminal_flag(idx, Env.Terminate.Fail) - is_succ = self.replay_buffer.check_terminal_flag(idx, Env.Terminate.Succ) - is_fail = np.logical_and(is_end, is_fail) - is_succ = np.logical_and(is_end, is_succ) - - V_next = self._eval_critic(s_next, g_next) - V_next[is_fail] = self.val_fail - V_next[is_succ] = self.val_succ - - new_V = r + self.discount * V_next - return new_V - - def _calc_action_logp(self, norm_action_deltas): - # norm action delta are for the normalized actions (scaled by self.a_norm.std) - stdev = self.exp_params_curr.noise - assert stdev > 0 - - a_size = self.get_action_size() - logp = -0.5 / (stdev * stdev) * np.sum(np.square(norm_action_deltas), axis=-1) - logp += -0.5 * a_size * np.log(2 * np.pi) - logp += -a_size * np.log(stdev) - return logp - - def _log_val(self, s, g): - val = self._eval_critic(s, g) - norm_val = self.val_norm.normalize(val) - self.world.env.log_val(self.id, norm_val[0]) - return - - def _build_replay_buffer(self, buffer_size): - super()._build_replay_buffer(buffer_size) - self.replay_buffer.add_filter_key(self.EXP_ACTION_FLAG) - return + NAME = 'PG' + + ACTOR_NET_KEY = 'ActorNet' + ACTOR_STEPSIZE_KEY = 'ActorStepsize' + ACTOR_MOMENTUM_KEY = 'ActorMomentum' + ACTOR_WEIGHT_DECAY_KEY = 'ActorWeightDecay' + ACTOR_INIT_OUTPUT_SCALE_KEY = 'ActorInitOutputScale' + + CRITIC_NET_KEY = 'CriticNet' + CRITIC_STEPSIZE_KEY = 'CriticStepsize' + CRITIC_MOMENTUM_KEY = 'CriticMomentum' + CRITIC_WEIGHT_DECAY_KEY = 'CriticWeightDecay' + + EXP_ACTION_FLAG = 1 << 0 + + def __init__(self, world, id, json_data): + self._exp_action = False + super().__init__(world, id, json_data) + return + + def reset(self): + super().reset() + self._exp_action = False + return + + def _check_action_space(self): + action_space = self.get_action_space() + return action_space == ActionSpace.Continuous + + def _load_params(self, json_data): + super()._load_params(json_data) + self.val_min, self.val_max = self._calc_val_bounds(self.discount) + self.val_fail, self.val_succ = self._calc_term_vals(self.discount) + return + + def _build_nets(self, json_data): + assert self.ACTOR_NET_KEY in json_data + assert self.CRITIC_NET_KEY in json_data + + actor_net_name = json_data[self.ACTOR_NET_KEY] + critic_net_name = json_data[self.CRITIC_NET_KEY] + actor_init_output_scale = 1 if (self.ACTOR_INIT_OUTPUT_SCALE_KEY not in json_data + ) else json_data[self.ACTOR_INIT_OUTPUT_SCALE_KEY] + + s_size = self.get_state_size() + g_size = self.get_goal_size() + a_size = self.get_action_size() + + # setup input tensors + self.s_tf = tf.placeholder(tf.float32, shape=[None, s_size], name="s") # observations + self.tar_val_tf = tf.placeholder(tf.float32, shape=[None], name="tar_val") # target value s + self.adv_tf = tf.placeholder(tf.float32, shape=[None], name="adv") # advantage + self.a_tf = tf.placeholder(tf.float32, shape=[None, a_size], name="a") # target actions + self.g_tf = tf.placeholder(tf.float32, + shape=([None, g_size] if self.has_goal() else None), + name="g") # goals + + with tf.variable_scope('main'): + with tf.variable_scope('actor'): + self.actor_tf = self._build_net_actor(actor_net_name, actor_init_output_scale) + with tf.variable_scope('critic'): + self.critic_tf = self._build_net_critic(critic_net_name) + + if (self.actor_tf != None): + Logger.print2('Built actor net: ' + actor_net_name) + + if (self.critic_tf != None): + Logger.print2('Built critic net: ' + critic_net_name) + + return + + def _build_normalizers(self): + super()._build_normalizers() + with self.sess.as_default(), self.graph.as_default(), tf.variable_scope(self.tf_scope): + with tf.variable_scope(self.RESOURCE_SCOPE): + val_offset, val_scale = self._calc_val_offset_scale(self.discount) + self.val_norm = TFNormalizer(self.sess, 'val_norm', 1) + self.val_norm.set_mean_std(-val_offset, 1.0 / val_scale) + return + + def _init_normalizers(self): + super()._init_normalizers() + with self.sess.as_default(), self.graph.as_default(): + self.val_norm.update() + return + + def _load_normalizers(self): + super()._load_normalizers() + self.val_norm.load() + return + + def _build_losses(self, json_data): + actor_weight_decay = 0 if ( + self.ACTOR_WEIGHT_DECAY_KEY not in json_data) else json_data[self.ACTOR_WEIGHT_DECAY_KEY] + critic_weight_decay = 0 if ( + self.CRITIC_WEIGHT_DECAY_KEY not in json_data) else json_data[self.CRITIC_WEIGHT_DECAY_KEY] + + norm_val_diff = self.val_norm.normalize_tf(self.tar_val_tf) - self.val_norm.normalize_tf( + self.critic_tf) + self.critic_loss_tf = 0.5 * tf.reduce_mean(tf.square(norm_val_diff)) + + if (critic_weight_decay != 0): + self.critic_loss_tf += critic_weight_decay * self._weight_decay_loss('main/critic') + + norm_a_mean_tf = self.a_norm.normalize_tf(self.actor_tf) + norm_a_diff = self.a_norm.normalize_tf(self.a_tf) - norm_a_mean_tf + + self.actor_loss_tf = tf.reduce_sum(tf.square(norm_a_diff), axis=-1) + self.actor_loss_tf *= self.adv_tf + self.actor_loss_tf = 0.5 * tf.reduce_mean(self.actor_loss_tf) + + norm_a_bound_min = self.a_norm.normalize(self.a_bound_min) + norm_a_bound_max = self.a_norm.normalize(self.a_bound_max) + a_bound_loss = TFUtil.calc_bound_loss(norm_a_mean_tf, norm_a_bound_min, norm_a_bound_max) + a_bound_loss /= self.exp_params_curr.noise + self.actor_loss_tf += a_bound_loss + + if (actor_weight_decay != 0): + self.actor_loss_tf += actor_weight_decay * self._weight_decay_loss('main/actor') + + return + + def _build_solvers(self, json_data): + actor_stepsize = 0.001 if ( + self.ACTOR_STEPSIZE_KEY not in json_data) else json_data[self.ACTOR_STEPSIZE_KEY] + actor_momentum = 0.9 if ( + self.ACTOR_MOMENTUM_KEY not in json_data) else json_data[self.ACTOR_MOMENTUM_KEY] + critic_stepsize = 0.01 if ( + self.CRITIC_STEPSIZE_KEY not in json_data) else json_data[self.CRITIC_STEPSIZE_KEY] + critic_momentum = 0.9 if ( + self.CRITIC_MOMENTUM_KEY not in json_data) else json_data[self.CRITIC_MOMENTUM_KEY] + + critic_vars = self._tf_vars('main/critic') + critic_opt = tf.train.MomentumOptimizer(learning_rate=critic_stepsize, + momentum=critic_momentum) + self.critic_grad_tf = tf.gradients(self.critic_loss_tf, critic_vars) + self.critic_solver = MPISolver(self.sess, critic_opt, critic_vars) + + actor_vars = self._tf_vars('main/actor') + actor_opt = tf.train.MomentumOptimizer(learning_rate=actor_stepsize, momentum=actor_momentum) + self.actor_grad_tf = tf.gradients(self.actor_loss_tf, actor_vars) + self.actor_solver = MPISolver(self.sess, actor_opt, actor_vars) + + return + + def _build_net_actor(self, net_name, init_output_scale): + norm_s_tf = self.s_norm.normalize_tf(self.s_tf) + input_tfs = [norm_s_tf] + if (self.has_goal()): + norm_g_tf = self.g_norm.normalize_tf(self.g_tf) + input_tfs += [norm_g_tf] + + h = NetBuilder.build_net(net_name, input_tfs) + norm_a_tf = tf.layers.dense(inputs=h, + units=self.get_action_size(), + activation=None, + kernel_initializer=tf.random_uniform_initializer( + minval=-init_output_scale, maxval=init_output_scale)) + + a_tf = self.a_norm.unnormalize_tf(norm_a_tf) + return a_tf + + def _build_net_critic(self, net_name): + norm_s_tf = self.s_norm.normalize_tf(self.s_tf) + input_tfs = [norm_s_tf] + if (self.has_goal()): + norm_g_tf = self.g_norm.normalize_tf(self.g_tf) + input_tfs += [norm_g_tf] + + h = NetBuilder.build_net(net_name, input_tfs) + norm_val_tf = tf.layers.dense(inputs=h, + units=1, + activation=None, + kernel_initializer=TFUtil.xavier_initializer) + + norm_val_tf = tf.reshape(norm_val_tf, [-1]) + val_tf = self.val_norm.unnormalize_tf(norm_val_tf) + return val_tf + + def _initialize_vars(self): + super()._initialize_vars() + self._sync_solvers() + return + + def _sync_solvers(self): + self.actor_solver.sync() + self.critic_solver.sync() + return + + def _decide_action(self, s, g): + with self.sess.as_default(), self.graph.as_default(): + self._exp_action = False + a = self._eval_actor(s, g)[0] + logp = 0 + + if self._enable_stoch_policy(): + # epsilon-greedy + rand_action = MathUtil.flip_coin(self.exp_params_curr.rate) + if rand_action: + norm_exp_noise = np.random.randn(*a.shape) + norm_exp_noise *= self.exp_params_curr.noise + exp_noise = norm_exp_noise * self.a_norm.std + a += exp_noise + + logp = self._calc_action_logp(norm_exp_noise) + self._exp_action = True + + return a, logp + + def _enable_stoch_policy(self): + return self.enable_training and (self._mode == self.Mode.TRAIN or + self._mode == self.Mode.TRAIN_END) + + def _eval_actor(self, s, g): + s = np.reshape(s, [-1, self.get_state_size()]) + g = np.reshape(g, [-1, self.get_goal_size()]) if self.has_goal() else None + + feed = {self.s_tf: s, self.g_tf: g} + + a = self.actor_tf.eval(feed) + return a + + def _eval_critic(self, s, g): + with self.sess.as_default(), self.graph.as_default(): + s = np.reshape(s, [-1, self.get_state_size()]) + g = np.reshape(g, [-1, self.get_goal_size()]) if self.has_goal() else None + + feed = {self.s_tf: s, self.g_tf: g} + + val = self.critic_tf.eval(feed) + return val + + def _record_flags(self): + flags = int(0) + if (self._exp_action): + flags = flags | self.EXP_ACTION_FLAG + return flags + + def _train_step(self): + super()._train_step() + + critic_loss = self._update_critic() + actor_loss = self._update_actor() + critic_loss = MPIUtil.reduce_avg(critic_loss) + actor_loss = MPIUtil.reduce_avg(actor_loss) + + critic_stepsize = self.critic_solver.get_stepsize() + actor_stepsize = self.actor_solver.get_stepsize() + + self.logger.log_tabular('Critic_Loss', critic_loss) + self.logger.log_tabular('Critic_Stepsize', critic_stepsize) + self.logger.log_tabular('Actor_Loss', actor_loss) + self.logger.log_tabular('Actor_Stepsize', actor_stepsize) + + return + + def _update_critic(self): + idx = self.replay_buffer.sample(self._local_mini_batch_size) + s = self.replay_buffer.get('states', idx) + g = self.replay_buffer.get('goals', idx) if self.has_goal() else None + + tar_V = self._calc_updated_vals(idx) + tar_V = np.clip(tar_V, self.val_min, self.val_max) + + feed = {self.s_tf: s, self.g_tf: g, self.tar_val_tf: tar_V} + + loss, grads = self.sess.run([self.critic_loss_tf, self.critic_grad_tf], feed) + self.critic_solver.update(grads) + return loss + + def _update_actor(self): + key = self.EXP_ACTION_FLAG + idx = self.replay_buffer.sample_filtered(self._local_mini_batch_size, key) + has_goal = self.has_goal() + + s = self.replay_buffer.get('states', idx) + g = self.replay_buffer.get('goals', idx) if has_goal else None + a = self.replay_buffer.get('actions', idx) + + V_new = self._calc_updated_vals(idx) + V_old = self._eval_critic(s, g) + adv = V_new - V_old + + feed = {self.s_tf: s, self.g_tf: g, self.a_tf: a, self.adv_tf: adv} + + loss, grads = self.sess.run([self.actor_loss_tf, self.actor_grad_tf], feed) + self.actor_solver.update(grads) + + return loss + + def _calc_updated_vals(self, idx): + r = self.replay_buffer.get('rewards', idx) + + if self.discount == 0: + new_V = r + else: + next_idx = self.replay_buffer.get_next_idx(idx) + s_next = self.replay_buffer.get('states', next_idx) + g_next = self.replay_buffer.get('goals', next_idx) if self.has_goal() else None + + is_end = self.replay_buffer.is_path_end(idx) + is_fail = self.replay_buffer.check_terminal_flag(idx, Env.Terminate.Fail) + is_succ = self.replay_buffer.check_terminal_flag(idx, Env.Terminate.Succ) + is_fail = np.logical_and(is_end, is_fail) + is_succ = np.logical_and(is_end, is_succ) + + V_next = self._eval_critic(s_next, g_next) + V_next[is_fail] = self.val_fail + V_next[is_succ] = self.val_succ + + new_V = r + self.discount * V_next + return new_V + + def _calc_action_logp(self, norm_action_deltas): + # norm action delta are for the normalized actions (scaled by self.a_norm.std) + stdev = self.exp_params_curr.noise + assert stdev > 0 + + a_size = self.get_action_size() + logp = -0.5 / (stdev * stdev) * np.sum(np.square(norm_action_deltas), axis=-1) + logp += -0.5 * a_size * np.log(2 * np.pi) + logp += -a_size * np.log(stdev) + return logp + + def _log_val(self, s, g): + val = self._eval_critic(s, g) + norm_val = self.val_norm.normalize(val) + self.world.env.log_val(self.id, norm_val[0]) + return + + def _build_replay_buffer(self, buffer_size): + super()._build_replay_buffer(buffer_size) + self.replay_buffer.add_filter_key(self.EXP_ACTION_FLAG) + return diff --git a/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/ppo_agent.py b/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/ppo_agent.py index ff66ba004..125a1bb38 100644 --- a/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/ppo_agent.py +++ b/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/ppo_agent.py @@ -10,359 +10,374 @@ from pybullet_utils.logger import Logger import pybullet_utils.mpi_util as MPIUtil import pybullet_utils.math_util as MathUtil from pybullet_envs.deep_mimic.env.env import Env - ''' Proximal Policy Optimization Agent ''' + class PPOAgent(PGAgent): - NAME = "PPO" - EPOCHS_KEY = "Epochs" - BATCH_SIZE_KEY = "BatchSize" - RATIO_CLIP_KEY = "RatioClip" - NORM_ADV_CLIP_KEY = "NormAdvClip" - TD_LAMBDA_KEY = "TDLambda" - TAR_CLIP_FRAC = "TarClipFrac" - ACTOR_STEPSIZE_DECAY = "ActorStepsizeDecay" - - def __init__(self, world, id, json_data): - super().__init__(world, id, json_data) - return - - def _load_params(self, json_data): - super()._load_params(json_data) - - self.epochs = 1 if (self.EPOCHS_KEY not in json_data) else json_data[self.EPOCHS_KEY] - self.batch_size = 1024 if (self.BATCH_SIZE_KEY not in json_data) else json_data[self.BATCH_SIZE_KEY] - self.ratio_clip = 0.2 if (self.RATIO_CLIP_KEY not in json_data) else json_data[self.RATIO_CLIP_KEY] - self.norm_adv_clip = 5 if (self.NORM_ADV_CLIP_KEY not in json_data) else json_data[self.NORM_ADV_CLIP_KEY] - self.td_lambda = 0.95 if (self.TD_LAMBDA_KEY not in json_data) else json_data[self.TD_LAMBDA_KEY] - self.tar_clip_frac = -1 if (self.TAR_CLIP_FRAC not in json_data) else json_data[self.TAR_CLIP_FRAC] - self.actor_stepsize_decay = 0.5 if (self.ACTOR_STEPSIZE_DECAY not in json_data) else json_data[self.ACTOR_STEPSIZE_DECAY] - - num_procs = MPIUtil.get_num_procs() - local_batch_size = int(self.batch_size / num_procs) - min_replay_size = 2 * local_batch_size # needed to prevent buffer overflow - assert(self.replay_buffer_size > min_replay_size) - - self.replay_buffer_size = np.maximum(min_replay_size, self.replay_buffer_size) - - return - - def _build_nets(self, json_data): - assert self.ACTOR_NET_KEY in json_data - assert self.CRITIC_NET_KEY in json_data - - actor_net_name = json_data[self.ACTOR_NET_KEY] - critic_net_name = json_data[self.CRITIC_NET_KEY] - actor_init_output_scale = 1 if (self.ACTOR_INIT_OUTPUT_SCALE_KEY not in json_data) else json_data[self.ACTOR_INIT_OUTPUT_SCALE_KEY] - - s_size = self.get_state_size() - g_size = self.get_goal_size() - a_size = self.get_action_size() - - # setup input tensors - self.s_tf = tf.placeholder(tf.float32, shape=[None, s_size], name="s") - self.a_tf = tf.placeholder(tf.float32, shape=[None, a_size], name="a") - self.tar_val_tf = tf.placeholder(tf.float32, shape=[None], name="tar_val") - self.adv_tf = tf.placeholder(tf.float32, shape=[None], name="adv") - self.g_tf = tf.placeholder(tf.float32, shape=([None, g_size] if self.has_goal() else None), name="g") - self.old_logp_tf = tf.placeholder(tf.float32, shape=[None], name="old_logp") - self.exp_mask_tf = tf.placeholder(tf.float32, shape=[None], name="exp_mask") - - with tf.variable_scope('main'): - with tf.variable_scope('actor'): - self.a_mean_tf = self._build_net_actor(actor_net_name, actor_init_output_scale) - with tf.variable_scope('critic'): - self.critic_tf = self._build_net_critic(critic_net_name) - - if (self.a_mean_tf != None): - Logger.print2('Built actor net: ' + actor_net_name) - - if (self.critic_tf != None): - Logger.print2('Built critic net: ' + critic_net_name) - - self.norm_a_std_tf = self.exp_params_curr.noise * tf.ones(a_size) - norm_a_noise_tf = self.norm_a_std_tf * tf.random_normal(shape=tf.shape(self.a_mean_tf)) - norm_a_noise_tf *= tf.expand_dims(self.exp_mask_tf, axis=-1) - self.sample_a_tf = self.a_mean_tf + norm_a_noise_tf * self.a_norm.std_tf - self.sample_a_logp_tf = TFUtil.calc_logp_gaussian(x_tf=norm_a_noise_tf, mean_tf=None, std_tf=self.norm_a_std_tf) - - return - - def _build_losses(self, json_data): - actor_weight_decay = 0 if (self.ACTOR_WEIGHT_DECAY_KEY not in json_data) else json_data[self.ACTOR_WEIGHT_DECAY_KEY] - critic_weight_decay = 0 if (self.CRITIC_WEIGHT_DECAY_KEY not in json_data) else json_data[self.CRITIC_WEIGHT_DECAY_KEY] - - norm_val_diff = self.val_norm.normalize_tf(self.tar_val_tf) - self.val_norm.normalize_tf(self.critic_tf) - self.critic_loss_tf = 0.5 * tf.reduce_mean(tf.square(norm_val_diff)) - - if (critic_weight_decay != 0): - self.critic_loss_tf += critic_weight_decay * self._weight_decay_loss('main/critic') - - norm_tar_a_tf = self.a_norm.normalize_tf(self.a_tf) - self._norm_a_mean_tf = self.a_norm.normalize_tf(self.a_mean_tf) - - self.logp_tf = TFUtil.calc_logp_gaussian(norm_tar_a_tf, self._norm_a_mean_tf, self.norm_a_std_tf) - ratio_tf = tf.exp(self.logp_tf - self.old_logp_tf) - actor_loss0 = self.adv_tf * ratio_tf - actor_loss1 = self.adv_tf * tf.clip_by_value(ratio_tf, 1.0 - self.ratio_clip, 1 + self.ratio_clip) - self.actor_loss_tf = -tf.reduce_mean(tf.minimum(actor_loss0, actor_loss1)) - - norm_a_bound_min = self.a_norm.normalize(self.a_bound_min) - norm_a_bound_max = self.a_norm.normalize(self.a_bound_max) - a_bound_loss = TFUtil.calc_bound_loss(self._norm_a_mean_tf, norm_a_bound_min, norm_a_bound_max) - self.actor_loss_tf += a_bound_loss - - if (actor_weight_decay != 0): - self.actor_loss_tf += actor_weight_decay * self._weight_decay_loss('main/actor') - - # for debugging - self.clip_frac_tf = tf.reduce_mean(tf.to_float(tf.greater(tf.abs(ratio_tf - 1.0), self.ratio_clip))) - - return - - def _build_solvers(self, json_data): - actor_stepsize = 0.001 if (self.ACTOR_STEPSIZE_KEY not in json_data) else json_data[self.ACTOR_STEPSIZE_KEY] - actor_momentum = 0.9 if (self.ACTOR_MOMENTUM_KEY not in json_data) else json_data[self.ACTOR_MOMENTUM_KEY] - critic_stepsize = 0.01 if (self.CRITIC_STEPSIZE_KEY not in json_data) else json_data[self.CRITIC_STEPSIZE_KEY] - critic_momentum = 0.9 if (self.CRITIC_MOMENTUM_KEY not in json_data) else json_data[self.CRITIC_MOMENTUM_KEY] - - critic_vars = self._tf_vars('main/critic') - critic_opt = tf.train.MomentumOptimizer(learning_rate=critic_stepsize, momentum=critic_momentum) - self.critic_grad_tf = tf.gradients(self.critic_loss_tf, critic_vars) - self.critic_solver = MPISolver(self.sess, critic_opt, critic_vars) - - self._actor_stepsize_tf = tf.get_variable(dtype=tf.float32, name='actor_stepsize', initializer=actor_stepsize, trainable=False) - self._actor_stepsize_ph = tf.get_variable(dtype=tf.float32, name='actor_stepsize_ph', shape=[]) - self._actor_stepsize_update_op = self._actor_stepsize_tf.assign(self._actor_stepsize_ph) - - actor_vars = self._tf_vars('main/actor') - actor_opt = tf.train.MomentumOptimizer(learning_rate=self._actor_stepsize_tf, momentum=actor_momentum) - self.actor_grad_tf = tf.gradients(self.actor_loss_tf, actor_vars) - self.actor_solver = MPISolver(self.sess, actor_opt, actor_vars) - - return - - def _decide_action(self, s, g): - with self.sess.as_default(), self.graph.as_default(): - self._exp_action = self._enable_stoch_policy() and MathUtil.flip_coin(self.exp_params_curr.rate) - #print("_decide_action._exp_action=",self._exp_action) - a, logp = self._eval_actor(s, g, self._exp_action) - return a[0], logp[0] - - def _eval_actor(self, s, g, enable_exp): - s = np.reshape(s, [-1, self.get_state_size()]) - g = np.reshape(g, [-1, self.get_goal_size()]) if self.has_goal() else None - - feed = { - self.s_tf : s, - self.g_tf : g, - self.exp_mask_tf: np.array([1 if enable_exp else 0]) - } - - a, logp = self.sess.run([self.sample_a_tf, self.sample_a_logp_tf], feed_dict=feed) - return a, logp - - def _train_step(self): - adv_eps = 1e-5 - - start_idx = self.replay_buffer.buffer_tail - end_idx = self.replay_buffer.buffer_head - assert(start_idx == 0) - assert(self.replay_buffer.get_current_size() <= self.replay_buffer.buffer_size) # must avoid overflow - assert(start_idx < end_idx) - - idx = np.array(list(range(start_idx, end_idx))) - end_mask = self.replay_buffer.is_path_end(idx) - end_mask = np.logical_not(end_mask) - - vals = self._compute_batch_vals(start_idx, end_idx) - new_vals = self._compute_batch_new_vals(start_idx, end_idx, vals) - - valid_idx = idx[end_mask] - exp_idx = self.replay_buffer.get_idx_filtered(self.EXP_ACTION_FLAG).copy() - num_valid_idx = valid_idx.shape[0] - num_exp_idx = exp_idx.shape[0] - exp_idx = np.column_stack([exp_idx, np.array(list(range(0, num_exp_idx)), dtype=np.int32)]) - - local_sample_count = valid_idx.size - global_sample_count = int(MPIUtil.reduce_sum(local_sample_count)) - mini_batches = int(np.ceil(global_sample_count / self.mini_batch_size)) - - adv = new_vals[exp_idx[:,0]] - vals[exp_idx[:,0]] - new_vals = np.clip(new_vals, self.val_min, self.val_max) - - adv_mean = np.mean(adv) - adv_std = np.std(adv) - adv = (adv - adv_mean) / (adv_std + adv_eps) - adv = np.clip(adv, -self.norm_adv_clip, self.norm_adv_clip) - - critic_loss = 0 - actor_loss = 0 - actor_clip_frac = 0 - - for e in range(self.epochs): - np.random.shuffle(valid_idx) - np.random.shuffle(exp_idx) - - for b in range(mini_batches): - batch_idx_beg = b * self._local_mini_batch_size - batch_idx_end = batch_idx_beg + self._local_mini_batch_size - - critic_batch = np.array(range(batch_idx_beg, batch_idx_end), dtype=np.int32) - actor_batch = critic_batch.copy() - critic_batch = np.mod(critic_batch, num_valid_idx) - actor_batch = np.mod(actor_batch, num_exp_idx) - shuffle_actor = (actor_batch[-1] < actor_batch[0]) or (actor_batch[-1] == num_exp_idx - 1) - - critic_batch = valid_idx[critic_batch] - actor_batch = exp_idx[actor_batch] - critic_batch_vals = new_vals[critic_batch] - actor_batch_adv = adv[actor_batch[:,1]] - - critic_s = self.replay_buffer.get('states', critic_batch) - critic_g = self.replay_buffer.get('goals', critic_batch) if self.has_goal() else None - curr_critic_loss = self._update_critic(critic_s, critic_g, critic_batch_vals) - - actor_s = self.replay_buffer.get("states", actor_batch[:,0]) - actor_g = self.replay_buffer.get("goals", actor_batch[:,0]) if self.has_goal() else None - actor_a = self.replay_buffer.get("actions", actor_batch[:,0]) - actor_logp = self.replay_buffer.get("logps", actor_batch[:,0]) - curr_actor_loss, curr_actor_clip_frac = self._update_actor(actor_s, actor_g, actor_a, actor_logp, actor_batch_adv) - - critic_loss += curr_critic_loss - actor_loss += np.abs(curr_actor_loss) - actor_clip_frac += curr_actor_clip_frac - - if (shuffle_actor): - np.random.shuffle(exp_idx) - - total_batches = mini_batches * self.epochs - critic_loss /= total_batches - actor_loss /= total_batches - actor_clip_frac /= total_batches - - critic_loss = MPIUtil.reduce_avg(critic_loss) - actor_loss = MPIUtil.reduce_avg(actor_loss) - actor_clip_frac = MPIUtil.reduce_avg(actor_clip_frac) - - critic_stepsize = self.critic_solver.get_stepsize() - actor_stepsize = self.update_actor_stepsize(actor_clip_frac) - - self.logger.log_tabular('Critic_Loss', critic_loss) - self.logger.log_tabular('Critic_Stepsize', critic_stepsize) - self.logger.log_tabular('Actor_Loss', actor_loss) - self.logger.log_tabular('Actor_Stepsize', actor_stepsize) - self.logger.log_tabular('Clip_Frac', actor_clip_frac) - self.logger.log_tabular('Adv_Mean', adv_mean) - self.logger.log_tabular('Adv_Std', adv_std) - - self.replay_buffer.clear() - - return - - def _get_iters_per_update(self): - return 1 - - def _valid_train_step(self): - samples = self.replay_buffer.get_current_size() - exp_samples = self.replay_buffer.count_filtered(self.EXP_ACTION_FLAG) - global_sample_count = int(MPIUtil.reduce_sum(samples)) - global_exp_min = int(MPIUtil.reduce_min(exp_samples)) - return (global_sample_count > self.batch_size) and (global_exp_min > 0) - - def _compute_batch_vals(self, start_idx, end_idx): - states = self.replay_buffer.get_all("states")[start_idx:end_idx] - goals = self.replay_buffer.get_all("goals")[start_idx:end_idx] if self.has_goal() else None - - idx = np.array(list(range(start_idx, end_idx))) - is_end = self.replay_buffer.is_path_end(idx) - is_fail = self.replay_buffer.check_terminal_flag(idx, Env.Terminate.Fail) - is_succ = self.replay_buffer.check_terminal_flag(idx, Env.Terminate.Succ) - is_fail = np.logical_and(is_end, is_fail) - is_succ = np.logical_and(is_end, is_succ) - - vals = self._eval_critic(states, goals) - vals[is_fail] = self.val_fail - vals[is_succ] = self.val_succ - - return vals - - def _compute_batch_new_vals(self, start_idx, end_idx, val_buffer): - rewards = self.replay_buffer.get_all("rewards")[start_idx:end_idx] - - if self.discount == 0: - new_vals = rewards.copy() + NAME = "PPO" + EPOCHS_KEY = "Epochs" + BATCH_SIZE_KEY = "BatchSize" + RATIO_CLIP_KEY = "RatioClip" + NORM_ADV_CLIP_KEY = "NormAdvClip" + TD_LAMBDA_KEY = "TDLambda" + TAR_CLIP_FRAC = "TarClipFrac" + ACTOR_STEPSIZE_DECAY = "ActorStepsizeDecay" + + def __init__(self, world, id, json_data): + super().__init__(world, id, json_data) + return + + def _load_params(self, json_data): + super()._load_params(json_data) + + self.epochs = 1 if (self.EPOCHS_KEY not in json_data) else json_data[self.EPOCHS_KEY] + self.batch_size = 1024 if ( + self.BATCH_SIZE_KEY not in json_data) else json_data[self.BATCH_SIZE_KEY] + self.ratio_clip = 0.2 if ( + self.RATIO_CLIP_KEY not in json_data) else json_data[self.RATIO_CLIP_KEY] + self.norm_adv_clip = 5 if ( + self.NORM_ADV_CLIP_KEY not in json_data) else json_data[self.NORM_ADV_CLIP_KEY] + self.td_lambda = 0.95 if ( + self.TD_LAMBDA_KEY not in json_data) else json_data[self.TD_LAMBDA_KEY] + self.tar_clip_frac = -1 if ( + self.TAR_CLIP_FRAC not in json_data) else json_data[self.TAR_CLIP_FRAC] + self.actor_stepsize_decay = 0.5 if ( + self.ACTOR_STEPSIZE_DECAY not in json_data) else json_data[self.ACTOR_STEPSIZE_DECAY] + + num_procs = MPIUtil.get_num_procs() + local_batch_size = int(self.batch_size / num_procs) + min_replay_size = 2 * local_batch_size # needed to prevent buffer overflow + assert (self.replay_buffer_size > min_replay_size) + + self.replay_buffer_size = np.maximum(min_replay_size, self.replay_buffer_size) + + return + + def _build_nets(self, json_data): + assert self.ACTOR_NET_KEY in json_data + assert self.CRITIC_NET_KEY in json_data + + actor_net_name = json_data[self.ACTOR_NET_KEY] + critic_net_name = json_data[self.CRITIC_NET_KEY] + actor_init_output_scale = 1 if (self.ACTOR_INIT_OUTPUT_SCALE_KEY not in json_data + ) else json_data[self.ACTOR_INIT_OUTPUT_SCALE_KEY] + + s_size = self.get_state_size() + g_size = self.get_goal_size() + a_size = self.get_action_size() + + # setup input tensors + self.s_tf = tf.placeholder(tf.float32, shape=[None, s_size], name="s") + self.a_tf = tf.placeholder(tf.float32, shape=[None, a_size], name="a") + self.tar_val_tf = tf.placeholder(tf.float32, shape=[None], name="tar_val") + self.adv_tf = tf.placeholder(tf.float32, shape=[None], name="adv") + self.g_tf = tf.placeholder(tf.float32, + shape=([None, g_size] if self.has_goal() else None), + name="g") + self.old_logp_tf = tf.placeholder(tf.float32, shape=[None], name="old_logp") + self.exp_mask_tf = tf.placeholder(tf.float32, shape=[None], name="exp_mask") + + with tf.variable_scope('main'): + with tf.variable_scope('actor'): + self.a_mean_tf = self._build_net_actor(actor_net_name, actor_init_output_scale) + with tf.variable_scope('critic'): + self.critic_tf = self._build_net_critic(critic_net_name) + + if (self.a_mean_tf != None): + Logger.print2('Built actor net: ' + actor_net_name) + + if (self.critic_tf != None): + Logger.print2('Built critic net: ' + critic_net_name) + + self.norm_a_std_tf = self.exp_params_curr.noise * tf.ones(a_size) + norm_a_noise_tf = self.norm_a_std_tf * tf.random_normal(shape=tf.shape(self.a_mean_tf)) + norm_a_noise_tf *= tf.expand_dims(self.exp_mask_tf, axis=-1) + self.sample_a_tf = self.a_mean_tf + norm_a_noise_tf * self.a_norm.std_tf + self.sample_a_logp_tf = TFUtil.calc_logp_gaussian(x_tf=norm_a_noise_tf, + mean_tf=None, + std_tf=self.norm_a_std_tf) + + return + + def _build_losses(self, json_data): + actor_weight_decay = 0 if ( + self.ACTOR_WEIGHT_DECAY_KEY not in json_data) else json_data[self.ACTOR_WEIGHT_DECAY_KEY] + critic_weight_decay = 0 if ( + self.CRITIC_WEIGHT_DECAY_KEY not in json_data) else json_data[self.CRITIC_WEIGHT_DECAY_KEY] + + norm_val_diff = self.val_norm.normalize_tf(self.tar_val_tf) - self.val_norm.normalize_tf( + self.critic_tf) + self.critic_loss_tf = 0.5 * tf.reduce_mean(tf.square(norm_val_diff)) + + if (critic_weight_decay != 0): + self.critic_loss_tf += critic_weight_decay * self._weight_decay_loss('main/critic') + + norm_tar_a_tf = self.a_norm.normalize_tf(self.a_tf) + self._norm_a_mean_tf = self.a_norm.normalize_tf(self.a_mean_tf) + + self.logp_tf = TFUtil.calc_logp_gaussian(norm_tar_a_tf, self._norm_a_mean_tf, + self.norm_a_std_tf) + ratio_tf = tf.exp(self.logp_tf - self.old_logp_tf) + actor_loss0 = self.adv_tf * ratio_tf + actor_loss1 = self.adv_tf * tf.clip_by_value(ratio_tf, 1.0 - self.ratio_clip, + 1 + self.ratio_clip) + self.actor_loss_tf = -tf.reduce_mean(tf.minimum(actor_loss0, actor_loss1)) + + norm_a_bound_min = self.a_norm.normalize(self.a_bound_min) + norm_a_bound_max = self.a_norm.normalize(self.a_bound_max) + a_bound_loss = TFUtil.calc_bound_loss(self._norm_a_mean_tf, norm_a_bound_min, norm_a_bound_max) + self.actor_loss_tf += a_bound_loss + + if (actor_weight_decay != 0): + self.actor_loss_tf += actor_weight_decay * self._weight_decay_loss('main/actor') + + # for debugging + self.clip_frac_tf = tf.reduce_mean( + tf.to_float(tf.greater(tf.abs(ratio_tf - 1.0), self.ratio_clip))) + + return + + def _build_solvers(self, json_data): + actor_stepsize = 0.001 if ( + self.ACTOR_STEPSIZE_KEY not in json_data) else json_data[self.ACTOR_STEPSIZE_KEY] + actor_momentum = 0.9 if ( + self.ACTOR_MOMENTUM_KEY not in json_data) else json_data[self.ACTOR_MOMENTUM_KEY] + critic_stepsize = 0.01 if ( + self.CRITIC_STEPSIZE_KEY not in json_data) else json_data[self.CRITIC_STEPSIZE_KEY] + critic_momentum = 0.9 if ( + self.CRITIC_MOMENTUM_KEY not in json_data) else json_data[self.CRITIC_MOMENTUM_KEY] + + critic_vars = self._tf_vars('main/critic') + critic_opt = tf.train.MomentumOptimizer(learning_rate=critic_stepsize, + momentum=critic_momentum) + self.critic_grad_tf = tf.gradients(self.critic_loss_tf, critic_vars) + self.critic_solver = MPISolver(self.sess, critic_opt, critic_vars) + + self._actor_stepsize_tf = tf.get_variable(dtype=tf.float32, + name='actor_stepsize', + initializer=actor_stepsize, + trainable=False) + self._actor_stepsize_ph = tf.get_variable(dtype=tf.float32, name='actor_stepsize_ph', shape=[]) + self._actor_stepsize_update_op = self._actor_stepsize_tf.assign(self._actor_stepsize_ph) + + actor_vars = self._tf_vars('main/actor') + actor_opt = tf.train.MomentumOptimizer(learning_rate=self._actor_stepsize_tf, + momentum=actor_momentum) + self.actor_grad_tf = tf.gradients(self.actor_loss_tf, actor_vars) + self.actor_solver = MPISolver(self.sess, actor_opt, actor_vars) + + return + + def _decide_action(self, s, g): + with self.sess.as_default(), self.graph.as_default(): + self._exp_action = self._enable_stoch_policy() and MathUtil.flip_coin( + self.exp_params_curr.rate) + #print("_decide_action._exp_action=",self._exp_action) + a, logp = self._eval_actor(s, g, self._exp_action) + return a[0], logp[0] + + def _eval_actor(self, s, g, enable_exp): + s = np.reshape(s, [-1, self.get_state_size()]) + g = np.reshape(g, [-1, self.get_goal_size()]) if self.has_goal() else None + + feed = {self.s_tf: s, self.g_tf: g, self.exp_mask_tf: np.array([1 if enable_exp else 0])} + + a, logp = self.sess.run([self.sample_a_tf, self.sample_a_logp_tf], feed_dict=feed) + return a, logp + + def _train_step(self): + adv_eps = 1e-5 + + start_idx = self.replay_buffer.buffer_tail + end_idx = self.replay_buffer.buffer_head + assert (start_idx == 0) + assert (self.replay_buffer.get_current_size() <= self.replay_buffer.buffer_size + ) # must avoid overflow + assert (start_idx < end_idx) + + idx = np.array(list(range(start_idx, end_idx))) + end_mask = self.replay_buffer.is_path_end(idx) + end_mask = np.logical_not(end_mask) + + vals = self._compute_batch_vals(start_idx, end_idx) + new_vals = self._compute_batch_new_vals(start_idx, end_idx, vals) + + valid_idx = idx[end_mask] + exp_idx = self.replay_buffer.get_idx_filtered(self.EXP_ACTION_FLAG).copy() + num_valid_idx = valid_idx.shape[0] + num_exp_idx = exp_idx.shape[0] + exp_idx = np.column_stack([exp_idx, np.array(list(range(0, num_exp_idx)), dtype=np.int32)]) + + local_sample_count = valid_idx.size + global_sample_count = int(MPIUtil.reduce_sum(local_sample_count)) + mini_batches = int(np.ceil(global_sample_count / self.mini_batch_size)) + + adv = new_vals[exp_idx[:, 0]] - vals[exp_idx[:, 0]] + new_vals = np.clip(new_vals, self.val_min, self.val_max) + + adv_mean = np.mean(adv) + adv_std = np.std(adv) + adv = (adv - adv_mean) / (adv_std + adv_eps) + adv = np.clip(adv, -self.norm_adv_clip, self.norm_adv_clip) + + critic_loss = 0 + actor_loss = 0 + actor_clip_frac = 0 + + for e in range(self.epochs): + np.random.shuffle(valid_idx) + np.random.shuffle(exp_idx) + + for b in range(mini_batches): + batch_idx_beg = b * self._local_mini_batch_size + batch_idx_end = batch_idx_beg + self._local_mini_batch_size + + critic_batch = np.array(range(batch_idx_beg, batch_idx_end), dtype=np.int32) + actor_batch = critic_batch.copy() + critic_batch = np.mod(critic_batch, num_valid_idx) + actor_batch = np.mod(actor_batch, num_exp_idx) + shuffle_actor = (actor_batch[-1] < actor_batch[0]) or (actor_batch[-1] == num_exp_idx - 1) + + critic_batch = valid_idx[critic_batch] + actor_batch = exp_idx[actor_batch] + critic_batch_vals = new_vals[critic_batch] + actor_batch_adv = adv[actor_batch[:, 1]] + + critic_s = self.replay_buffer.get('states', critic_batch) + critic_g = self.replay_buffer.get('goals', critic_batch) if self.has_goal() else None + curr_critic_loss = self._update_critic(critic_s, critic_g, critic_batch_vals) + + actor_s = self.replay_buffer.get("states", actor_batch[:, 0]) + actor_g = self.replay_buffer.get("goals", actor_batch[:, 0]) if self.has_goal() else None + actor_a = self.replay_buffer.get("actions", actor_batch[:, 0]) + actor_logp = self.replay_buffer.get("logps", actor_batch[:, 0]) + curr_actor_loss, curr_actor_clip_frac = self._update_actor(actor_s, actor_g, actor_a, + actor_logp, actor_batch_adv) + + critic_loss += curr_critic_loss + actor_loss += np.abs(curr_actor_loss) + actor_clip_frac += curr_actor_clip_frac + + if (shuffle_actor): + np.random.shuffle(exp_idx) + + total_batches = mini_batches * self.epochs + critic_loss /= total_batches + actor_loss /= total_batches + actor_clip_frac /= total_batches + + critic_loss = MPIUtil.reduce_avg(critic_loss) + actor_loss = MPIUtil.reduce_avg(actor_loss) + actor_clip_frac = MPIUtil.reduce_avg(actor_clip_frac) + + critic_stepsize = self.critic_solver.get_stepsize() + actor_stepsize = self.update_actor_stepsize(actor_clip_frac) + + self.logger.log_tabular('Critic_Loss', critic_loss) + self.logger.log_tabular('Critic_Stepsize', critic_stepsize) + self.logger.log_tabular('Actor_Loss', actor_loss) + self.logger.log_tabular('Actor_Stepsize', actor_stepsize) + self.logger.log_tabular('Clip_Frac', actor_clip_frac) + self.logger.log_tabular('Adv_Mean', adv_mean) + self.logger.log_tabular('Adv_Std', adv_std) + + self.replay_buffer.clear() + + return + + def _get_iters_per_update(self): + return 1 + + def _valid_train_step(self): + samples = self.replay_buffer.get_current_size() + exp_samples = self.replay_buffer.count_filtered(self.EXP_ACTION_FLAG) + global_sample_count = int(MPIUtil.reduce_sum(samples)) + global_exp_min = int(MPIUtil.reduce_min(exp_samples)) + return (global_sample_count > self.batch_size) and (global_exp_min > 0) + + def _compute_batch_vals(self, start_idx, end_idx): + states = self.replay_buffer.get_all("states")[start_idx:end_idx] + goals = self.replay_buffer.get_all("goals")[start_idx:end_idx] if self.has_goal() else None + + idx = np.array(list(range(start_idx, end_idx))) + is_end = self.replay_buffer.is_path_end(idx) + is_fail = self.replay_buffer.check_terminal_flag(idx, Env.Terminate.Fail) + is_succ = self.replay_buffer.check_terminal_flag(idx, Env.Terminate.Succ) + is_fail = np.logical_and(is_end, is_fail) + is_succ = np.logical_and(is_end, is_succ) + + vals = self._eval_critic(states, goals) + vals[is_fail] = self.val_fail + vals[is_succ] = self.val_succ + + return vals + + def _compute_batch_new_vals(self, start_idx, end_idx, val_buffer): + rewards = self.replay_buffer.get_all("rewards")[start_idx:end_idx] + + if self.discount == 0: + new_vals = rewards.copy() + else: + new_vals = np.zeros_like(val_buffer) + + curr_idx = start_idx + while curr_idx < end_idx: + idx0 = curr_idx - start_idx + idx1 = self.replay_buffer.get_path_end(curr_idx) - start_idx + r = rewards[idx0:idx1] + v = val_buffer[idx0:(idx1 + 1)] + + new_vals[idx0:idx1] = RLUtil.compute_return(r, self.discount, self.td_lambda, v) + curr_idx = idx1 + start_idx + 1 + + return new_vals + + def _update_critic(self, s, g, tar_vals): + feed = {self.s_tf: s, self.g_tf: g, self.tar_val_tf: tar_vals} + + loss, grads = self.sess.run([self.critic_loss_tf, self.critic_grad_tf], feed) + self.critic_solver.update(grads) + return loss + + def _update_actor(self, s, g, a, logp, adv): + feed = {self.s_tf: s, self.g_tf: g, self.a_tf: a, self.adv_tf: adv, self.old_logp_tf: logp} + + loss, grads, clip_frac = self.sess.run( + [self.actor_loss_tf, self.actor_grad_tf, self.clip_frac_tf], feed) + self.actor_solver.update(grads) + + return loss, clip_frac + + def update_actor_stepsize(self, clip_frac): + clip_tol = 1.5 + step_scale = 2 + max_stepsize = 1e-2 + min_stepsize = 1e-8 + warmup_iters = 5 + + actor_stepsize = self.actor_solver.get_stepsize() + if (self.tar_clip_frac >= 0 and self.iter > warmup_iters): + min_clip = self.tar_clip_frac / clip_tol + max_clip = self.tar_clip_frac * clip_tol + under_tol = clip_frac < min_clip + over_tol = clip_frac > max_clip + + if (over_tol or under_tol): + if (over_tol): + actor_stepsize *= self.actor_stepsize_decay else: - new_vals = np.zeros_like(val_buffer) - - curr_idx = start_idx - while curr_idx < end_idx: - idx0 = curr_idx - start_idx - idx1 = self.replay_buffer.get_path_end(curr_idx) - start_idx - r = rewards[idx0:idx1] - v = val_buffer[idx0:(idx1 + 1)] - - new_vals[idx0:idx1] = RLUtil.compute_return(r, self.discount, self.td_lambda, v) - curr_idx = idx1 + start_idx + 1 - - return new_vals - - def _update_critic(self, s, g, tar_vals): - feed = { - self.s_tf: s, - self.g_tf: g, - self.tar_val_tf: tar_vals - } - - loss, grads = self.sess.run([self.critic_loss_tf, self.critic_grad_tf], feed) - self.critic_solver.update(grads) - return loss - - def _update_actor(self, s, g, a, logp, adv): - feed = { - self.s_tf: s, - self.g_tf: g, - self.a_tf: a, - self.adv_tf: adv, - self.old_logp_tf: logp - } - - loss, grads, clip_frac = self.sess.run([self.actor_loss_tf, self.actor_grad_tf, - self.clip_frac_tf], feed) - self.actor_solver.update(grads) - - return loss, clip_frac - - def update_actor_stepsize(self, clip_frac): - clip_tol = 1.5 - step_scale = 2 - max_stepsize = 1e-2 - min_stepsize = 1e-8 - warmup_iters = 5 - - actor_stepsize = self.actor_solver.get_stepsize() - if (self.tar_clip_frac >= 0 and self.iter > warmup_iters): - min_clip = self.tar_clip_frac / clip_tol - max_clip = self.tar_clip_frac * clip_tol - under_tol = clip_frac < min_clip - over_tol = clip_frac > max_clip - - if (over_tol or under_tol): - if (over_tol): - actor_stepsize *= self.actor_stepsize_decay - else: - actor_stepsize /= self.actor_stepsize_decay - - actor_stepsize = np.clip(actor_stepsize, min_stepsize, max_stepsize) - self.set_actor_stepsize(actor_stepsize) - - return actor_stepsize - - def set_actor_stepsize(self, stepsize): - feed = { - self._actor_stepsize_ph: stepsize, - } - self.sess.run(self._actor_stepsize_update_op, feed) - return + actor_stepsize /= self.actor_stepsize_decay + + actor_stepsize = np.clip(actor_stepsize, min_stepsize, max_stepsize) + self.set_actor_stepsize(actor_stepsize) + + return actor_stepsize + + def set_actor_stepsize(self, stepsize): + feed = { + self._actor_stepsize_ph: stepsize, + } + self.sess.run(self._actor_stepsize_update_op, feed) + return diff --git a/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/replay_buffer.py b/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/replay_buffer.py index 284515851..f002c7572 100644 --- a/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/replay_buffer.py +++ b/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/replay_buffer.py @@ -5,347 +5,353 @@ import inspect as inspect from pybullet_envs.deep_mimic.env.env import Env import pybullet_utils.math_util as MathUtil + class ReplayBuffer(object): - TERMINATE_KEY = 'terminate' - PATH_START_KEY = 'path_start' - PATH_END_KEY = 'path_end' - - def __init__(self, buffer_size): - assert buffer_size > 0 - - self.buffer_size = buffer_size - self.total_count = 0 - self.buffer_head = 0 - self.buffer_tail = MathUtil.INVALID_IDX - self.num_paths = 0 - self._sample_buffers = dict() - self.buffers = None - - self.clear() - return - - def sample(self, n): - curr_size = self.get_current_size() - assert curr_size > 0 - - idx = np.empty(n, dtype=int) - # makes sure that the end states are not sampled - for i in range(n): - while True: - curr_idx = np.random.randint(0, curr_size, size=1)[0] - curr_idx += self.buffer_tail - curr_idx = np.mod(curr_idx, self.buffer_size) - - if not self.is_path_end(curr_idx): - break - idx[i] = curr_idx - - return idx - - def sample_filtered(self, n, key): - assert key in self._sample_buffers - curr_buffer = self._sample_buffers[key] - idx = curr_buffer.sample(n) - return idx - - def count_filtered(self, key): - curr_buffer = self._sample_buffers[key] - return curr_buffer.count - - def get(self, key, idx): - return self.buffers[key][idx] - - def get_all(self, key): - return self.buffers[key] - - def get_idx_filtered(self, key): - assert key in self._sample_buffers - curr_buffer = self._sample_buffers[key] - idx = curr_buffer.slot_to_idx[:curr_buffer.count] - return idx - - def get_path_start(self, idx): - return self.buffers[self.PATH_START_KEY][idx] - - def get_path_end(self, idx): - return self.buffers[self.PATH_END_KEY][idx] - - def get_pathlen(self, idx): - is_array = isinstance(idx, np.ndarray) or isinstance(idx, list) - if not is_array: - idx = [idx] - - n = len(idx) - start_idx = self.get_path_start(idx) - end_idx = self.get_path_end(idx) - pathlen = np.empty(n, dtype=int) - - for i in range(n): - curr_start = start_idx[i] - curr_end = end_idx[i] - if curr_start < curr_end: - curr_len = curr_end - curr_start + TERMINATE_KEY = 'terminate' + PATH_START_KEY = 'path_start' + PATH_END_KEY = 'path_end' + + def __init__(self, buffer_size): + assert buffer_size > 0 + + self.buffer_size = buffer_size + self.total_count = 0 + self.buffer_head = 0 + self.buffer_tail = MathUtil.INVALID_IDX + self.num_paths = 0 + self._sample_buffers = dict() + self.buffers = None + + self.clear() + return + + def sample(self, n): + curr_size = self.get_current_size() + assert curr_size > 0 + + idx = np.empty(n, dtype=int) + # makes sure that the end states are not sampled + for i in range(n): + while True: + curr_idx = np.random.randint(0, curr_size, size=1)[0] + curr_idx += self.buffer_tail + curr_idx = np.mod(curr_idx, self.buffer_size) + + if not self.is_path_end(curr_idx): + break + idx[i] = curr_idx + + return idx + + def sample_filtered(self, n, key): + assert key in self._sample_buffers + curr_buffer = self._sample_buffers[key] + idx = curr_buffer.sample(n) + return idx + + def count_filtered(self, key): + curr_buffer = self._sample_buffers[key] + return curr_buffer.count + + def get(self, key, idx): + return self.buffers[key][idx] + + def get_all(self, key): + return self.buffers[key] + + def get_idx_filtered(self, key): + assert key in self._sample_buffers + curr_buffer = self._sample_buffers[key] + idx = curr_buffer.slot_to_idx[:curr_buffer.count] + return idx + + def get_path_start(self, idx): + return self.buffers[self.PATH_START_KEY][idx] + + def get_path_end(self, idx): + return self.buffers[self.PATH_END_KEY][idx] + + def get_pathlen(self, idx): + is_array = isinstance(idx, np.ndarray) or isinstance(idx, list) + if not is_array: + idx = [idx] + + n = len(idx) + start_idx = self.get_path_start(idx) + end_idx = self.get_path_end(idx) + pathlen = np.empty(n, dtype=int) + + for i in range(n): + curr_start = start_idx[i] + curr_end = end_idx[i] + if curr_start < curr_end: + curr_len = curr_end - curr_start + else: + curr_len = self.buffer_size - curr_start + curr_end + pathlen[i] = curr_len + + if not is_array: + pathlen = pathlen[0] + + return pathlen + + def is_valid_path(self, idx): + start_idx = self.get_path_start(idx) + valid = start_idx != MathUtil.INVALID_IDX + return valid + + def store(self, path): + start_idx = MathUtil.INVALID_IDX + n = path.pathlength() + + if (n > 0): + assert path.is_valid() + + if path.check_vals(): + if self.buffers is None: + self._init_buffers(path) + + idx = self._request_idx(n + 1) + self._store_path(path, idx) + self._add_sample_buffers(idx) + + self.num_paths += 1 + self.total_count += n + 1 + start_idx = idx[0] + else: + Logger.print2('Invalid path data value detected') + + return start_idx + + def clear(self): + self.buffer_head = 0 + self.buffer_tail = MathUtil.INVALID_IDX + self.num_paths = 0 + + for key in self._sample_buffers: + self._sample_buffers[key].clear() + return + + def get_next_idx(self, idx): + next_idx = np.mod(idx + 1, self.buffer_size) + return next_idx + + def is_terminal_state(self, idx): + terminate_flags = self.buffers[self.TERMINATE_KEY][idx] + terminate = terminate_flags != Env.Terminate.Null.value + is_end = self.is_path_end(idx) + terminal_state = np.logical_and(terminate, is_end) + return terminal_state + + def check_terminal_flag(self, idx, flag): + terminate_flags = self.buffers[self.TERMINATE_KEY][idx] + terminate = terminate_flags == flag.value + return terminate + + def is_path_end(self, idx): + is_end = self.buffers[self.PATH_END_KEY][idx] == idx + return is_end + + def add_filter_key(self, key): + assert self.get_current_size() == 0 + if key not in self._sample_buffers: + self._sample_buffers[key] = SampleBuffer(self.buffer_size) + return + + def get_current_size(self): + if self.buffer_tail == MathUtil.INVALID_IDX: + return 0 + elif self.buffer_tail < self.buffer_head: + return self.buffer_head - self.buffer_tail + else: + return self.buffer_size - self.buffer_tail + self.buffer_head + + def _check_flags(self, key, flags): + return (flags & key) == key + + def _add_sample_buffers(self, idx): + flags = self.buffers['flags'] + for key in self._sample_buffers: + curr_buffer = self._sample_buffers[key] + filter_idx = [ + i for i in idx if (self._check_flags(key, flags[i]) and not self.is_path_end(i)) + ] + curr_buffer.add(filter_idx) + return + + def _free_sample_buffers(self, idx): + for key in self._sample_buffers: + curr_buffer = self._sample_buffers[key] + curr_buffer.free(idx) + return + + def _init_buffers(self, path): + self.buffers = dict() + self.buffers[self.PATH_START_KEY] = MathUtil.INVALID_IDX * np.ones(self.buffer_size, dtype=int) + self.buffers[self.PATH_END_KEY] = MathUtil.INVALID_IDX * np.ones(self.buffer_size, dtype=int) + + for key in dir(path): + val = getattr(path, key) + if not key.startswith('__') and not inspect.ismethod(val): + if key == self.TERMINATE_KEY: + self.buffers[self.TERMINATE_KEY] = np.zeros(shape=[self.buffer_size], dtype=int) + else: + val_type = type(val[0]) + is_array = val_type == np.ndarray + if is_array: + shape = [self.buffer_size, val[0].shape[0]] + dtype = val[0].dtype + else: + shape = [self.buffer_size] + dtype = val_type + + self.buffers[key] = np.zeros(shape, dtype=dtype) + return + + def _request_idx(self, n): + assert n + 1 < self.buffer_size # bad things can happen if path is too long + + remainder = n + idx = [] + + start_idx = self.buffer_head + while remainder > 0: + end_idx = np.minimum(start_idx + remainder, self.buffer_size) + remainder -= (end_idx - start_idx) + + free_idx = list(range(start_idx, end_idx)) + self._free_idx(free_idx) + idx += free_idx + start_idx = 0 + + self.buffer_head = (self.buffer_head + n) % self.buffer_size + return idx + + def _free_idx(self, idx): + assert (idx[0] <= idx[-1]) + n = len(idx) + if self.buffer_tail != MathUtil.INVALID_IDX: + update_tail = idx[0] <= idx[-1] and idx[0] <= self.buffer_tail and idx[-1] >= self.buffer_tail + update_tail |= idx[0] > idx[-1] and (idx[0] <= self.buffer_tail or + idx[-1] >= self.buffer_tail) + + if update_tail: + i = 0 + while i < n: + curr_idx = idx[i] + if self.is_valid_path(curr_idx): + start_idx = self.get_path_start(curr_idx) + end_idx = self.get_path_end(curr_idx) + pathlen = self.get_pathlen(curr_idx) + + if start_idx < end_idx: + self.buffers[self.PATH_START_KEY][start_idx:end_idx + 1] = MathUtil.INVALID_IDX + self._free_sample_buffers(list(range(start_idx, end_idx + 1))) else: - curr_len = self.buffer_size - curr_start + curr_end - pathlen[i] = curr_len - - if not is_array: - pathlen = pathlen[0] - - return pathlen - - def is_valid_path(self, idx): - start_idx = self.get_path_start(idx) - valid = start_idx != MathUtil.INVALID_IDX - return valid - - def store(self, path): - start_idx = MathUtil.INVALID_IDX - n = path.pathlength() - - if (n > 0): - assert path.is_valid() - - if path.check_vals(): - if self.buffers is None: - self._init_buffers(path) + self.buffers[self.PATH_START_KEY][start_idx:self.buffer_size] = MathUtil.INVALID_IDX + self.buffers[self.PATH_START_KEY][0:end_idx + 1] = MathUtil.INVALID_IDX + self._free_sample_buffers(list(range(start_idx, self.buffer_size))) + self._free_sample_buffers(list(range(0, end_idx + 1))) + + self.num_paths -= 1 + i += pathlen + 1 + self.buffer_tail = (end_idx + 1) % self.buffer_size + else: + i += 1 + else: + self.buffer_tail = idx[0] + return + + def _store_path(self, path, idx): + n = path.pathlength() + for key, data in self.buffers.items(): + if key != self.PATH_START_KEY and key != self.PATH_END_KEY and key != self.TERMINATE_KEY: + val = getattr(path, key) + val_len = len(val) + assert val_len == n or val_len == n + 1 + data[idx[:val_len]] = val + + self.buffers[self.TERMINATE_KEY][idx] = path.terminate.value + self.buffers[self.PATH_START_KEY][idx] = idx[0] + self.buffers[self.PATH_END_KEY][idx] = idx[-1] + return - idx = self._request_idx(n + 1) - self._store_path(path, idx) - self._add_sample_buffers(idx) - - self.num_paths += 1 - self.total_count += n + 1 - start_idx = idx[0] - else: - Logger.print2('Invalid path data value detected') - - return start_idx - - def clear(self): - self.buffer_head = 0 - self.buffer_tail = MathUtil.INVALID_IDX - self.num_paths = 0 - - for key in self._sample_buffers: - self._sample_buffers[key].clear() - return - - def get_next_idx(self, idx): - next_idx = np.mod(idx + 1, self.buffer_size) - return next_idx - - def is_terminal_state(self, idx): - terminate_flags = self.buffers[self.TERMINATE_KEY][idx] - terminate = terminate_flags != Env.Terminate.Null.value - is_end = self.is_path_end(idx) - terminal_state = np.logical_and(terminate, is_end) - return terminal_state - - def check_terminal_flag(self, idx, flag): - terminate_flags = self.buffers[self.TERMINATE_KEY][idx] - terminate = terminate_flags == flag.value - return terminate - - def is_path_end(self, idx): - is_end = self.buffers[self.PATH_END_KEY][idx] == idx - return is_end - - def add_filter_key(self, key): - assert self.get_current_size() == 0 - if key not in self._sample_buffers: - self._sample_buffers[key] = SampleBuffer(self.buffer_size) - return - - def get_current_size(self): - if self.buffer_tail == MathUtil.INVALID_IDX: - return 0 - elif self.buffer_tail < self.buffer_head: - return self.buffer_head - self.buffer_tail - else: - return self.buffer_size - self.buffer_tail + self.buffer_head - - def _check_flags(self, key, flags): - return (flags & key) == key - - def _add_sample_buffers(self, idx): - flags = self.buffers['flags'] - for key in self._sample_buffers: - curr_buffer = self._sample_buffers[key] - filter_idx = [i for i in idx if (self._check_flags(key, flags[i]) and not self.is_path_end(i))] - curr_buffer.add(filter_idx) - return - - def _free_sample_buffers(self, idx): - for key in self._sample_buffers: - curr_buffer = self._sample_buffers[key] - curr_buffer.free(idx) - return - - def _init_buffers(self, path): - self.buffers = dict() - self.buffers[self.PATH_START_KEY] = MathUtil.INVALID_IDX * np.ones(self.buffer_size, dtype=int); - self.buffers[self.PATH_END_KEY] = MathUtil.INVALID_IDX * np.ones(self.buffer_size, dtype=int); - - for key in dir(path): - val = getattr(path, key) - if not key.startswith('__') and not inspect.ismethod(val): - if key == self.TERMINATE_KEY: - self.buffers[self.TERMINATE_KEY] = np.zeros(shape=[self.buffer_size], dtype=int) - else: - val_type = type(val[0]) - is_array = val_type == np.ndarray - if is_array: - shape = [self.buffer_size, val[0].shape[0]] - dtype = val[0].dtype - else: - shape = [self.buffer_size] - dtype = val_type - - self.buffers[key] = np.zeros(shape, dtype=dtype) - return - - def _request_idx(self, n): - assert n + 1 < self.buffer_size # bad things can happen if path is too long - - remainder = n - idx = [] - - start_idx = self.buffer_head - while remainder > 0: - end_idx = np.minimum(start_idx + remainder, self.buffer_size) - remainder -= (end_idx - start_idx) - - free_idx = list(range(start_idx, end_idx)) - self._free_idx(free_idx) - idx += free_idx - start_idx = 0 - - self.buffer_head = (self.buffer_head + n) % self.buffer_size - return idx - - def _free_idx(self, idx): - assert(idx[0] <= idx[-1]) - n = len(idx) - if self.buffer_tail != MathUtil.INVALID_IDX: - update_tail = idx[0] <= idx[-1] and idx[0] <= self.buffer_tail and idx[-1] >= self.buffer_tail - update_tail |= idx[0] > idx[-1] and (idx[0] <= self.buffer_tail or idx[-1] >= self.buffer_tail) - - if update_tail: - i = 0 - while i < n: - curr_idx = idx[i] - if self.is_valid_path(curr_idx): - start_idx = self.get_path_start(curr_idx) - end_idx = self.get_path_end(curr_idx) - pathlen = self.get_pathlen(curr_idx) - - if start_idx < end_idx: - self.buffers[self.PATH_START_KEY][start_idx:end_idx + 1] = MathUtil.INVALID_IDX - self._free_sample_buffers(list(range(start_idx, end_idx + 1))) - else: - self.buffers[self.PATH_START_KEY][start_idx:self.buffer_size] = MathUtil.INVALID_IDX - self.buffers[self.PATH_START_KEY][0:end_idx + 1] = MathUtil.INVALID_IDX - self._free_sample_buffers(list(range(start_idx, self.buffer_size))) - self._free_sample_buffers(list(range(0, end_idx + 1))) - - self.num_paths -= 1 - i += pathlen + 1 - self.buffer_tail = (end_idx + 1) % self.buffer_size; - else: - i += 1 - else: - self.buffer_tail = idx[0] - return - - def _store_path(self, path, idx): - n = path.pathlength() - for key, data in self.buffers.items(): - if key != self.PATH_START_KEY and key != self.PATH_END_KEY and key != self.TERMINATE_KEY: - val = getattr(path, key) - val_len = len(val) - assert val_len == n or val_len == n + 1 - data[idx[:val_len]] = val - - self.buffers[self.TERMINATE_KEY][idx] = path.terminate.value - self.buffers[self.PATH_START_KEY][idx] = idx[0] - self.buffers[self.PATH_END_KEY][idx] = idx[-1] - return class SampleBuffer(object): - def __init__(self, size): - self.idx_to_slot = np.empty(shape=[size], dtype=int) - self.slot_to_idx = np.empty(shape=[size], dtype=int) - self.count = 0 - self.clear() - return - - def clear(self): - self.idx_to_slot.fill(MathUtil.INVALID_IDX) - self.slot_to_idx.fill(MathUtil.INVALID_IDX) - self.count = 0 - return - - def is_valid(self, idx): - return self.idx_to_slot[idx] != MathUtil.INVALID_IDX - - def get_size(self): - return self.idx_to_slot.shape[0] - - def add(self, idx): - for i in idx: - if not self.is_valid(i): - new_slot = self.count - assert new_slot >= 0 - - self.idx_to_slot[i] = new_slot - self.slot_to_idx[new_slot] = i - self.count += 1 - return - - def free(self, idx): - for i in idx: - if self.is_valid(i): - slot = self.idx_to_slot[i] - last_slot = self.count - 1 - last_idx = self.slot_to_idx[last_slot] - - self.idx_to_slot[last_idx] = slot - self.slot_to_idx[slot] = last_idx - self.idx_to_slot[i] = MathUtil.INVALID_IDX - self.slot_to_idx[last_slot] = MathUtil.INVALID_IDX - self.count -= 1 - return - - def sample(self, n): - if self.count > 0: - slots = np.random.randint(0, self.count, size=n) - idx = self.slot_to_idx[slots] - else: - idx = np.empty(shape=[0], dtype=int) - return idx - def check_consistency(self): - valid = True - if self.count < 0: + def __init__(self, size): + self.idx_to_slot = np.empty(shape=[size], dtype=int) + self.slot_to_idx = np.empty(shape=[size], dtype=int) + self.count = 0 + self.clear() + return + + def clear(self): + self.idx_to_slot.fill(MathUtil.INVALID_IDX) + self.slot_to_idx.fill(MathUtil.INVALID_IDX) + self.count = 0 + return + + def is_valid(self, idx): + return self.idx_to_slot[idx] != MathUtil.INVALID_IDX + + def get_size(self): + return self.idx_to_slot.shape[0] + + def add(self, idx): + for i in idx: + if not self.is_valid(i): + new_slot = self.count + assert new_slot >= 0 + + self.idx_to_slot[i] = new_slot + self.slot_to_idx[new_slot] = i + self.count += 1 + return + + def free(self, idx): + for i in idx: + if self.is_valid(i): + slot = self.idx_to_slot[i] + last_slot = self.count - 1 + last_idx = self.slot_to_idx[last_slot] + + self.idx_to_slot[last_idx] = slot + self.slot_to_idx[slot] = last_idx + self.idx_to_slot[i] = MathUtil.INVALID_IDX + self.slot_to_idx[last_slot] = MathUtil.INVALID_IDX + self.count -= 1 + return + + def sample(self, n): + if self.count > 0: + slots = np.random.randint(0, self.count, size=n) + idx = self.slot_to_idx[slots] + else: + idx = np.empty(shape=[0], dtype=int) + return idx + + def check_consistency(self): + valid = True + if self.count < 0: + valid = False + + if valid: + for i in range(self.get_size()): + if self.is_valid(i): + s = self.idx_to_slot[i] + if self.slot_to_idx[s] != i: + valid = False + break + + s2i = self.slot_to_idx[i] + if s2i != MathUtil.INVALID_IDX: + i2s = self.idx_to_slot[s2i] + if i2s != i: valid = False + break - if valid: - for i in range(self.get_size()): - if self.is_valid(i): - s = self.idx_to_slot[i] - if self.slot_to_idx[s] != i: - valid = False - break - - s2i = self.slot_to_idx[i] - if s2i != MathUtil.INVALID_IDX: - i2s = self.idx_to_slot[s2i] - if i2s != i: - valid = False - break - - count0 = np.sum(self.idx_to_slot == MathUtil.INVALID_IDX) - count1 = np.sum(self.slot_to_idx == MathUtil.INVALID_IDX) - valid &= count0 == count1 - return valid + count0 = np.sum(self.idx_to_slot == MathUtil.INVALID_IDX) + count1 = np.sum(self.slot_to_idx == MathUtil.INVALID_IDX) + valid &= count0 == count1 + return valid diff --git a/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/rl_agent.py b/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/rl_agent.py index ce6b2a9f4..fe9a05d85 100644 --- a/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/rl_agent.py +++ b/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/rl_agent.py @@ -6,9 +6,9 @@ import sys from abc import abstractmethod import abc if sys.version_info >= (3, 4): - ABC = abc.ABC + ABC = abc.ABC else: - ABC = abc.ABCMeta('ABC', (), {}) + ABC = abc.ABCMeta('ABC', (), {}) from enum import Enum @@ -20,583 +20,586 @@ from pybullet_utils.logger import Logger import pybullet_utils.mpi_util as MPIUtil import pybullet_utils.math_util as MathUtil + class RLAgent(ABC): - class Mode(Enum): - TRAIN = 0 - TEST = 1 - TRAIN_END = 2 - - NAME = "None" - - UPDATE_PERIOD_KEY = "UpdatePeriod" - ITERS_PER_UPDATE = "ItersPerUpdate" - DISCOUNT_KEY = "Discount" - MINI_BATCH_SIZE_KEY = "MiniBatchSize" - REPLAY_BUFFER_SIZE_KEY = "ReplayBufferSize" - INIT_SAMPLES_KEY = "InitSamples" - NORMALIZER_SAMPLES_KEY = "NormalizerSamples" - - OUTPUT_ITERS_KEY = "OutputIters" - INT_OUTPUT_ITERS_KEY = "IntOutputIters" - TEST_EPISODES_KEY = "TestEpisodes" - - EXP_ANNEAL_SAMPLES_KEY = "ExpAnnealSamples" - EXP_PARAM_BEG_KEY = "ExpParamsBeg" - EXP_PARAM_END_KEY = "ExpParamsEnd" - - def __init__(self, world, id, json_data): - self.world = world - self.id = id - self.logger = Logger() - self._mode = self.Mode.TRAIN - - assert self._check_action_space(), \ - Logger.print2("Invalid action space, got {:s}".format(str(self.get_action_space()))) - - self._enable_training = True - self.path = Path() - self.iter = int(0) - self.start_time = time.time() - self._update_counter = 0 - - self.update_period = 1.0 # simulated time (seconds) before each training update - self.iters_per_update = int(1) - self.discount = 0.95 - self.mini_batch_size = int(32) - self.replay_buffer_size = int(50000) - self.init_samples = int(1000) - self.normalizer_samples = np.inf - self._local_mini_batch_size = self.mini_batch_size # batch size for each work for multiprocessing - self._need_normalizer_update = True - self._total_sample_count = 0 - - self._output_dir = "" - self._int_output_dir = "" - self.output_iters = 100 - self.int_output_iters = 100 - - self.train_return = 0.0 - self.test_episodes = int(0) - self.test_episode_count = int(0) - self.test_return = 0.0 - self.avg_test_return = 0.0 - - self.exp_anneal_samples = 320000 - self.exp_params_beg = ExpParams() - self.exp_params_end = ExpParams() - self.exp_params_curr = ExpParams() - - self._load_params(json_data) - self._build_replay_buffer(self.replay_buffer_size) - self._build_normalizers() - self._build_bounds() - self.reset() - - return - - def __str__(self): - action_space_str = str(self.get_action_space()) - info_str = "" - info_str += '"ID": {:d},\n "Type": "{:s}",\n "ActionSpace": "{:s}",\n "StateDim": {:d},\n "GoalDim": {:d},\n "ActionDim": {:d}'.format( - self.id, self.NAME, action_space_str[action_space_str.rfind('.') + 1:], self.get_state_size(), self.get_goal_size(), self.get_action_size()) - return "{\n" + info_str + "\n}" - - def get_output_dir(self): - return self._output_dir - - def set_output_dir(self, out_dir): - self._output_dir = out_dir - if (self._output_dir != ""): - self.logger.configure_output_file(out_dir + "/agent" + str(self.id) + "_log.txt") - return - - output_dir = property(get_output_dir, set_output_dir) - - def get_int_output_dir(self): - return self._int_output_dir - - def set_int_output_dir(self, out_dir): - self._int_output_dir = out_dir - return - - int_output_dir = property(get_int_output_dir, set_int_output_dir) - - def reset(self): - self.path.clear() - return - - def update(self, timestep): - if self.need_new_action(): - #print("update_new_action!!!") - self._update_new_action() - - - if (self._mode == self.Mode.TRAIN and self.enable_training): - self._update_counter += timestep - - while self._update_counter >= self.update_period: - self._train() - self._update_exp_params() - self.world.env.set_sample_count(self._total_sample_count) - self._update_counter -= self.update_period - - return - - def end_episode(self): - if (self.path.pathlength() > 0): - self._end_path() - - if (self._mode == self.Mode.TRAIN or self._mode == self.Mode.TRAIN_END): - if (self.enable_training and self.path.pathlength() > 0): - self._store_path(self.path) - elif (self._mode == self.Mode.TEST): - self._update_test_return(self.path) - else: - assert False, Logger.print2("Unsupported RL agent mode" + str(self._mode)) - - self._update_mode() - return - - def has_goal(self): - return self.get_goal_size() > 0 - - def predict_val(self): - return 0 - - def get_enable_training(self): - return self._enable_training - - def set_enable_training(self, enable): - print("set_enable_training=", enable) - self._enable_training = enable - if (self._enable_training): - self.reset() - return - - enable_training = property(get_enable_training, set_enable_training) - - def enable_testing(self): - return self.test_episodes > 0 - - def get_name(self): - return self.NAME - - @abstractmethod - def save_model(self, out_path): - pass - - @abstractmethod - def load_model(self, in_path): - pass - - @abstractmethod - def _decide_action(self, s, g): - pass - - @abstractmethod - def _get_output_path(self): - pass - - @abstractmethod - def _get_int_output_path(self): - pass - - @abstractmethod - def _train_step(self): - pass - - @abstractmethod - def _check_action_space(self): - pass - - def get_action_space(self): - return self.world.env.get_action_space(self.id) - - def get_state_size(self): - return self.world.env.get_state_size(self.id) - - def get_goal_size(self): - return self.world.env.get_goal_size(self.id) - - def get_action_size(self): - return self.world.env.get_action_size(self.id) - - def get_num_actions(self): - return self.world.env.get_num_actions(self.id) - - def need_new_action(self): - return self.world.env.need_new_action(self.id) - - def _build_normalizers(self): - self.s_norm = Normalizer(self.get_state_size(), self.world.env.build_state_norm_groups(self.id)) - self.s_norm.set_mean_std(-self.world.env.build_state_offset(self.id), - 1 / self.world.env.build_state_scale(self.id)) - - self.g_norm = Normalizer(self.get_goal_size(), self.world.env.build_goal_norm_groups(self.id)) - self.g_norm.set_mean_std(-self.world.env.build_goal_offset(self.id), - 1 / self.world.env.build_goal_scale(self.id)) - - self.a_norm = Normalizer(self.world.env.get_action_size()) - self.a_norm.set_mean_std(-self.world.env.build_action_offset(self.id), - 1 / self.world.env.build_action_scale(self.id)) - return - - def _build_bounds(self): - self.a_bound_min = self.world.env.build_action_bound_min(self.id) - self.a_bound_max = self.world.env.build_action_bound_max(self.id) - return - - def _load_params(self, json_data): - if (self.UPDATE_PERIOD_KEY in json_data): - self.update_period = int(json_data[self.UPDATE_PERIOD_KEY]) - - if (self.ITERS_PER_UPDATE in json_data): - self.iters_per_update = int(json_data[self.ITERS_PER_UPDATE]) - - if (self.DISCOUNT_KEY in json_data): - self.discount = json_data[self.DISCOUNT_KEY] - - if (self.MINI_BATCH_SIZE_KEY in json_data): - self.mini_batch_size = int(json_data[self.MINI_BATCH_SIZE_KEY]) - - if (self.REPLAY_BUFFER_SIZE_KEY in json_data): - self.replay_buffer_size = int(json_data[self.REPLAY_BUFFER_SIZE_KEY]) - - if (self.INIT_SAMPLES_KEY in json_data): - self.init_samples = int(json_data[self.INIT_SAMPLES_KEY]) - - if (self.NORMALIZER_SAMPLES_KEY in json_data): - self.normalizer_samples = int(json_data[self.NORMALIZER_SAMPLES_KEY]) - - if (self.OUTPUT_ITERS_KEY in json_data): - self.output_iters = json_data[self.OUTPUT_ITERS_KEY] - - if (self.INT_OUTPUT_ITERS_KEY in json_data): - self.int_output_iters = json_data[self.INT_OUTPUT_ITERS_KEY] - - if (self.TEST_EPISODES_KEY in json_data): - self.test_episodes = int(json_data[self.TEST_EPISODES_KEY]) - - if (self.EXP_ANNEAL_SAMPLES_KEY in json_data): - self.exp_anneal_samples = json_data[self.EXP_ANNEAL_SAMPLES_KEY] - - if (self.EXP_PARAM_BEG_KEY in json_data): - self.exp_params_beg.load(json_data[self.EXP_PARAM_BEG_KEY]) - - if (self.EXP_PARAM_END_KEY in json_data): - self.exp_params_end.load(json_data[self.EXP_PARAM_END_KEY]) - - num_procs = MPIUtil.get_num_procs() - self._local_mini_batch_size = int(np.ceil(self.mini_batch_size / num_procs)) - self._local_mini_batch_size = np.maximum(self._local_mini_batch_size, 1) - self.mini_batch_size = self._local_mini_batch_size * num_procs - - assert(self.exp_params_beg.noise == self.exp_params_end.noise) # noise std should not change - self.exp_params_curr = copy.deepcopy(self.exp_params_beg) - self.exp_params_end.noise = self.exp_params_beg.noise - - self._need_normalizer_update = self.normalizer_samples > 0 - - return - - def _record_state(self): - s = self.world.env.record_state(self.id) - return s - - def _record_goal(self): - g = self.world.env.record_goal(self.id) - return g - - def _record_reward(self): - r = self.world.env.calc_reward(self.id) - return r - - def _apply_action(self, a): - self.world.env.set_action(self.id, a) - return - - def _record_flags(self): - return int(0) - - def _is_first_step(self): - return len(self.path.states) == 0 - - def _end_path(self): - s = self._record_state() - g = self._record_goal() - r = self._record_reward() - - self.path.rewards.append(r) - self.path.states.append(s) - self.path.goals.append(g) - self.path.terminate = self.world.env.check_terminate(self.id) - - return - - def _update_new_action(self): - #print("_update_new_action!") - s = self._record_state() - #np.savetxt("pb_record_state_s.csv", s, delimiter=",") - g = self._record_goal() - - if not (self._is_first_step()): - r = self._record_reward() - self.path.rewards.append(r) - - a, logp = self._decide_action(s=s, g=g) - assert len(np.shape(a)) == 1 - assert len(np.shape(logp)) <= 1 - - flags = self._record_flags() - self._apply_action(a) - - self.path.states.append(s) - self.path.goals.append(g) - self.path.actions.append(a) - self.path.logps.append(logp) - self.path.flags.append(flags) - - if self._enable_draw(): - self._log_val(s, g) - - return - - def _update_exp_params(self): - lerp = float(self._total_sample_count) / self.exp_anneal_samples - lerp = np.clip(lerp, 0.0, 1.0) - self.exp_params_curr = self.exp_params_beg.lerp(self.exp_params_end, lerp) - return - - def _update_test_return(self, path): - path_reward = path.calc_return() - self.test_return += path_reward - self.test_episode_count += 1 - return - - def _update_mode(self): - if (self._mode == self.Mode.TRAIN): - self._update_mode_train() - elif (self._mode == self.Mode.TRAIN_END): - self._update_mode_train_end() - elif (self._mode == self.Mode.TEST): - self._update_mode_test() - else: - assert False, Logger.print2("Unsupported RL agent mode" + str(self._mode)) - return - - def _update_mode_train(self): - return - - def _update_mode_train_end(self): - self._init_mode_test() - return - - def _update_mode_test(self): - if (self.test_episode_count * MPIUtil.get_num_procs() >= self.test_episodes): - global_return = MPIUtil.reduce_sum(self.test_return) - global_count = MPIUtil.reduce_sum(self.test_episode_count) - avg_return = global_return / global_count - self.avg_test_return = avg_return - - if self.enable_training: - self._init_mode_train() - return - - def _init_mode_train(self): - self._mode = self.Mode.TRAIN - self.world.env.set_mode(self._mode) - return - - def _init_mode_train_end(self): - self._mode = self.Mode.TRAIN_END - return - - def _init_mode_test(self): - self._mode = self.Mode.TEST - self.test_return = 0.0 - self.test_episode_count = 0 - self.world.env.set_mode(self._mode) - return - - def _enable_output(self): - return MPIUtil.is_root_proc() and self.output_dir != "" - - def _enable_int_output(self): - return MPIUtil.is_root_proc() and self.int_output_dir != "" - - def _calc_val_bounds(self, discount): - r_min = self.world.env.get_reward_min(self.id) - r_max = self.world.env.get_reward_max(self.id) - assert(r_min <= r_max) - - val_min = r_min / ( 1.0 - discount) - val_max = r_max / ( 1.0 - discount) - return val_min, val_max - - def _calc_val_offset_scale(self, discount): - val_min, val_max = self._calc_val_bounds(discount) - val_offset = 0 - val_scale = 1 - - if (np.isfinite(val_min) and np.isfinite(val_max)): - val_offset = -0.5 * (val_max + val_min) - val_scale = 2 / (val_max - val_min) - - return val_offset, val_scale - - def _calc_term_vals(self, discount): - r_fail = self.world.env.get_reward_fail(self.id) - r_succ = self.world.env.get_reward_succ(self.id) - - r_min = self.world.env.get_reward_min(self.id) - r_max = self.world.env.get_reward_max(self.id) - assert(r_fail <= r_max and r_fail >= r_min) - assert(r_succ <= r_max and r_succ >= r_min) - assert(not np.isinf(r_fail)) - assert(not np.isinf(r_succ)) - - if (discount == 0): - val_fail = 0 - val_succ = 0 - else: - val_fail = r_fail / (1.0 - discount) - val_succ = r_succ / (1.0 - discount) - - return val_fail, val_succ - - def _update_iter(self, iter): - if (self._enable_output() and self.iter % self.output_iters == 0): - output_path = self._get_output_path() - output_dir = os.path.dirname(output_path) - if not os.path.exists(output_dir): - os.makedirs(output_dir) - self.save_model(output_path) - - if (self._enable_int_output() and self.iter % self.int_output_iters == 0): - int_output_path = self._get_int_output_path() - int_output_dir = os.path.dirname(int_output_path) - if not os.path.exists(int_output_dir): - os.makedirs(int_output_dir) - self.save_model(int_output_path) - - self.iter = iter - return - - def _enable_draw(self): - return self.world.env.enable_draw - - def _log_val(self, s, g): - pass - - def _build_replay_buffer(self, buffer_size): - num_procs = MPIUtil.get_num_procs() - buffer_size = int(buffer_size / num_procs) - self.replay_buffer = ReplayBuffer(buffer_size=buffer_size) - self.replay_buffer_initialized = False - return - - def _store_path(self, path): - path_id = self.replay_buffer.store(path) - valid_path = path_id != MathUtil.INVALID_IDX - - if valid_path: - self.train_return = path.calc_return() - - if self._need_normalizer_update: - self._record_normalizers(path) - - return path_id - - def _record_normalizers(self, path): - states = np.array(path.states) - self.s_norm.record(states) - - if self.has_goal(): - goals = np.array(path.goals) - self.g_norm.record(goals) - - return - - def _update_normalizers(self): - self.s_norm.update() - - if self.has_goal(): - self.g_norm.update() - return - - def _train(self): - samples = self.replay_buffer.total_count - self._total_sample_count = int(MPIUtil.reduce_sum(samples)) - end_training = False - - if (self.replay_buffer_initialized): - if (self._valid_train_step()): - prev_iter = self.iter - iters = self._get_iters_per_update() - avg_train_return = MPIUtil.reduce_avg(self.train_return) - - for i in range(iters): - curr_iter = self.iter - wall_time = time.time() - self.start_time - wall_time /= 60 * 60 # store time in hours - - has_goal = self.has_goal() - s_mean = np.mean(self.s_norm.mean) - s_std = np.mean(self.s_norm.std) - g_mean = np.mean(self.g_norm.mean) if has_goal else 0 - g_std = np.mean(self.g_norm.std) if has_goal else 0 - - self.logger.log_tabular("Iteration", self.iter) - self.logger.log_tabular("Wall_Time", wall_time) - self.logger.log_tabular("Samples", self._total_sample_count) - self.logger.log_tabular("Train_Return", avg_train_return) - self.logger.log_tabular("Test_Return", self.avg_test_return) - self.logger.log_tabular("State_Mean", s_mean) - self.logger.log_tabular("State_Std", s_std) - self.logger.log_tabular("Goal_Mean", g_mean) - self.logger.log_tabular("Goal_Std", g_std) - self._log_exp_params() - - self._update_iter(self.iter + 1) - self._train_step() - - Logger.print2("Agent " + str(self.id)) - self.logger.print_tabular() - Logger.print2("") - - if (self._enable_output() and curr_iter % self.int_output_iters == 0): - self.logger.dump_tabular() - - if (prev_iter // self.int_output_iters != self.iter // self.int_output_iters): - end_training = self.enable_testing() - - else: - - Logger.print2("Agent " + str(self.id)) - Logger.print2("Samples: " + str(self._total_sample_count)) - Logger.print2("") - - if (self._total_sample_count >= self.init_samples): - self.replay_buffer_initialized = True - end_training = self.enable_testing() - - if self._need_normalizer_update: - self._update_normalizers() - self._need_normalizer_update = self.normalizer_samples > self._total_sample_count - - if end_training: - self._init_mode_train_end() - - return - - def _get_iters_per_update(self): - return MPIUtil.get_num_procs() * self.iters_per_update - - def _valid_train_step(self): - return True - - def _log_exp_params(self): - self.logger.log_tabular("Exp_Rate", self.exp_params_curr.rate) - self.logger.log_tabular("Exp_Noise", self.exp_params_curr.noise) - self.logger.log_tabular("Exp_Temp", self.exp_params_curr.temp) - return + + class Mode(Enum): + TRAIN = 0 + TEST = 1 + TRAIN_END = 2 + + NAME = "None" + + UPDATE_PERIOD_KEY = "UpdatePeriod" + ITERS_PER_UPDATE = "ItersPerUpdate" + DISCOUNT_KEY = "Discount" + MINI_BATCH_SIZE_KEY = "MiniBatchSize" + REPLAY_BUFFER_SIZE_KEY = "ReplayBufferSize" + INIT_SAMPLES_KEY = "InitSamples" + NORMALIZER_SAMPLES_KEY = "NormalizerSamples" + + OUTPUT_ITERS_KEY = "OutputIters" + INT_OUTPUT_ITERS_KEY = "IntOutputIters" + TEST_EPISODES_KEY = "TestEpisodes" + + EXP_ANNEAL_SAMPLES_KEY = "ExpAnnealSamples" + EXP_PARAM_BEG_KEY = "ExpParamsBeg" + EXP_PARAM_END_KEY = "ExpParamsEnd" + + def __init__(self, world, id, json_data): + self.world = world + self.id = id + self.logger = Logger() + self._mode = self.Mode.TRAIN + + assert self._check_action_space(), \ + Logger.print2("Invalid action space, got {:s}".format(str(self.get_action_space()))) + + self._enable_training = True + self.path = Path() + self.iter = int(0) + self.start_time = time.time() + self._update_counter = 0 + + self.update_period = 1.0 # simulated time (seconds) before each training update + self.iters_per_update = int(1) + self.discount = 0.95 + self.mini_batch_size = int(32) + self.replay_buffer_size = int(50000) + self.init_samples = int(1000) + self.normalizer_samples = np.inf + self._local_mini_batch_size = self.mini_batch_size # batch size for each work for multiprocessing + self._need_normalizer_update = True + self._total_sample_count = 0 + + self._output_dir = "" + self._int_output_dir = "" + self.output_iters = 100 + self.int_output_iters = 100 + + self.train_return = 0.0 + self.test_episodes = int(0) + self.test_episode_count = int(0) + self.test_return = 0.0 + self.avg_test_return = 0.0 + + self.exp_anneal_samples = 320000 + self.exp_params_beg = ExpParams() + self.exp_params_end = ExpParams() + self.exp_params_curr = ExpParams() + + self._load_params(json_data) + self._build_replay_buffer(self.replay_buffer_size) + self._build_normalizers() + self._build_bounds() + self.reset() + + return + + def __str__(self): + action_space_str = str(self.get_action_space()) + info_str = "" + info_str += '"ID": {:d},\n "Type": "{:s}",\n "ActionSpace": "{:s}",\n "StateDim": {:d},\n "GoalDim": {:d},\n "ActionDim": {:d}'.format( + self.id, self.NAME, action_space_str[action_space_str.rfind('.') + 1:], + self.get_state_size(), self.get_goal_size(), self.get_action_size()) + return "{\n" + info_str + "\n}" + + def get_output_dir(self): + return self._output_dir + + def set_output_dir(self, out_dir): + self._output_dir = out_dir + if (self._output_dir != ""): + self.logger.configure_output_file(out_dir + "/agent" + str(self.id) + "_log.txt") + return + + output_dir = property(get_output_dir, set_output_dir) + + def get_int_output_dir(self): + return self._int_output_dir + + def set_int_output_dir(self, out_dir): + self._int_output_dir = out_dir + return + + int_output_dir = property(get_int_output_dir, set_int_output_dir) + + def reset(self): + self.path.clear() + return + + def update(self, timestep): + if self.need_new_action(): + #print("update_new_action!!!") + self._update_new_action() + + if (self._mode == self.Mode.TRAIN and self.enable_training): + self._update_counter += timestep + + while self._update_counter >= self.update_period: + self._train() + self._update_exp_params() + self.world.env.set_sample_count(self._total_sample_count) + self._update_counter -= self.update_period + + return + + def end_episode(self): + if (self.path.pathlength() > 0): + self._end_path() + + if (self._mode == self.Mode.TRAIN or self._mode == self.Mode.TRAIN_END): + if (self.enable_training and self.path.pathlength() > 0): + self._store_path(self.path) + elif (self._mode == self.Mode.TEST): + self._update_test_return(self.path) + else: + assert False, Logger.print2("Unsupported RL agent mode" + str(self._mode)) + + self._update_mode() + return + + def has_goal(self): + return self.get_goal_size() > 0 + + def predict_val(self): + return 0 + + def get_enable_training(self): + return self._enable_training + + def set_enable_training(self, enable): + print("set_enable_training=", enable) + self._enable_training = enable + if (self._enable_training): + self.reset() + return + + enable_training = property(get_enable_training, set_enable_training) + + def enable_testing(self): + return self.test_episodes > 0 + + def get_name(self): + return self.NAME + + @abstractmethod + def save_model(self, out_path): + pass + + @abstractmethod + def load_model(self, in_path): + pass + + @abstractmethod + def _decide_action(self, s, g): + pass + + @abstractmethod + def _get_output_path(self): + pass + + @abstractmethod + def _get_int_output_path(self): + pass + + @abstractmethod + def _train_step(self): + pass + + @abstractmethod + def _check_action_space(self): + pass + + def get_action_space(self): + return self.world.env.get_action_space(self.id) + + def get_state_size(self): + return self.world.env.get_state_size(self.id) + + def get_goal_size(self): + return self.world.env.get_goal_size(self.id) + + def get_action_size(self): + return self.world.env.get_action_size(self.id) + + def get_num_actions(self): + return self.world.env.get_num_actions(self.id) + + def need_new_action(self): + return self.world.env.need_new_action(self.id) + + def _build_normalizers(self): + self.s_norm = Normalizer(self.get_state_size(), + self.world.env.build_state_norm_groups(self.id)) + self.s_norm.set_mean_std(-self.world.env.build_state_offset(self.id), + 1 / self.world.env.build_state_scale(self.id)) + + self.g_norm = Normalizer(self.get_goal_size(), self.world.env.build_goal_norm_groups(self.id)) + self.g_norm.set_mean_std(-self.world.env.build_goal_offset(self.id), + 1 / self.world.env.build_goal_scale(self.id)) + + self.a_norm = Normalizer(self.world.env.get_action_size()) + self.a_norm.set_mean_std(-self.world.env.build_action_offset(self.id), + 1 / self.world.env.build_action_scale(self.id)) + return + + def _build_bounds(self): + self.a_bound_min = self.world.env.build_action_bound_min(self.id) + self.a_bound_max = self.world.env.build_action_bound_max(self.id) + return + + def _load_params(self, json_data): + if (self.UPDATE_PERIOD_KEY in json_data): + self.update_period = int(json_data[self.UPDATE_PERIOD_KEY]) + + if (self.ITERS_PER_UPDATE in json_data): + self.iters_per_update = int(json_data[self.ITERS_PER_UPDATE]) + + if (self.DISCOUNT_KEY in json_data): + self.discount = json_data[self.DISCOUNT_KEY] + + if (self.MINI_BATCH_SIZE_KEY in json_data): + self.mini_batch_size = int(json_data[self.MINI_BATCH_SIZE_KEY]) + + if (self.REPLAY_BUFFER_SIZE_KEY in json_data): + self.replay_buffer_size = int(json_data[self.REPLAY_BUFFER_SIZE_KEY]) + + if (self.INIT_SAMPLES_KEY in json_data): + self.init_samples = int(json_data[self.INIT_SAMPLES_KEY]) + + if (self.NORMALIZER_SAMPLES_KEY in json_data): + self.normalizer_samples = int(json_data[self.NORMALIZER_SAMPLES_KEY]) + + if (self.OUTPUT_ITERS_KEY in json_data): + self.output_iters = json_data[self.OUTPUT_ITERS_KEY] + + if (self.INT_OUTPUT_ITERS_KEY in json_data): + self.int_output_iters = json_data[self.INT_OUTPUT_ITERS_KEY] + + if (self.TEST_EPISODES_KEY in json_data): + self.test_episodes = int(json_data[self.TEST_EPISODES_KEY]) + + if (self.EXP_ANNEAL_SAMPLES_KEY in json_data): + self.exp_anneal_samples = json_data[self.EXP_ANNEAL_SAMPLES_KEY] + + if (self.EXP_PARAM_BEG_KEY in json_data): + self.exp_params_beg.load(json_data[self.EXP_PARAM_BEG_KEY]) + + if (self.EXP_PARAM_END_KEY in json_data): + self.exp_params_end.load(json_data[self.EXP_PARAM_END_KEY]) + + num_procs = MPIUtil.get_num_procs() + self._local_mini_batch_size = int(np.ceil(self.mini_batch_size / num_procs)) + self._local_mini_batch_size = np.maximum(self._local_mini_batch_size, 1) + self.mini_batch_size = self._local_mini_batch_size * num_procs + + assert (self.exp_params_beg.noise == self.exp_params_end.noise) # noise std should not change + self.exp_params_curr = copy.deepcopy(self.exp_params_beg) + self.exp_params_end.noise = self.exp_params_beg.noise + + self._need_normalizer_update = self.normalizer_samples > 0 + + return + + def _record_state(self): + s = self.world.env.record_state(self.id) + return s + + def _record_goal(self): + g = self.world.env.record_goal(self.id) + return g + + def _record_reward(self): + r = self.world.env.calc_reward(self.id) + return r + + def _apply_action(self, a): + self.world.env.set_action(self.id, a) + return + + def _record_flags(self): + return int(0) + + def _is_first_step(self): + return len(self.path.states) == 0 + + def _end_path(self): + s = self._record_state() + g = self._record_goal() + r = self._record_reward() + + self.path.rewards.append(r) + self.path.states.append(s) + self.path.goals.append(g) + self.path.terminate = self.world.env.check_terminate(self.id) + + return + + def _update_new_action(self): + #print("_update_new_action!") + s = self._record_state() + #np.savetxt("pb_record_state_s.csv", s, delimiter=",") + g = self._record_goal() + + if not (self._is_first_step()): + r = self._record_reward() + self.path.rewards.append(r) + + a, logp = self._decide_action(s=s, g=g) + assert len(np.shape(a)) == 1 + assert len(np.shape(logp)) <= 1 + + flags = self._record_flags() + self._apply_action(a) + + self.path.states.append(s) + self.path.goals.append(g) + self.path.actions.append(a) + self.path.logps.append(logp) + self.path.flags.append(flags) + + if self._enable_draw(): + self._log_val(s, g) + + return + + def _update_exp_params(self): + lerp = float(self._total_sample_count) / self.exp_anneal_samples + lerp = np.clip(lerp, 0.0, 1.0) + self.exp_params_curr = self.exp_params_beg.lerp(self.exp_params_end, lerp) + return + + def _update_test_return(self, path): + path_reward = path.calc_return() + self.test_return += path_reward + self.test_episode_count += 1 + return + + def _update_mode(self): + if (self._mode == self.Mode.TRAIN): + self._update_mode_train() + elif (self._mode == self.Mode.TRAIN_END): + self._update_mode_train_end() + elif (self._mode == self.Mode.TEST): + self._update_mode_test() + else: + assert False, Logger.print2("Unsupported RL agent mode" + str(self._mode)) + return + + def _update_mode_train(self): + return + + def _update_mode_train_end(self): + self._init_mode_test() + return + + def _update_mode_test(self): + if (self.test_episode_count * MPIUtil.get_num_procs() >= self.test_episodes): + global_return = MPIUtil.reduce_sum(self.test_return) + global_count = MPIUtil.reduce_sum(self.test_episode_count) + avg_return = global_return / global_count + self.avg_test_return = avg_return + + if self.enable_training: + self._init_mode_train() + return + + def _init_mode_train(self): + self._mode = self.Mode.TRAIN + self.world.env.set_mode(self._mode) + return + + def _init_mode_train_end(self): + self._mode = self.Mode.TRAIN_END + return + + def _init_mode_test(self): + self._mode = self.Mode.TEST + self.test_return = 0.0 + self.test_episode_count = 0 + self.world.env.set_mode(self._mode) + return + + def _enable_output(self): + return MPIUtil.is_root_proc() and self.output_dir != "" + + def _enable_int_output(self): + return MPIUtil.is_root_proc() and self.int_output_dir != "" + + def _calc_val_bounds(self, discount): + r_min = self.world.env.get_reward_min(self.id) + r_max = self.world.env.get_reward_max(self.id) + assert (r_min <= r_max) + + val_min = r_min / (1.0 - discount) + val_max = r_max / (1.0 - discount) + return val_min, val_max + + def _calc_val_offset_scale(self, discount): + val_min, val_max = self._calc_val_bounds(discount) + val_offset = 0 + val_scale = 1 + + if (np.isfinite(val_min) and np.isfinite(val_max)): + val_offset = -0.5 * (val_max + val_min) + val_scale = 2 / (val_max - val_min) + + return val_offset, val_scale + + def _calc_term_vals(self, discount): + r_fail = self.world.env.get_reward_fail(self.id) + r_succ = self.world.env.get_reward_succ(self.id) + + r_min = self.world.env.get_reward_min(self.id) + r_max = self.world.env.get_reward_max(self.id) + assert (r_fail <= r_max and r_fail >= r_min) + assert (r_succ <= r_max and r_succ >= r_min) + assert (not np.isinf(r_fail)) + assert (not np.isinf(r_succ)) + + if (discount == 0): + val_fail = 0 + val_succ = 0 + else: + val_fail = r_fail / (1.0 - discount) + val_succ = r_succ / (1.0 - discount) + + return val_fail, val_succ + + def _update_iter(self, iter): + if (self._enable_output() and self.iter % self.output_iters == 0): + output_path = self._get_output_path() + output_dir = os.path.dirname(output_path) + if not os.path.exists(output_dir): + os.makedirs(output_dir) + self.save_model(output_path) + + if (self._enable_int_output() and self.iter % self.int_output_iters == 0): + int_output_path = self._get_int_output_path() + int_output_dir = os.path.dirname(int_output_path) + if not os.path.exists(int_output_dir): + os.makedirs(int_output_dir) + self.save_model(int_output_path) + + self.iter = iter + return + + def _enable_draw(self): + return self.world.env.enable_draw + + def _log_val(self, s, g): + pass + + def _build_replay_buffer(self, buffer_size): + num_procs = MPIUtil.get_num_procs() + buffer_size = int(buffer_size / num_procs) + self.replay_buffer = ReplayBuffer(buffer_size=buffer_size) + self.replay_buffer_initialized = False + return + + def _store_path(self, path): + path_id = self.replay_buffer.store(path) + valid_path = path_id != MathUtil.INVALID_IDX + + if valid_path: + self.train_return = path.calc_return() + + if self._need_normalizer_update: + self._record_normalizers(path) + + return path_id + + def _record_normalizers(self, path): + states = np.array(path.states) + self.s_norm.record(states) + + if self.has_goal(): + goals = np.array(path.goals) + self.g_norm.record(goals) + + return + + def _update_normalizers(self): + self.s_norm.update() + + if self.has_goal(): + self.g_norm.update() + return + + def _train(self): + samples = self.replay_buffer.total_count + self._total_sample_count = int(MPIUtil.reduce_sum(samples)) + end_training = False + + if (self.replay_buffer_initialized): + if (self._valid_train_step()): + prev_iter = self.iter + iters = self._get_iters_per_update() + avg_train_return = MPIUtil.reduce_avg(self.train_return) + + for i in range(iters): + curr_iter = self.iter + wall_time = time.time() - self.start_time + wall_time /= 60 * 60 # store time in hours + + has_goal = self.has_goal() + s_mean = np.mean(self.s_norm.mean) + s_std = np.mean(self.s_norm.std) + g_mean = np.mean(self.g_norm.mean) if has_goal else 0 + g_std = np.mean(self.g_norm.std) if has_goal else 0 + + self.logger.log_tabular("Iteration", self.iter) + self.logger.log_tabular("Wall_Time", wall_time) + self.logger.log_tabular("Samples", self._total_sample_count) + self.logger.log_tabular("Train_Return", avg_train_return) + self.logger.log_tabular("Test_Return", self.avg_test_return) + self.logger.log_tabular("State_Mean", s_mean) + self.logger.log_tabular("State_Std", s_std) + self.logger.log_tabular("Goal_Mean", g_mean) + self.logger.log_tabular("Goal_Std", g_std) + self._log_exp_params() + + self._update_iter(self.iter + 1) + self._train_step() + + Logger.print2("Agent " + str(self.id)) + self.logger.print_tabular() + Logger.print2("") + + if (self._enable_output() and curr_iter % self.int_output_iters == 0): + self.logger.dump_tabular() + + if (prev_iter // self.int_output_iters != self.iter // self.int_output_iters): + end_training = self.enable_testing() + + else: + + Logger.print2("Agent " + str(self.id)) + Logger.print2("Samples: " + str(self._total_sample_count)) + Logger.print2("") + + if (self._total_sample_count >= self.init_samples): + self.replay_buffer_initialized = True + end_training = self.enable_testing() + + if self._need_normalizer_update: + self._update_normalizers() + self._need_normalizer_update = self.normalizer_samples > self._total_sample_count + + if end_training: + self._init_mode_train_end() + + return + + def _get_iters_per_update(self): + return MPIUtil.get_num_procs() * self.iters_per_update + + def _valid_train_step(self): + return True + + def _log_exp_params(self): + self.logger.log_tabular("Exp_Rate", self.exp_params_curr.rate) + self.logger.log_tabular("Exp_Noise", self.exp_params_curr.noise) + self.logger.log_tabular("Exp_Temp", self.exp_params_curr.temp) + return diff --git a/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/rl_util.py b/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/rl_util.py index 12c682a19..71132e15d 100644 --- a/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/rl_util.py +++ b/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/rl_util.py @@ -1,18 +1,19 @@ import numpy as np + def compute_return(rewards, gamma, td_lambda, val_t): - # computes td-lambda return of path - path_len = len(rewards) - assert len(val_t) == path_len + 1 + # computes td-lambda return of path + path_len = len(rewards) + assert len(val_t) == path_len + 1 + + return_t = np.zeros(path_len) + last_val = rewards[-1] + gamma * val_t[-1] + return_t[-1] = last_val - return_t = np.zeros(path_len) - last_val = rewards[-1] + gamma * val_t[-1] - return_t[-1] = last_val + for i in reversed(range(0, path_len - 1)): + curr_r = rewards[i] + next_ret = return_t[i + 1] + curr_val = curr_r + gamma * ((1.0 - td_lambda) * val_t[i + 1] + td_lambda * next_ret) + return_t[i] = curr_val - for i in reversed(range(0, path_len - 1)): - curr_r = rewards[i] - next_ret = return_t[i + 1] - curr_val = curr_r + gamma * ((1.0 - td_lambda) * val_t[i + 1] + td_lambda * next_ret) - return_t[i] = curr_val - - return return_t
\ No newline at end of file + return return_t diff --git a/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/rl_world.py b/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/rl_world.py index a1761bc30..32841eade 100644 --- a/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/rl_world.py +++ b/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/rl_world.py @@ -5,139 +5,140 @@ from pybullet_envs.deep_mimic.learning.rl_agent import RLAgent from pybullet_utils.logger import Logger import pybullet_data + class RLWorld(object): - def __init__(self, env, arg_parser): - TFUtil.disable_gpu() - - self.env = env - self.arg_parser = arg_parser - self._enable_training = True - self.train_agents = [] - self.parse_args(arg_parser) - - self.build_agents() - - return - - def get_enable_training(self): - return self._enable_training - - def set_enable_training(self, enable): - self._enable_training = enable - for i in range(len(self.agents)): - curr_agent = self.agents[i] - if curr_agent is not None: - enable_curr_train = self.train_agents[i] if (len(self.train_agents) > 0) else True - curr_agent.enable_training = self.enable_training and enable_curr_train - - if (self._enable_training): - self.env.set_mode(RLAgent.Mode.TRAIN) - else: - self.env.set_mode(RLAgent.Mode.TEST) - - return - - enable_training = property(get_enable_training, set_enable_training) - - def parse_args(self, arg_parser): - self.train_agents = self.arg_parser.parse_bools('train_agents') - num_agents = self.env.get_num_agents() - assert(len(self.train_agents) == num_agents or len(self.train_agents) == 0) - - return - - def shutdown(self): - self.env.shutdown() - return - - def build_agents(self): - num_agents = self.env.get_num_agents() - print("num_agents=",num_agents) - self.agents = [] - - Logger.print2('') - Logger.print2('Num Agents: {:d}'.format(num_agents)) - - agent_files = self.arg_parser.parse_strings('agent_files') - print("len(agent_files)=",len(agent_files)) - assert(len(agent_files) == num_agents or len(agent_files) == 0) - - model_files = self.arg_parser.parse_strings('model_files') - assert(len(model_files) == num_agents or len(model_files) == 0) - - output_path = self.arg_parser.parse_string('output_path') - int_output_path = self.arg_parser.parse_string('int_output_path') - - for i in range(num_agents): - curr_file = agent_files[i] - curr_agent = self._build_agent(i, curr_file) - - if curr_agent is not None: - curr_agent.output_dir = output_path - curr_agent.int_output_dir = int_output_path - Logger.print2(str(curr_agent)) - - if (len(model_files) > 0): - curr_model_file = model_files[i] - if curr_model_file != 'none': - curr_agent.load_model(pybullet_data.getDataPath()+"/"+curr_model_file) - - self.agents.append(curr_agent) - Logger.print2('') - - self.set_enable_training(self.enable_training) - - return - - def update(self, timestep): - #print("world update!\n") - self._update_agents(timestep) - self._update_env(timestep) - return - - def reset(self): - self._reset_agents() - self._reset_env() - return - - def end_episode(self): - self._end_episode_agents(); - return - - def _update_env(self, timestep): - self.env.update(timestep) - return - - def _update_agents(self, timestep): - #print("len(agents)=",len(self.agents)) - for agent in self.agents: - if (agent is not None): - agent.update(timestep) - return - - def _reset_env(self): - self.env.reset() - return - - def _reset_agents(self): - for agent in self.agents: - if (agent != None): - agent.reset() - return - - def _end_episode_agents(self): - for agent in self.agents: - if (agent != None): - agent.end_episode() - return - - def _build_agent(self, id, agent_file): - Logger.print2('Agent {:d}: {}'.format(id, agent_file)) - if (agent_file == 'none'): - agent = None - else: - agent = AgentBuilder.build_agent(self, id, agent_file) - assert (agent != None), 'Failed to build agent {:d} from: {}'.format(id, agent_file) - - return agent - + + def __init__(self, env, arg_parser): + TFUtil.disable_gpu() + + self.env = env + self.arg_parser = arg_parser + self._enable_training = True + self.train_agents = [] + self.parse_args(arg_parser) + + self.build_agents() + + return + + def get_enable_training(self): + return self._enable_training + + def set_enable_training(self, enable): + self._enable_training = enable + for i in range(len(self.agents)): + curr_agent = self.agents[i] + if curr_agent is not None: + enable_curr_train = self.train_agents[i] if (len(self.train_agents) > 0) else True + curr_agent.enable_training = self.enable_training and enable_curr_train + + if (self._enable_training): + self.env.set_mode(RLAgent.Mode.TRAIN) + else: + self.env.set_mode(RLAgent.Mode.TEST) + + return + + enable_training = property(get_enable_training, set_enable_training) + + def parse_args(self, arg_parser): + self.train_agents = self.arg_parser.parse_bools('train_agents') + num_agents = self.env.get_num_agents() + assert (len(self.train_agents) == num_agents or len(self.train_agents) == 0) + + return + + def shutdown(self): + self.env.shutdown() + return + + def build_agents(self): + num_agents = self.env.get_num_agents() + print("num_agents=", num_agents) + self.agents = [] + + Logger.print2('') + Logger.print2('Num Agents: {:d}'.format(num_agents)) + + agent_files = self.arg_parser.parse_strings('agent_files') + print("len(agent_files)=", len(agent_files)) + assert (len(agent_files) == num_agents or len(agent_files) == 0) + + model_files = self.arg_parser.parse_strings('model_files') + assert (len(model_files) == num_agents or len(model_files) == 0) + + output_path = self.arg_parser.parse_string('output_path') + int_output_path = self.arg_parser.parse_string('int_output_path') + + for i in range(num_agents): + curr_file = agent_files[i] + curr_agent = self._build_agent(i, curr_file) + + if curr_agent is not None: + curr_agent.output_dir = output_path + curr_agent.int_output_dir = int_output_path + Logger.print2(str(curr_agent)) + + if (len(model_files) > 0): + curr_model_file = model_files[i] + if curr_model_file != 'none': + curr_agent.load_model(pybullet_data.getDataPath() + "/" + curr_model_file) + + self.agents.append(curr_agent) + Logger.print2('') + + self.set_enable_training(self.enable_training) + + return + + def update(self, timestep): + #print("world update!\n") + self._update_agents(timestep) + self._update_env(timestep) + return + + def reset(self): + self._reset_agents() + self._reset_env() + return + + def end_episode(self): + self._end_episode_agents() + return + + def _update_env(self, timestep): + self.env.update(timestep) + return + + def _update_agents(self, timestep): + #print("len(agents)=",len(self.agents)) + for agent in self.agents: + if (agent is not None): + agent.update(timestep) + return + + def _reset_env(self): + self.env.reset() + return + + def _reset_agents(self): + for agent in self.agents: + if (agent != None): + agent.reset() + return + + def _end_episode_agents(self): + for agent in self.agents: + if (agent != None): + agent.end_episode() + return + + def _build_agent(self, id, agent_file): + Logger.print2('Agent {:d}: {}'.format(id, agent_file)) + if (agent_file == 'none'): + agent = None + else: + agent = AgentBuilder.build_agent(self, id, agent_file) + assert (agent != None), 'Failed to build agent {:d} from: {}'.format(id, agent_file) + + return agent diff --git a/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/solvers/mpi_solver.py b/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/solvers/mpi_solver.py index f2d18051c..0077f4b11 100644 --- a/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/solvers/mpi_solver.py +++ b/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/solvers/mpi_solver.py @@ -8,96 +8,97 @@ from pybullet_utils.logger import Logger from pybullet_envs.deep_mimic.learning.solvers.solver import Solver + class MPISolver(Solver): - CHECK_SYNC_ITERS = 1000 - - def __init__(self, sess, optimizer, vars): - super().__init__(vars) - self.sess = sess - self.optimizer = optimizer - self._build_grad_feed(vars) - self._update = optimizer.apply_gradients(zip(self._grad_tf_list, self.vars)) - self._set_flat_vars = TFUtil.SetFromFlat(sess, self.vars) - self._get_flat_vars = TFUtil.GetFlat(sess, self.vars) - - self.iter = 0 - grad_dim = self._calc_grad_dim() - self._flat_grad = np.zeros(grad_dim, dtype=np.float32) - self._global_flat_grad = np.zeros(grad_dim, dtype=np.float32) - - return - - def get_stepsize(self): - return self.optimizer._learning_rate_tensor.eval() - - def update(self, grads=None, grad_scale=1.0): - if grads is not None: - self._flat_grad = MathUtil.flatten(grads) - else: - self._flat_grad.fill(0) - return self.update_flatgrad(self._flat_grad, grad_scale) - - def update_flatgrad(self, flat_grad, grad_scale=1.0): - if self.iter % self.CHECK_SYNC_ITERS == 0: - assert self.check_synced(), Logger.print2('Network parameters desynchronized') - - if grad_scale != 1.0: - flat_grad *= grad_scale - - MPI.COMM_WORLD.Allreduce(flat_grad, self._global_flat_grad, op=MPI.SUM) - self._global_flat_grad /= MPIUtil.get_num_procs() - - self._load_flat_grad(self._global_flat_grad) - self.sess.run([self._update], self._grad_feed) - self.iter += 1 - - return - - def sync(self): - vars = self._get_flat_vars() - MPIUtil.bcast(vars) - self._set_flat_vars(vars) - return - - def check_synced(self): - synced = True - if self._is_root(): - vars = self._get_flat_vars() - MPIUtil.bcast(vars) - else: - vars_local = self._get_flat_vars() - vars_root = np.empty_like(vars_local) - MPIUtil.bcast(vars_root) - synced = (vars_local == vars_root).all() - return synced - - def _is_root(self): - return MPIUtil.is_root_proc() - - def _build_grad_feed(self, vars): - self._grad_tf_list = [] - self._grad_buffers = [] - for v in self.vars: - shape = v.get_shape() - grad = np.zeros(shape) - grad_tf = tf.placeholder(tf.float32, shape=shape) - self._grad_buffers.append(grad) - self._grad_tf_list.append(grad_tf) - - self._grad_feed = dict({g_tf: g for g_tf, g in zip(self._grad_tf_list, self._grad_buffers)}) - - return - - def _calc_grad_dim(self): - grad_dim = 0 - for grad in self._grad_buffers: - grad_dim += grad.size - return grad_dim - - def _load_flat_grad(self, flat_grad): - start = 0 - for g in self._grad_buffers: - size = g.size - np.copyto(g, np.reshape(flat_grad[start:start + size], g.shape)) - start += size - return
\ No newline at end of file + CHECK_SYNC_ITERS = 1000 + + def __init__(self, sess, optimizer, vars): + super().__init__(vars) + self.sess = sess + self.optimizer = optimizer + self._build_grad_feed(vars) + self._update = optimizer.apply_gradients(zip(self._grad_tf_list, self.vars)) + self._set_flat_vars = TFUtil.SetFromFlat(sess, self.vars) + self._get_flat_vars = TFUtil.GetFlat(sess, self.vars) + + self.iter = 0 + grad_dim = self._calc_grad_dim() + self._flat_grad = np.zeros(grad_dim, dtype=np.float32) + self._global_flat_grad = np.zeros(grad_dim, dtype=np.float32) + + return + + def get_stepsize(self): + return self.optimizer._learning_rate_tensor.eval() + + def update(self, grads=None, grad_scale=1.0): + if grads is not None: + self._flat_grad = MathUtil.flatten(grads) + else: + self._flat_grad.fill(0) + return self.update_flatgrad(self._flat_grad, grad_scale) + + def update_flatgrad(self, flat_grad, grad_scale=1.0): + if self.iter % self.CHECK_SYNC_ITERS == 0: + assert self.check_synced(), Logger.print2('Network parameters desynchronized') + + if grad_scale != 1.0: + flat_grad *= grad_scale + + MPI.COMM_WORLD.Allreduce(flat_grad, self._global_flat_grad, op=MPI.SUM) + self._global_flat_grad /= MPIUtil.get_num_procs() + + self._load_flat_grad(self._global_flat_grad) + self.sess.run([self._update], self._grad_feed) + self.iter += 1 + + return + + def sync(self): + vars = self._get_flat_vars() + MPIUtil.bcast(vars) + self._set_flat_vars(vars) + return + + def check_synced(self): + synced = True + if self._is_root(): + vars = self._get_flat_vars() + MPIUtil.bcast(vars) + else: + vars_local = self._get_flat_vars() + vars_root = np.empty_like(vars_local) + MPIUtil.bcast(vars_root) + synced = (vars_local == vars_root).all() + return synced + + def _is_root(self): + return MPIUtil.is_root_proc() + + def _build_grad_feed(self, vars): + self._grad_tf_list = [] + self._grad_buffers = [] + for v in self.vars: + shape = v.get_shape() + grad = np.zeros(shape) + grad_tf = tf.placeholder(tf.float32, shape=shape) + self._grad_buffers.append(grad) + self._grad_tf_list.append(grad_tf) + + self._grad_feed = dict({g_tf: g for g_tf, g in zip(self._grad_tf_list, self._grad_buffers)}) + + return + + def _calc_grad_dim(self): + grad_dim = 0 + for grad in self._grad_buffers: + grad_dim += grad.size + return grad_dim + + def _load_flat_grad(self, flat_grad): + start = 0 + for g in self._grad_buffers: + size = g.size + np.copyto(g, np.reshape(flat_grad[start:start + size], g.shape)) + start += size + return diff --git a/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/solvers/solver.py b/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/solvers/solver.py index cd2765272..3491b40ed 100644 --- a/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/solvers/solver.py +++ b/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/solvers/solver.py @@ -1,15 +1,17 @@ from abc import abstractmethod import sys, abc if sys.version_info >= (3, 4): - ABC = abc.ABC + ABC = abc.ABC else: - ABC = abc.ABCMeta('ABC', (), {}) + ABC = abc.ABCMeta('ABC', (), {}) + class Solver(ABC): - def __init__(self, vars): - self.vars = vars - return - @abstractmethod - def update(self, grads): - pass + def __init__(self, vars): + self.vars = vars + return + + @abstractmethod + def update(self, grads): + pass diff --git a/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/tf_agent.py b/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/tf_agent.py index 95d707140..1770e5329 100644 --- a/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/tf_agent.py +++ b/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/tf_agent.py @@ -6,144 +6,148 @@ from pybullet_envs.deep_mimic.learning.rl_agent import RLAgent from pybullet_utils.logger import Logger from pybullet_envs.deep_mimic.learning.tf_normalizer import TFNormalizer + class TFAgent(RLAgent): - RESOURCE_SCOPE = 'resource' - SOLVER_SCOPE = 'solvers' - - def __init__(self, world, id, json_data): - self.tf_scope = 'agent' - self.graph = tf.Graph() - self.sess = tf.Session(graph=self.graph) - - super().__init__(world, id, json_data) - self._build_graph(json_data) - self._init_normalizers() - return - - def __del__(self): - self.sess.close() - return - - def save_model(self, out_path): - with self.sess.as_default(), self.graph.as_default(): - try: - save_path = self.saver.save(self.sess, out_path, write_meta_graph=False, write_state=False) - Logger.print2('Model saved to: ' + save_path) - except: - Logger.print2("Failed to save model to: " + save_path) - return - - def load_model(self, in_path): - with self.sess.as_default(), self.graph.as_default(): - self.saver.restore(self.sess, in_path) - self._load_normalizers() - Logger.print2('Model loaded from: ' + in_path) - return - - def _get_output_path(self): - assert(self.output_dir != '') - file_path = self.output_dir + '/agent' + str(self.id) + '_model.ckpt' - return file_path - - def _get_int_output_path(self): - assert(self.int_output_dir != '') - file_path = self.int_output_dir + ('/agent{:d}_models/agent{:d}_int_model_{:010d}.ckpt').format(self.id, self.id, self.iter) - return file_path - - def _build_graph(self, json_data): - with self.sess.as_default(), self.graph.as_default(): - with tf.variable_scope(self.tf_scope): - self._build_nets(json_data) - - with tf.variable_scope(self.SOLVER_SCOPE): - self._build_losses(json_data) - self._build_solvers(json_data) - - self._initialize_vars() - self._build_saver() - return - - def _init_normalizers(self): - with self.sess.as_default(), self.graph.as_default(): - # update normalizers to sync the tensorflow tensors - self.s_norm.update() - self.g_norm.update() - self.a_norm.update() - return - - @abstractmethod - def _build_nets(self, json_data): - pass - - @abstractmethod - def _build_losses(self, json_data): - pass - - @abstractmethod - def _build_solvers(self, json_data): - pass - - def _tf_vars(self, scope=''): - with self.sess.as_default(), self.graph.as_default(): - res = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope=self.tf_scope + '/' + scope) - assert len(res) > 0 - return res - - def _build_normalizers(self): - with self.sess.as_default(), self.graph.as_default(), tf.variable_scope(self.tf_scope): - with tf.variable_scope(self.RESOURCE_SCOPE): - self.s_norm = TFNormalizer(self.sess, 's_norm', self.get_state_size(), self.world.env.build_state_norm_groups(self.id)) - state_offset = -self.world.env.build_state_offset(self.id) - print("state_offset=",state_offset) - state_scale = 1 / self.world.env.build_state_scale(self.id) - print("state_scale=",state_scale) - self.s_norm.set_mean_std(-self.world.env.build_state_offset(self.id), - 1 / self.world.env.build_state_scale(self.id)) - - self.g_norm = TFNormalizer(self.sess, 'g_norm', self.get_goal_size(), self.world.env.build_goal_norm_groups(self.id)) - self.g_norm.set_mean_std(-self.world.env.build_goal_offset(self.id), - 1 / self.world.env.build_goal_scale(self.id)) - - self.a_norm = TFNormalizer(self.sess, 'a_norm', self.get_action_size()) - self.a_norm.set_mean_std(-self.world.env.build_action_offset(self.id), - 1 / self.world.env.build_action_scale(self.id)) - return - - def _load_normalizers(self): - self.s_norm.load() - self.g_norm.load() - self.a_norm.load() - return - - def _update_normalizers(self): - with self.sess.as_default(), self.graph.as_default(): - super()._update_normalizers() - return - - def _initialize_vars(self): - self.sess.run(tf.global_variables_initializer()) - return - - def _build_saver(self): - vars = self._get_saver_vars() - self.saver = tf.train.Saver(vars, max_to_keep=0) - return - - def _get_saver_vars(self): - with self.sess.as_default(), self.graph.as_default(): - vars = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope=self.tf_scope) - vars = [v for v in vars if '/' + self.SOLVER_SCOPE + '/' not in v.name] - #vars = [v for v in vars if '/target/' not in v.name] - assert len(vars) > 0 - return vars - - def _weight_decay_loss(self, scope): - vars = self._tf_vars(scope) - vars_no_bias = [v for v in vars if 'bias' not in v.name] - loss = tf.add_n([tf.nn.l2_loss(v) for v in vars_no_bias]) - return loss - - def _train(self): - with self.sess.as_default(), self.graph.as_default(): - super()._train() - return
\ No newline at end of file + RESOURCE_SCOPE = 'resource' + SOLVER_SCOPE = 'solvers' + + def __init__(self, world, id, json_data): + self.tf_scope = 'agent' + self.graph = tf.Graph() + self.sess = tf.Session(graph=self.graph) + + super().__init__(world, id, json_data) + self._build_graph(json_data) + self._init_normalizers() + return + + def __del__(self): + self.sess.close() + return + + def save_model(self, out_path): + with self.sess.as_default(), self.graph.as_default(): + try: + save_path = self.saver.save(self.sess, out_path, write_meta_graph=False, write_state=False) + Logger.print2('Model saved to: ' + save_path) + except: + Logger.print2("Failed to save model to: " + save_path) + return + + def load_model(self, in_path): + with self.sess.as_default(), self.graph.as_default(): + self.saver.restore(self.sess, in_path) + self._load_normalizers() + Logger.print2('Model loaded from: ' + in_path) + return + + def _get_output_path(self): + assert (self.output_dir != '') + file_path = self.output_dir + '/agent' + str(self.id) + '_model.ckpt' + return file_path + + def _get_int_output_path(self): + assert (self.int_output_dir != '') + file_path = self.int_output_dir + ( + '/agent{:d}_models/agent{:d}_int_model_{:010d}.ckpt').format(self.id, self.id, self.iter) + return file_path + + def _build_graph(self, json_data): + with self.sess.as_default(), self.graph.as_default(): + with tf.variable_scope(self.tf_scope): + self._build_nets(json_data) + + with tf.variable_scope(self.SOLVER_SCOPE): + self._build_losses(json_data) + self._build_solvers(json_data) + + self._initialize_vars() + self._build_saver() + return + + def _init_normalizers(self): + with self.sess.as_default(), self.graph.as_default(): + # update normalizers to sync the tensorflow tensors + self.s_norm.update() + self.g_norm.update() + self.a_norm.update() + return + + @abstractmethod + def _build_nets(self, json_data): + pass + + @abstractmethod + def _build_losses(self, json_data): + pass + + @abstractmethod + def _build_solvers(self, json_data): + pass + + def _tf_vars(self, scope=''): + with self.sess.as_default(), self.graph.as_default(): + res = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope=self.tf_scope + '/' + scope) + assert len(res) > 0 + return res + + def _build_normalizers(self): + with self.sess.as_default(), self.graph.as_default(), tf.variable_scope(self.tf_scope): + with tf.variable_scope(self.RESOURCE_SCOPE): + self.s_norm = TFNormalizer(self.sess, 's_norm', self.get_state_size(), + self.world.env.build_state_norm_groups(self.id)) + state_offset = -self.world.env.build_state_offset(self.id) + print("state_offset=", state_offset) + state_scale = 1 / self.world.env.build_state_scale(self.id) + print("state_scale=", state_scale) + self.s_norm.set_mean_std(-self.world.env.build_state_offset(self.id), + 1 / self.world.env.build_state_scale(self.id)) + + self.g_norm = TFNormalizer(self.sess, 'g_norm', self.get_goal_size(), + self.world.env.build_goal_norm_groups(self.id)) + self.g_norm.set_mean_std(-self.world.env.build_goal_offset(self.id), + 1 / self.world.env.build_goal_scale(self.id)) + + self.a_norm = TFNormalizer(self.sess, 'a_norm', self.get_action_size()) + self.a_norm.set_mean_std(-self.world.env.build_action_offset(self.id), + 1 / self.world.env.build_action_scale(self.id)) + return + + def _load_normalizers(self): + self.s_norm.load() + self.g_norm.load() + self.a_norm.load() + return + + def _update_normalizers(self): + with self.sess.as_default(), self.graph.as_default(): + super()._update_normalizers() + return + + def _initialize_vars(self): + self.sess.run(tf.global_variables_initializer()) + return + + def _build_saver(self): + vars = self._get_saver_vars() + self.saver = tf.train.Saver(vars, max_to_keep=0) + return + + def _get_saver_vars(self): + with self.sess.as_default(), self.graph.as_default(): + vars = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope=self.tf_scope) + vars = [v for v in vars if '/' + self.SOLVER_SCOPE + '/' not in v.name] + #vars = [v for v in vars if '/target/' not in v.name] + assert len(vars) > 0 + return vars + + def _weight_decay_loss(self, scope): + vars = self._tf_vars(scope) + vars_no_bias = [v for v in vars if 'bias' not in v.name] + loss = tf.add_n([tf.nn.l2_loss(v) for v in vars_no_bias]) + return loss + + def _train(self): + with self.sess.as_default(), self.graph.as_default(): + super()._train() + return diff --git a/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/tf_normalizer.py b/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/tf_normalizer.py index 82f5745f0..907eec85b 100644 --- a/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/tf_normalizer.py +++ b/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/tf_normalizer.py @@ -3,65 +3,72 @@ import copy import tensorflow as tf from pybullet_envs.deep_mimic.learning.normalizer import Normalizer + class TFNormalizer(Normalizer): - def __init__(self, sess, scope, size, groups_ids=None, eps=0.02, clip=np.inf): - self.sess = sess - self.scope = scope - super().__init__(size, groups_ids, eps, clip) + def __init__(self, sess, scope, size, groups_ids=None, eps=0.02, clip=np.inf): + self.sess = sess + self.scope = scope + super().__init__(size, groups_ids, eps, clip) + + with tf.variable_scope(self.scope): + self._build_resource_tf() + return + + # initialze count when loading saved values so that things don't change to quickly during updates + def load(self): + self.count = self.count_tf.eval()[0] + self.mean = self.mean_tf.eval() + self.std = self.std_tf.eval() + self.mean_sq = self.calc_mean_sq(self.mean, self.std) + return + + def update(self): + super().update() + self._update_resource_tf() + return - with tf.variable_scope(self.scope): - self._build_resource_tf() - return + def set_mean_std(self, mean, std): + super().set_mean_std(mean, std) + self._update_resource_tf() + return - # initialze count when loading saved values so that things don't change to quickly during updates - def load(self): - self.count = self.count_tf.eval()[0] - self.mean = self.mean_tf.eval() - self.std = self.std_tf.eval() - self.mean_sq = self.calc_mean_sq(self.mean, self.std) - return + def normalize_tf(self, x): + norm_x = (x - self.mean_tf) / self.std_tf + norm_x = tf.clip_by_value(norm_x, -self.clip, self.clip) + return norm_x - def update(self): - super().update() - self._update_resource_tf() - return + def unnormalize_tf(self, norm_x): + x = norm_x * self.std_tf + self.mean_tf + return x - def set_mean_std(self, mean, std): - super().set_mean_std(mean, std) - self._update_resource_tf() - return + def _build_resource_tf(self): + self.count_tf = tf.get_variable(dtype=tf.int32, + name='count', + initializer=np.array([self.count], dtype=np.int32), + trainable=False) + self.mean_tf = tf.get_variable(dtype=tf.float32, + name='mean', + initializer=self.mean.astype(np.float32), + trainable=False) + self.std_tf = tf.get_variable(dtype=tf.float32, + name='std', + initializer=self.std.astype(np.float32), + trainable=False) - def normalize_tf(self, x): - norm_x = (x - self.mean_tf) / self.std_tf - norm_x = tf.clip_by_value(norm_x, -self.clip, self.clip) - return norm_x + self.count_ph = tf.get_variable(dtype=tf.int32, name='count_ph', shape=[1]) + self.mean_ph = tf.get_variable(dtype=tf.float32, name='mean_ph', shape=self.mean.shape) + self.std_ph = tf.get_variable(dtype=tf.float32, name='std_ph', shape=self.std.shape) - def unnormalize_tf(self, norm_x): - x = norm_x * self.std_tf + self.mean_tf - return x - - def _build_resource_tf(self): - self.count_tf = tf.get_variable(dtype=tf.int32, name='count', initializer=np.array([self.count], dtype=np.int32), trainable=False) - self.mean_tf = tf.get_variable(dtype=tf.float32, name='mean', initializer=self.mean.astype(np.float32), trainable=False) - self.std_tf = tf.get_variable(dtype=tf.float32, name='std', initializer=self.std.astype(np.float32), trainable=False) - - self.count_ph = tf.get_variable(dtype=tf.int32, name='count_ph', shape=[1]) - self.mean_ph = tf.get_variable(dtype=tf.float32, name='mean_ph', shape=self.mean.shape) - self.std_ph = tf.get_variable(dtype=tf.float32, name='std_ph', shape=self.std.shape) - - self._update_op = tf.group( - self.count_tf.assign(self.count_ph), - self.mean_tf.assign(self.mean_ph), - self.std_tf.assign(self.std_ph) - ) - return + self._update_op = tf.group(self.count_tf.assign(self.count_ph), + self.mean_tf.assign(self.mean_ph), self.std_tf.assign(self.std_ph)) + return - def _update_resource_tf(self): - feed = { - self.count_ph: np.array([self.count], dtype=np.int32), - self.mean_ph: self.mean, - self.std_ph: self.std - } - self.sess.run(self._update_op, feed_dict=feed) - return + def _update_resource_tf(self): + feed = { + self.count_ph: np.array([self.count], dtype=np.int32), + self.mean_ph: self.mean, + self.std_ph: self.std + } + self.sess.run(self._update_op, feed_dict=feed) + return diff --git a/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/tf_util.py b/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/tf_util.py index b0a315bc3..043d8dcd9 100644 --- a/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/tf_util.py +++ b/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/tf_util.py @@ -4,101 +4,116 @@ import os xavier_initializer = tf.contrib.layers.xavier_initializer() + def disable_gpu(): - os.environ["CUDA_VISIBLE_DEVICES"] = '-1' - return + os.environ["CUDA_VISIBLE_DEVICES"] = '-1' + return + def var_shape(x): - out = [k.value for k in x.get_shape()] - assert all(isinstance(a, int) for a in out), "shape function assumes that shape is fully known" - return out + out = [k.value for k in x.get_shape()] + assert all(isinstance(a, int) for a in out), "shape function assumes that shape is fully known" + return out + def intprod(x): - return int(np.prod(x)) + return int(np.prod(x)) + def numel(x): - n = intprod(var_shape(x)) - return n + n = intprod(var_shape(x)) + return n + def flat_grad(loss, var_list, grad_ys=None): - grads = tf.gradients(loss, var_list, grad_ys) - return tf.concat([tf.reshape(grad, [numel(v)]) for (v, grad) in zip(var_list, grads)], axis=0) - -def fc_net(input, layers_sizes, activation, reuse=None, flatten=False): # build fully connected network - curr_tf = input - for i, size in enumerate(layers_sizes): - with tf.variable_scope(str(i), reuse=reuse): - curr_tf = tf.layers.dense(inputs=curr_tf, - units=size, - kernel_initializer=xavier_initializer, - activation = activation if i < len(layers_sizes)-1 else None) - if flatten: - assert layers_sizes[-1] == 1 - curr_tf = tf.reshape(curr_tf, [-1]) - - return curr_tf + grads = tf.gradients(loss, var_list, grad_ys) + return tf.concat([tf.reshape(grad, [numel(v)]) for (v, grad) in zip(var_list, grads)], axis=0) + + +def fc_net(input, layers_sizes, activation, reuse=None, + flatten=False): # build fully connected network + curr_tf = input + for i, size in enumerate(layers_sizes): + with tf.variable_scope(str(i), reuse=reuse): + curr_tf = tf.layers.dense(inputs=curr_tf, + units=size, + kernel_initializer=xavier_initializer, + activation=activation if i < len(layers_sizes) - 1 else None) + if flatten: + assert layers_sizes[-1] == 1 + curr_tf = tf.reshape(curr_tf, [-1]) + + return curr_tf + def copy(sess, src, dst): - assert len(src) == len(dst) - sess.run(list(map(lambda v: v[1].assign(v[0]), zip(src, dst)))) - return + assert len(src) == len(dst) + sess.run(list(map(lambda v: v[1].assign(v[0]), zip(src, dst)))) + return + def flat_grad(loss, var_list): - grads = tf.gradients(loss, var_list) - return tf.concat(axis=0, values=[tf.reshape(grad, [numel(v)]) - for (v, grad) in zip(var_list, grads)]) + grads = tf.gradients(loss, var_list) + return tf.concat(axis=0, + values=[tf.reshape(grad, [numel(v)]) for (v, grad) in zip(var_list, grads)]) def calc_logp_gaussian(x_tf, mean_tf, std_tf): - dim = tf.to_float(tf.shape(x_tf)[-1]) + dim = tf.to_float(tf.shape(x_tf)[-1]) + + if mean_tf is None: + diff_tf = x_tf + else: + diff_tf = x_tf - mean_tf + + logp_tf = -0.5 * tf.reduce_sum(tf.square(diff_tf / std_tf), axis=-1) + logp_tf += -0.5 * dim * np.log(2 * np.pi) - tf.reduce_sum(tf.log(std_tf), axis=-1) - if mean_tf is None: - diff_tf = x_tf - else: - diff_tf = x_tf - mean_tf + return logp_tf - logp_tf = -0.5 * tf.reduce_sum(tf.square(diff_tf / std_tf), axis=-1) - logp_tf += -0.5 * dim * np.log(2 * np.pi) - tf.reduce_sum(tf.log(std_tf), axis=-1) - - return logp_tf def calc_bound_loss(x_tf, bound_min, bound_max): - # penalty for violating bounds - violation_min = tf.minimum(x_tf - bound_min, 0) - violation_max = tf.maximum(x_tf - bound_max, 0) - violation = tf.reduce_sum(tf.square(violation_min), axis=-1) + tf.reduce_sum(tf.square(violation_max), axis=-1) - loss = 0.5 * tf.reduce_mean(violation) - return loss + # penalty for violating bounds + violation_min = tf.minimum(x_tf - bound_min, 0) + violation_max = tf.maximum(x_tf - bound_max, 0) + violation = tf.reduce_sum(tf.square(violation_min), axis=-1) + tf.reduce_sum( + tf.square(violation_max), axis=-1) + loss = 0.5 * tf.reduce_mean(violation) + return loss + class SetFromFlat(object): - def __init__(self, sess, var_list, dtype=tf.float32): - assigns = [] - shapes = list(map(var_shape, var_list)) - total_size = np.sum([intprod(shape) for shape in shapes]) - self.sess = sess - self.theta = tf.placeholder(dtype,[total_size]) - start=0 - assigns = [] + def __init__(self, sess, var_list, dtype=tf.float32): + assigns = [] + shapes = list(map(var_shape, var_list)) + total_size = np.sum([intprod(shape) for shape in shapes]) + + self.sess = sess + self.theta = tf.placeholder(dtype, [total_size]) + start = 0 + assigns = [] - for (shape,v) in zip(shapes,var_list): - size = intprod(shape) - assigns.append(tf.assign(v, tf.reshape(self.theta[start:start+size],shape))) - start += size + for (shape, v) in zip(shapes, var_list): + size = intprod(shape) + assigns.append(tf.assign(v, tf.reshape(self.theta[start:start + size], shape))) + start += size - self.op = tf.group(*assigns) + self.op = tf.group(*assigns) - return + return + + def __call__(self, theta): + self.sess.run(self.op, feed_dict={self.theta: theta}) + return - def __call__(self, theta): - self.sess.run(self.op, feed_dict={self.theta:theta}) - return class GetFlat(object): - def __init__(self, sess, var_list): - self.sess = sess - self.op = tf.concat(axis=0, values=[tf.reshape(v, [numel(v)]) for v in var_list]) - return - def __call__(self): - return self.sess.run(self.op)
\ No newline at end of file + def __init__(self, sess, var_list): + self.sess = sess + self.op = tf.concat(axis=0, values=[tf.reshape(v, [numel(v)]) for v in var_list]) + return + + def __call__(self): + return self.sess.run(self.op) diff --git a/examples/pybullet/gym/pybullet_envs/deep_mimic/mocap/camera.py b/examples/pybullet/gym/pybullet_envs/deep_mimic/mocap/camera.py index 329eb4b65..d98993b61 100644 --- a/examples/pybullet/gym/pybullet_envs/deep_mimic/mocap/camera.py +++ b/examples/pybullet/gym/pybullet_envs/deep_mimic/mocap/camera.py @@ -1,26 +1,28 @@ -import numpy as np +import numpy as np from quaternion import qrot, qinverse -def normalize_screen_coordinates(X, w, h): - assert X.shape[-1] == 2 - # Normalize so that [0, w] is mapped to [-1, 1], while preserving the aspect ratio - return X/w*2 - [1, h/w] +def normalize_screen_coordinates(X, w, h): + assert X.shape[-1] == 2 + # Normalize so that [0, w] is mapped to [-1, 1], while preserving the aspect ratio + return X / w * 2 - [1, h / w] + def image_coordinates(X, w, h): - assert X.shape[-1] == 2 - # Reverse camera frame normalization - return (X + [1, h/w])*w/2 + assert X.shape[-1] == 2 + # Reverse camera frame normalization + return (X + [1, h / w]) * w / 2 + def world_to_camera(X, R, t): - Rt = qinverse(R) - Q = np.tile(Rt, (*X.shape[:-1], 1)) - V = X - t - return qrot(Q, V) - -def camera_to_world(X, R, t): - Q = np.tile(R, (*X.shape[:-1], 1)) - V = X - return qrot(Q, V) + t + Rt = qinverse(R) + Q = np.tile(Rt, (*X.shape[:-1], 1)) + V = X - t + return qrot(Q, V) + +def camera_to_world(X, R, t): + Q = np.tile(R, (*X.shape[:-1], 1)) + V = X + return qrot(Q, V) + t diff --git a/examples/pybullet/gym/pybullet_envs/deep_mimic/mocap/deepmimic_json_generator.py b/examples/pybullet/gym/pybullet_envs/deep_mimic/mocap/deepmimic_json_generator.py index 23525cd72..872bac78b 100644 --- a/examples/pybullet/gym/pybullet_envs/deep_mimic/mocap/deepmimic_json_generator.py +++ b/examples/pybullet/gym/pybullet_envs/deep_mimic/mocap/deepmimic_json_generator.py @@ -3,54 +3,63 @@ from h36m_dataset import Human36mDataset from camera import * import numpy as np -# In[2]: +# In[2]: joint_info = { - 'joint_name':['root', 'right_hip', 'right_knee', 'right_ankle', 'left_hip', 'left_knee', 'left_ankle', 'chest', 'neck', 'nose', 'eye', 'left_shoulder', 'left_elbow', 'left_wrist', 'right_shoulder', 'right_elbow', 'right_wrist'], - 'father':[0, 0, 1, 2, 0, 4, 5, 0, 7, 8, 9, 8, 11, 12, 8, 14, 15], - 'side':['middle', 'right', 'right', 'right', 'left', 'left', 'left', 'middle', 'middle', 'middle', 'middle', 'left', 'left', 'left', 'right', 'right', 'right'] + 'joint_name': [ + 'root', 'right_hip', 'right_knee', 'right_ankle', 'left_hip', 'left_knee', 'left_ankle', + 'chest', 'neck', 'nose', 'eye', 'left_shoulder', 'left_elbow', 'left_wrist', + 'right_shoulder', 'right_elbow', 'right_wrist' + ], + 'father': [0, 0, 1, 2, 0, 4, 5, 0, 7, 8, 9, 8, 11, 12, 8, 14, 15], + 'side': [ + 'middle', 'right', 'right', 'right', 'left', 'left', 'left', 'middle', 'middle', 'middle', + 'middle', 'left', 'left', 'left', 'right', 'right', 'right' + ] } # In[3]: + def init_fb_h36m_dataset(dataset_path): - dataset = Human36mDataset(dataset_path) - print('Preparing Facebook Human3.6M Dataset...') - for subject in dataset.subjects(): - for action in dataset[subject].keys(): - anim = dataset[subject][action] - positions_3d = [] - for cam in anim['cameras']: - pos_3d = world_to_camera(anim['positions'], R=cam['orientation'], t=cam['translation']) - pos_3d[:, 1:] -= pos_3d[:, :1] # Remove global offset, but keep trajectory in first position - positions_3d.append(pos_3d) - anim['positions_3d'] = positions_3d - return dataset + dataset = Human36mDataset(dataset_path) + print('Preparing Facebook Human3.6M Dataset...') + for subject in dataset.subjects(): + for action in dataset[subject].keys(): + anim = dataset[subject][action] + positions_3d = [] + for cam in anim['cameras']: + pos_3d = world_to_camera(anim['positions'], R=cam['orientation'], t=cam['translation']) + pos_3d[:, 1:] -= pos_3d[:, : + 1] # Remove global offset, but keep trajectory in first position + positions_3d.append(pos_3d) + anim['positions_3d'] = positions_3d + return dataset + def pose3D_from_fb_h36m(dataset, subject, action, shift): - pose_seq = dataset[subject][action]['positions_3d'][0].copy() - trajectory = pose_seq[:, :1] - pose_seq[:, 1:] += trajectory - # Invert camera transformation - cam = dataset.cameras()[subject][0] - pose_seq = camera_to_world(pose_seq, - R=cam['orientation'], - t=cam['translation']) - x = pose_seq[:,:,0:1] - y = pose_seq[:,:,1:2] * -1 - z = pose_seq[:,:,2:3] - pose_seq = np.concatenate((x,z,y),axis=2) - # plus shift - pose_seq += np.array([[shift for i in range(pose_seq.shape[1])] for j in range(pose_seq.shape[0])]) - return pose_seq + pose_seq = dataset[subject][action]['positions_3d'][0].copy() + trajectory = pose_seq[:, :1] + pose_seq[:, 1:] += trajectory + # Invert camera transformation + cam = dataset.cameras()[subject][0] + pose_seq = camera_to_world(pose_seq, R=cam['orientation'], t=cam['translation']) + x = pose_seq[:, :, 0:1] + y = pose_seq[:, :, 1:2] * -1 + z = pose_seq[:, :, 2:3] + pose_seq = np.concatenate((x, z, y), axis=2) + # plus shift + pose_seq += np.array([[shift for i in range(pose_seq.shape[1])] for j in range(pose_seq.shape[0]) + ]) + return pose_seq -def rot_seq_to_deepmimic_json(rot_seq, loop, json_path): - to_json = {"Loop": loop, "Frames":[]} - rot_seq = np.around(rot_seq, decimals=6) - to_json["Frames"] = rot_seq.tolist() - # In[14]: - to_file = json.dumps(to_json) - file = open(json_path,"w") - file.write(to_file) - file.close() +def rot_seq_to_deepmimic_json(rot_seq, loop, json_path): + to_json = {"Loop": loop, "Frames": []} + rot_seq = np.around(rot_seq, decimals=6) + to_json["Frames"] = rot_seq.tolist() + # In[14]: + to_file = json.dumps(to_json) + file = open(json_path, "w") + file.write(to_file) + file.close() diff --git a/examples/pybullet/gym/pybullet_envs/deep_mimic/mocap/h36m_dataset.py b/examples/pybullet/gym/pybullet_envs/deep_mimic/mocap/h36m_dataset.py index bc961fbf4..9dd95f902 100644 --- a/examples/pybullet/gym/pybullet_envs/deep_mimic/mocap/h36m_dataset.py +++ b/examples/pybullet/gym/pybullet_envs/deep_mimic/mocap/h36m_dataset.py @@ -3,11 +3,13 @@ import copy from skeleton import Skeleton from mocap_dataset import MocapDataset from camera import normalize_screen_coordinates, image_coordinates - -h36m_skeleton = Skeleton(parents=[-1, 0, 1, 2, 3, 4, 0, 6, 7, 8, 9, 0, 11, 12, 13, 14, 12, - 16, 17, 18, 19, 20, 19, 22, 12, 24, 25, 26, 27, 28, 27, 30], - joints_left=[6, 7, 8, 9, 10, 16, 17, 18, 19, 20, 21, 22, 23], - joints_right=[1, 2, 3, 4, 5, 24, 25, 26, 27, 28, 29, 30, 31]) + +h36m_skeleton = Skeleton(parents=[ + -1, 0, 1, 2, 3, 4, 0, 6, 7, 8, 9, 0, 11, 12, 13, 14, 12, 16, 17, 18, 19, 20, 19, 22, 12, 24, + 25, 26, 27, 28, 27, 30 +], + joints_left=[6, 7, 8, 9, 10, 16, 17, 18, 19, 20, 21, 22, 23], + joints_right=[1, 2, 3, 4, 5, 24, 25, 26, 27, 28, 29, 30, 31]) h36m_cameras_intrinsic_params = [ { @@ -18,7 +20,7 @@ h36m_cameras_intrinsic_params = [ 'tangential_distortion': [-0.0009756988729350269, -0.00142447161488235], 'res_w': 1000, 'res_h': 1002, - 'azimuth': 70, # Only used for visualization + 'azimuth': 70, # Only used for visualization }, { 'id': '55011271', @@ -28,7 +30,7 @@ h36m_cameras_intrinsic_params = [ 'tangential_distortion': [-0.0016190266469493508, -0.0027408944442868233], 'res_w': 1000, 'res_h': 1000, - 'azimuth': -70, # Only used for visualization + 'azimuth': -70, # Only used for visualization }, { 'id': '58860488', @@ -38,7 +40,7 @@ h36m_cameras_intrinsic_params = [ 'tangential_distortion': [0.0014843869721516967, -0.0007599993259645998], 'res_w': 1000, 'res_h': 1000, - 'azimuth': 110, # Only used for visualization + 'azimuth': 110, # Only used for visualization }, { 'id': '60457274', @@ -48,26 +50,34 @@ h36m_cameras_intrinsic_params = [ 'tangential_distortion': [-0.0005872055771760643, -0.0018133620033040643], 'res_w': 1000, 'res_h': 1002, - 'azimuth': -110, # Only used for visualization + 'azimuth': -110, # Only used for visualization }, ] h36m_cameras_extrinsic_params = { 'S1': [ { - 'orientation': [0.1407056450843811, -0.1500701755285263, -0.755240797996521, 0.6223280429840088], + 'orientation': [ + 0.1407056450843811, -0.1500701755285263, -0.755240797996521, 0.6223280429840088 + ], 'translation': [1841.1070556640625, 4955.28466796875, 1563.4454345703125], }, { - 'orientation': [0.6157187819480896, -0.764836311340332, -0.14833825826644897, 0.11794740706682205], + 'orientation': [ + 0.6157187819480896, -0.764836311340332, -0.14833825826644897, 0.11794740706682205 + ], 'translation': [1761.278564453125, -5078.0068359375, 1606.2650146484375], }, { - 'orientation': [0.14651472866535187, -0.14647851884365082, 0.7653023600578308, -0.6094175577163696], + 'orientation': [ + 0.14651472866535187, -0.14647851884365082, 0.7653023600578308, -0.6094175577163696 + ], 'translation': [-1846.7777099609375, 5215.04638671875, 1491.972412109375], }, { - 'orientation': [0.5834008455276489, -0.7853162288665771, 0.14548823237419128, -0.14749594032764435], + 'orientation': [ + 0.5834008455276489, -0.7853162288665771, 0.14548823237419128, -0.14749594032764435 + ], 'translation': [-1794.7896728515625, -3722.698974609375, 1574.8927001953125], }, ], @@ -91,158 +101,206 @@ h36m_cameras_extrinsic_params = { ], 'S5': [ { - 'orientation': [0.1467377245426178, -0.162370964884758, -0.7551892995834351, 0.6178938746452332], + 'orientation': [ + 0.1467377245426178, -0.162370964884758, -0.7551892995834351, 0.6178938746452332 + ], 'translation': [2097.3916015625, 4880.94482421875, 1605.732421875], }, { - 'orientation': [0.6159758567810059, -0.7626792192459106, -0.15728192031383514, 0.1189815029501915], + 'orientation': [ + 0.6159758567810059, -0.7626792192459106, -0.15728192031383514, 0.1189815029501915 + ], 'translation': [2031.7008056640625, -5167.93310546875, 1612.923095703125], }, { - 'orientation': [0.14291371405124664, -0.12907841801643372, 0.7678384780883789, -0.6110143065452576], + 'orientation': [ + 0.14291371405124664, -0.12907841801643372, 0.7678384780883789, -0.6110143065452576 + ], 'translation': [-1620.5948486328125, 5171.65869140625, 1496.43701171875], }, { - 'orientation': [0.5920479893684387, -0.7814217805862427, 0.1274748593568802, -0.15036417543888092], + 'orientation': [ + 0.5920479893684387, -0.7814217805862427, 0.1274748593568802, -0.15036417543888092 + ], 'translation': [-1637.1737060546875, -3867.3173828125, 1547.033203125], }, ], 'S6': [ { - 'orientation': [0.1337897777557373, -0.15692396461963654, -0.7571090459823608, 0.6198879480361938], + 'orientation': [ + 0.1337897777557373, -0.15692396461963654, -0.7571090459823608, 0.6198879480361938 + ], 'translation': [1935.4517822265625, 4950.24560546875, 1618.0838623046875], }, { - 'orientation': [0.6147197484970093, -0.7628812789916992, -0.16174767911434174, 0.11819244921207428], + 'orientation': [ + 0.6147197484970093, -0.7628812789916992, -0.16174767911434174, 0.11819244921207428 + ], 'translation': [1969.803955078125, -5128.73876953125, 1632.77880859375], }, { - 'orientation': [0.1529948115348816, -0.13529130816459656, 0.7646096348762512, -0.6112781167030334], + 'orientation': [ + 0.1529948115348816, -0.13529130816459656, 0.7646096348762512, -0.6112781167030334 + ], 'translation': [-1769.596435546875, 5185.361328125, 1476.993408203125], }, { - 'orientation': [0.5916101336479187, -0.7804774045944214, 0.12832270562648773, -0.1561593860387802], + 'orientation': [ + 0.5916101336479187, -0.7804774045944214, 0.12832270562648773, -0.1561593860387802 + ], 'translation': [-1721.668701171875, -3884.13134765625, 1540.4879150390625], }, ], 'S7': [ { - 'orientation': [0.1435241848230362, -0.1631336808204651, -0.7548328638076782, 0.6188824772834778], + 'orientation': [ + 0.1435241848230362, -0.1631336808204651, -0.7548328638076782, 0.6188824772834778 + ], 'translation': [1974.512939453125, 4926.3544921875, 1597.8326416015625], }, { - 'orientation': [0.6141672730445862, -0.7638262510299683, -0.1596645563840866, 0.1177929937839508], + 'orientation': [ + 0.6141672730445862, -0.7638262510299683, -0.1596645563840866, 0.1177929937839508 + ], 'translation': [1937.0584716796875, -5119.7900390625, 1631.5665283203125], }, { - 'orientation': [0.14550060033798218, -0.12874816358089447, 0.7660516500473022, -0.6127139329910278], + 'orientation': [ + 0.14550060033798218, -0.12874816358089447, 0.7660516500473022, -0.6127139329910278 + ], 'translation': [-1741.8111572265625, 5208.24951171875, 1464.8245849609375], }, { - 'orientation': [0.5912848114967346, -0.7821764349937439, 0.12445473670959473, -0.15196487307548523], + 'orientation': [ + 0.5912848114967346, -0.7821764349937439, 0.12445473670959473, -0.15196487307548523 + ], 'translation': [-1734.7105712890625, -3832.42138671875, 1548.5830078125], }, ], 'S8': [ { - 'orientation': [0.14110587537288666, -0.15589867532253265, -0.7561917304992676, 0.619644045829773], + 'orientation': [ + 0.14110587537288666, -0.15589867532253265, -0.7561917304992676, 0.619644045829773 + ], 'translation': [2150.65185546875, 4896.1611328125, 1611.9046630859375], }, { - 'orientation': [0.6169601678848267, -0.7647668123245239, -0.14846350252628326, 0.11158157885074615], + 'orientation': [ + 0.6169601678848267, -0.7647668123245239, -0.14846350252628326, 0.11158157885074615 + ], 'translation': [2219.965576171875, -5148.453125, 1613.0440673828125], }, { - 'orientation': [0.1471444070339203, -0.13377119600772858, 0.7670128345489502, -0.6100369691848755], + 'orientation': [ + 0.1471444070339203, -0.13377119600772858, 0.7670128345489502, -0.6100369691848755 + ], 'translation': [-1571.2215576171875, 5137.0185546875, 1498.1761474609375], }, { - 'orientation': [0.5927824378013611, -0.7825870513916016, 0.12147816270589828, -0.14631995558738708], + 'orientation': [ + 0.5927824378013611, -0.7825870513916016, 0.12147816270589828, -0.14631995558738708 + ], 'translation': [-1476.913330078125, -3896.7412109375, 1547.97216796875], }, ], 'S9': [ { - 'orientation': [0.15540587902069092, -0.15548215806484222, -0.7532095313072205, 0.6199594736099243], + 'orientation': [ + 0.15540587902069092, -0.15548215806484222, -0.7532095313072205, 0.6199594736099243 + ], 'translation': [2044.45849609375, 4935.1171875, 1481.2275390625], }, { - 'orientation': [0.618784487247467, -0.7634735107421875, -0.14132238924503326, 0.11933968216180801], + 'orientation': [ + 0.618784487247467, -0.7634735107421875, -0.14132238924503326, 0.11933968216180801 + ], 'translation': [1990.959716796875, -5123.810546875, 1568.8048095703125], }, { - 'orientation': [0.13357827067375183, -0.1367100477218628, 0.7689454555511475, -0.6100738644599915], + 'orientation': [ + 0.13357827067375183, -0.1367100477218628, 0.7689454555511475, -0.6100738644599915 + ], 'translation': [-1670.9921875, 5211.98583984375, 1528.387939453125], }, { - 'orientation': [0.5879399180412292, -0.7823407053947449, 0.1427614390850067, -0.14794869720935822], + 'orientation': [ + 0.5879399180412292, -0.7823407053947449, 0.1427614390850067, -0.14794869720935822 + ], 'translation': [-1696.04345703125, -3827.099853515625, 1591.4127197265625], }, ], 'S11': [ { - 'orientation': [0.15232472121715546, -0.15442320704460144, -0.7547563314437866, 0.6191070079803467], + 'orientation': [ + 0.15232472121715546, -0.15442320704460144, -0.7547563314437866, 0.6191070079803467 + ], 'translation': [2098.440185546875, 4926.5546875, 1500.278564453125], }, { - 'orientation': [0.6189449429512024, -0.7600917220115662, -0.15300633013248444, 0.1255258321762085], + 'orientation': [ + 0.6189449429512024, -0.7600917220115662, -0.15300633013248444, 0.1255258321762085 + ], 'translation': [2083.182373046875, -4912.1728515625, 1561.07861328125], }, { - 'orientation': [0.14943228662014008, -0.15650227665901184, 0.7681233882904053, -0.6026304364204407], + 'orientation': [ + 0.14943228662014008, -0.15650227665901184, 0.7681233882904053, -0.6026304364204407 + ], 'translation': [-1609.8153076171875, 5177.3359375, 1537.896728515625], }, { - 'orientation': [0.5894251465797424, -0.7818877100944519, 0.13991211354732513, -0.14715361595153809], + 'orientation': [ + 0.5894251465797424, -0.7818877100944519, 0.13991211354732513, -0.14715361595153809 + ], 'translation': [-1590.738037109375, -3854.1689453125, 1578.017578125], }, ], } + class Human36mDataset(MocapDataset): - def __init__(self, path, remove_static_joints=True): - super().__init__(fps=50, skeleton=h36m_skeleton) - - self._cameras = copy.deepcopy(h36m_cameras_extrinsic_params) - for cameras in self._cameras.values(): - for i, cam in enumerate(cameras): - cam.update(h36m_cameras_intrinsic_params[i]) - for k, v in cam.items(): - if k not in ['id', 'res_w', 'res_h']: - cam[k] = np.array(v, dtype='float32') - - # Normalize camera frame - cam['center'] = normalize_screen_coordinates(cam['center'], w=cam['res_w'], h=cam['res_h']).astype('float32') - cam['focal_length'] = cam['focal_length']/cam['res_w']*2 - if 'translation' in cam: - cam['translation'] = cam['translation']/1000 # mm to meters - - # Add intrinsic parameters vector - cam['intrinsic'] = np.concatenate((cam['focal_length'], - cam['center'], - cam['radial_distortion'], - cam['tangential_distortion'])) - - # Load serialized dataset - data = np.load(path)['positions_3d'].item() - - self._data = {} - for subject, actions in data.items(): - self._data[subject] = {} - for action_name, positions in actions.items(): - self._data[subject][action_name] = { - 'positions': positions, - 'cameras': self._cameras[subject], - } - - if remove_static_joints: - # Bring the skeleton to 17 joints instead of the original 32 - self.remove_joints([4, 5, 9, 10, 11, 16, 20, 21, 22, 23, 24, 28, 29, 30, 31]) - - # Rewire shoulders to the correct parents - self._skeleton._parents[11] = 8 - self._skeleton._parents[14] = 8 - - def supports_semi_supervised(self): - return True -
\ No newline at end of file + + def __init__(self, path, remove_static_joints=True): + super().__init__(fps=50, skeleton=h36m_skeleton) + + self._cameras = copy.deepcopy(h36m_cameras_extrinsic_params) + for cameras in self._cameras.values(): + for i, cam in enumerate(cameras): + cam.update(h36m_cameras_intrinsic_params[i]) + for k, v in cam.items(): + if k not in ['id', 'res_w', 'res_h']: + cam[k] = np.array(v, dtype='float32') + + # Normalize camera frame + cam['center'] = normalize_screen_coordinates(cam['center'], w=cam['res_w'], + h=cam['res_h']).astype('float32') + cam['focal_length'] = cam['focal_length'] / cam['res_w'] * 2 + if 'translation' in cam: + cam['translation'] = cam['translation'] / 1000 # mm to meters + + # Add intrinsic parameters vector + cam['intrinsic'] = np.concatenate((cam['focal_length'], cam['center'], + cam['radial_distortion'], cam['tangential_distortion'])) + + # Load serialized dataset + data = np.load(path)['positions_3d'].item() + + self._data = {} + for subject, actions in data.items(): + self._data[subject] = {} + for action_name, positions in actions.items(): + self._data[subject][action_name] = { + 'positions': positions, + 'cameras': self._cameras[subject], + } + + if remove_static_joints: + # Bring the skeleton to 17 joints instead of the original 32 + self.remove_joints([4, 5, 9, 10, 11, 16, 20, 21, 22, 23, 24, 28, 29, 30, 31]) + + # Rewire shoulders to the correct parents + self._skeleton._parents[11] = 8 + self._skeleton._parents[14] = 8 + + def supports_semi_supervised(self): + return True diff --git a/examples/pybullet/gym/pybullet_envs/deep_mimic/mocap/humanoid.py b/examples/pybullet/gym/pybullet_envs/deep_mimic/mocap/humanoid.py index f55e3cadb..c9f89c4a1 100644 --- a/examples/pybullet/gym/pybullet_envs/deep_mimic/mocap/humanoid.py +++ b/examples/pybullet/gym/pybullet_envs/deep_mimic/mocap/humanoid.py @@ -1,166 +1,198 @@ -import os, inspect +import os, inspect import math currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe()))) parentdir = os.path.dirname(os.path.dirname(currentdir)) -os.sys.path.insert(0,parentdir) +os.sys.path.insert(0, parentdir) from pybullet_utils.bullet_client import BulletClient import pybullet_data -jointTypes = ["JOINT_REVOLUTE","JOINT_PRISMATIC", - "JOINT_SPHERICAL","JOINT_PLANAR","JOINT_FIXED"] - +jointTypes = [ + "JOINT_REVOLUTE", "JOINT_PRISMATIC", "JOINT_SPHERICAL", "JOINT_PLANAR", "JOINT_FIXED" +] + + class HumanoidPose(object): + def __init__(self): pass - + def Reset(self): - - self._basePos = [0,0,0] - self._baseLinVel = [0,0,0] - self._baseOrn = [0,0,0,1] - self._baseAngVel = [0,0,0] - - self._chestRot = [0,0,0,1] - self._chestVel = [0,0,0] - self._neckRot = [0,0,0,1] - self._neckVel = [0,0,0] - - self._rightHipRot = [0,0,0,1] - self._rightHipVel = [0,0,0] + + self._basePos = [0, 0, 0] + self._baseLinVel = [0, 0, 0] + self._baseOrn = [0, 0, 0, 1] + self._baseAngVel = [0, 0, 0] + + self._chestRot = [0, 0, 0, 1] + self._chestVel = [0, 0, 0] + self._neckRot = [0, 0, 0, 1] + self._neckVel = [0, 0, 0] + + self._rightHipRot = [0, 0, 0, 1] + self._rightHipVel = [0, 0, 0] self._rightKneeRot = [0] self._rightKneeVel = [0] - self._rightAnkleRot = [0,0,0,1] - self._rightAnkleVel = [0,0,0] - - self._rightShoulderRot = [0,0,0,1] - self._rightShoulderVel = [0,0,0] + self._rightAnkleRot = [0, 0, 0, 1] + self._rightAnkleVel = [0, 0, 0] + + self._rightShoulderRot = [0, 0, 0, 1] + self._rightShoulderVel = [0, 0, 0] self._rightElbowRot = [0] self._rightElbowVel = [0] - self._leftHipRot = [0,0,0,1] - self._leftHipVel = [0,0,0] + self._leftHipRot = [0, 0, 0, 1] + self._leftHipVel = [0, 0, 0] self._leftKneeRot = [0] self._leftKneeVel = [0] - self._leftAnkleRot = [0,0,0,1] - self._leftAnkleVel = [0,0,0] - - self._leftShoulderRot = [0,0,0,1] - self._leftShoulderVel = [0,0,0] + self._leftAnkleRot = [0, 0, 0, 1] + self._leftAnkleVel = [0, 0, 0] + + self._leftShoulderRot = [0, 0, 0, 1] + self._leftShoulderVel = [0, 0, 0] self._leftElbowRot = [0] self._leftElbowVel = [0] - def ComputeLinVel(self,posStart, posEnd, deltaTime): - vel = [(posEnd[0]-posStart[0])/deltaTime,(posEnd[1]-posStart[1])/deltaTime,(posEnd[2]-posStart[2])/deltaTime] + def ComputeLinVel(self, posStart, posEnd, deltaTime): + vel = [(posEnd[0] - posStart[0]) / deltaTime, (posEnd[1] - posStart[1]) / deltaTime, + (posEnd[2] - posStart[2]) / deltaTime] return vel - - def ComputeAngVel(self,ornStart, ornEnd, deltaTime, bullet_client): - dorn = bullet_client.getDifferenceQuaternion(ornStart,ornEnd) - axis,angle = bullet_client.getAxisAngleFromQuaternion(dorn) - angVel = [(axis[0]*angle)/deltaTime,(axis[1]*angle)/deltaTime,(axis[2]*angle)/deltaTime] + + def ComputeAngVel(self, ornStart, ornEnd, deltaTime, bullet_client): + dorn = bullet_client.getDifferenceQuaternion(ornStart, ornEnd) + axis, angle = bullet_client.getAxisAngleFromQuaternion(dorn) + angVel = [(axis[0] * angle) / deltaTime, (axis[1] * angle) / deltaTime, + (axis[2] * angle) / deltaTime] return angVel - + def NormalizeQuaternion(self, orn): - length2 = orn[0]*orn[0]+orn[1]*orn[1]+orn[2]*orn[2]+orn[3]*orn[3] - if (length2>0): + length2 = orn[0] * orn[0] + orn[1] * orn[1] + orn[2] * orn[2] + orn[3] * orn[3] + if (length2 > 0): length = math.sqrt(length2) #print("Normalize? length=",length) - def PostProcessMotionData(self, frameData): - baseOrn1Start = [frameData[5],frameData[6], frameData[7],frameData[4]] + baseOrn1Start = [frameData[5], frameData[6], frameData[7], frameData[4]] self.NormalizeQuaternion(baseOrn1Start) - chestRotStart = [frameData[9],frameData[10],frameData[11],frameData[8]] - - neckRotStart = [frameData[13],frameData[14],frameData[15],frameData[12]] - rightHipRotStart = [frameData[17],frameData[18],frameData[19],frameData[16]] - rightAnkleRotStart = [frameData[22],frameData[23],frameData[24],frameData[21]] - rightShoulderRotStart = [frameData[26],frameData[27],frameData[28],frameData[25]] - leftHipRotStart = [frameData[31],frameData[32],frameData[33],frameData[30]] - leftAnkleRotStart = [frameData[36],frameData[37],frameData[38],frameData[35]] - leftShoulderRotStart = [frameData[40],frameData[41],frameData[42],frameData[39]] - - - def Slerp(self, frameFraction, frameData, frameDataNext,bullet_client ): + chestRotStart = [frameData[9], frameData[10], frameData[11], frameData[8]] + + neckRotStart = [frameData[13], frameData[14], frameData[15], frameData[12]] + rightHipRotStart = [frameData[17], frameData[18], frameData[19], frameData[16]] + rightAnkleRotStart = [frameData[22], frameData[23], frameData[24], frameData[21]] + rightShoulderRotStart = [frameData[26], frameData[27], frameData[28], frameData[25]] + leftHipRotStart = [frameData[31], frameData[32], frameData[33], frameData[30]] + leftAnkleRotStart = [frameData[36], frameData[37], frameData[38], frameData[35]] + leftShoulderRotStart = [frameData[40], frameData[41], frameData[42], frameData[39]] + + def Slerp(self, frameFraction, frameData, frameDataNext, bullet_client): keyFrameDuration = frameData[0] - basePos1Start = [frameData[1],frameData[2],frameData[3]] - basePos1End = [frameDataNext[1],frameDataNext[2],frameDataNext[3]] - self._basePos = [basePos1Start[0]+frameFraction*(basePos1End[0]-basePos1Start[0]), - basePos1Start[1]+frameFraction*(basePos1End[1]-basePos1Start[1]), - basePos1Start[2]+frameFraction*(basePos1End[2]-basePos1Start[2])] - self._baseLinVel = self.ComputeLinVel(basePos1Start,basePos1End, keyFrameDuration) - baseOrn1Start = [frameData[5],frameData[6], frameData[7],frameData[4]] - baseOrn1Next = [frameDataNext[5],frameDataNext[6], frameDataNext[7],frameDataNext[4]] - self._baseOrn = bullet_client.getQuaternionSlerp(baseOrn1Start,baseOrn1Next,frameFraction) - self._baseAngVel = self.ComputeAngVel(baseOrn1Start,baseOrn1Next, keyFrameDuration, bullet_client) - + basePos1Start = [frameData[1], frameData[2], frameData[3]] + basePos1End = [frameDataNext[1], frameDataNext[2], frameDataNext[3]] + self._basePos = [ + basePos1Start[0] + frameFraction * (basePos1End[0] - basePos1Start[0]), + basePos1Start[1] + frameFraction * (basePos1End[1] - basePos1Start[1]), + basePos1Start[2] + frameFraction * (basePos1End[2] - basePos1Start[2]) + ] + self._baseLinVel = self.ComputeLinVel(basePos1Start, basePos1End, keyFrameDuration) + baseOrn1Start = [frameData[5], frameData[6], frameData[7], frameData[4]] + baseOrn1Next = [frameDataNext[5], frameDataNext[6], frameDataNext[7], frameDataNext[4]] + self._baseOrn = bullet_client.getQuaternionSlerp(baseOrn1Start, baseOrn1Next, frameFraction) + self._baseAngVel = self.ComputeAngVel(baseOrn1Start, baseOrn1Next, keyFrameDuration, + bullet_client) + ##pre-rotate to make z-up #y2zPos=[0,0,0.0] #y2zOrn = p.getQuaternionFromEuler([1.57,0,0]) #basePos,baseOrn = p.multiplyTransforms(y2zPos, y2zOrn,basePos1,baseOrn1) - chestRotStart = [frameData[9],frameData[10],frameData[11],frameData[8]] - chestRotEnd = [frameDataNext[9],frameDataNext[10],frameDataNext[11],frameDataNext[8]] - self._chestRot = bullet_client.getQuaternionSlerp(chestRotStart,chestRotEnd,frameFraction) - self._chestVel = self.ComputeAngVel(chestRotStart,chestRotEnd,keyFrameDuration,bullet_client) - - neckRotStart = [frameData[13],frameData[14],frameData[15],frameData[12]] - neckRotEnd= [frameDataNext[13],frameDataNext[14],frameDataNext[15],frameDataNext[12]] - self._neckRot = bullet_client.getQuaternionSlerp(neckRotStart,neckRotEnd,frameFraction) - self._neckVel = self.ComputeAngVel(neckRotStart,neckRotEnd,keyFrameDuration,bullet_client) - - rightHipRotStart = [frameData[17],frameData[18],frameData[19],frameData[16]] - rightHipRotEnd = [frameDataNext[17],frameDataNext[18],frameDataNext[19],frameDataNext[16]] - self._rightHipRot = bullet_client.getQuaternionSlerp(rightHipRotStart,rightHipRotEnd,frameFraction) - self._rightHipVel = self.ComputeAngVel(rightHipRotStart,rightHipRotEnd,keyFrameDuration,bullet_client) - + chestRotStart = [frameData[9], frameData[10], frameData[11], frameData[8]] + chestRotEnd = [frameDataNext[9], frameDataNext[10], frameDataNext[11], frameDataNext[8]] + self._chestRot = bullet_client.getQuaternionSlerp(chestRotStart, chestRotEnd, frameFraction) + self._chestVel = self.ComputeAngVel(chestRotStart, chestRotEnd, keyFrameDuration, + bullet_client) + + neckRotStart = [frameData[13], frameData[14], frameData[15], frameData[12]] + neckRotEnd = [frameDataNext[13], frameDataNext[14], frameDataNext[15], frameDataNext[12]] + self._neckRot = bullet_client.getQuaternionSlerp(neckRotStart, neckRotEnd, frameFraction) + self._neckVel = self.ComputeAngVel(neckRotStart, neckRotEnd, keyFrameDuration, bullet_client) + + rightHipRotStart = [frameData[17], frameData[18], frameData[19], frameData[16]] + rightHipRotEnd = [frameDataNext[17], frameDataNext[18], frameDataNext[19], frameDataNext[16]] + self._rightHipRot = bullet_client.getQuaternionSlerp(rightHipRotStart, rightHipRotEnd, + frameFraction) + self._rightHipVel = self.ComputeAngVel(rightHipRotStart, rightHipRotEnd, keyFrameDuration, + bullet_client) + rightKneeRotStart = [frameData[20]] rightKneeRotEnd = [frameDataNext[20]] - self._rightKneeRot = [rightKneeRotStart[0]+frameFraction*(rightKneeRotEnd[0]-rightKneeRotStart[0])] - self._rightKneeVel = [(rightKneeRotEnd[0]-rightKneeRotStart[0])/keyFrameDuration] - - rightAnkleRotStart = [frameData[22],frameData[23],frameData[24],frameData[21]] - rightAnkleRotEnd = [frameDataNext[22],frameDataNext[23],frameDataNext[24],frameDataNext[21]] - self._rightAnkleRot = bullet_client.getQuaternionSlerp(rightAnkleRotStart,rightAnkleRotEnd,frameFraction) - self._rightAnkleVel = self.ComputeAngVel(rightAnkleRotStart,rightAnkleRotEnd,keyFrameDuration,bullet_client) - - rightShoulderRotStart = [frameData[26],frameData[27],frameData[28],frameData[25]] - rightShoulderRotEnd = [frameDataNext[26],frameDataNext[27],frameDataNext[28],frameDataNext[25]] - self._rightShoulderRot = bullet_client.getQuaternionSlerp(rightShoulderRotStart,rightShoulderRotEnd,frameFraction) - self._rightShoulderVel = self.ComputeAngVel(rightShoulderRotStart,rightShoulderRotEnd, keyFrameDuration,bullet_client) - + self._rightKneeRot = [ + rightKneeRotStart[0] + frameFraction * (rightKneeRotEnd[0] - rightKneeRotStart[0]) + ] + self._rightKneeVel = [(rightKneeRotEnd[0] - rightKneeRotStart[0]) / keyFrameDuration] + + rightAnkleRotStart = [frameData[22], frameData[23], frameData[24], frameData[21]] + rightAnkleRotEnd = [frameDataNext[22], frameDataNext[23], frameDataNext[24], frameDataNext[21]] + self._rightAnkleRot = bullet_client.getQuaternionSlerp(rightAnkleRotStart, rightAnkleRotEnd, + frameFraction) + self._rightAnkleVel = self.ComputeAngVel(rightAnkleRotStart, rightAnkleRotEnd, + keyFrameDuration, bullet_client) + + rightShoulderRotStart = [frameData[26], frameData[27], frameData[28], frameData[25]] + rightShoulderRotEnd = [ + frameDataNext[26], frameDataNext[27], frameDataNext[28], frameDataNext[25] + ] + self._rightShoulderRot = bullet_client.getQuaternionSlerp(rightShoulderRotStart, + rightShoulderRotEnd, frameFraction) + self._rightShoulderVel = self.ComputeAngVel(rightShoulderRotStart, rightShoulderRotEnd, + keyFrameDuration, bullet_client) + rightElbowRotStart = [frameData[29]] rightElbowRotEnd = [frameDataNext[29]] - self._rightElbowRot = [rightElbowRotStart[0]+frameFraction*(rightElbowRotEnd[0]-rightElbowRotStart[0])] - self._rightElbowVel = [(rightElbowRotEnd[0]-rightElbowRotStart[0])/keyFrameDuration] - - leftHipRotStart = [frameData[31],frameData[32],frameData[33],frameData[30]] - leftHipRotEnd = [frameDataNext[31],frameDataNext[32],frameDataNext[33],frameDataNext[30]] - self._leftHipRot = bullet_client.getQuaternionSlerp(leftHipRotStart,leftHipRotEnd,frameFraction) - self._leftHipVel = self.ComputeAngVel(leftHipRotStart, leftHipRotEnd,keyFrameDuration,bullet_client) - + self._rightElbowRot = [ + rightElbowRotStart[0] + frameFraction * (rightElbowRotEnd[0] - rightElbowRotStart[0]) + ] + self._rightElbowVel = [(rightElbowRotEnd[0] - rightElbowRotStart[0]) / keyFrameDuration] + + leftHipRotStart = [frameData[31], frameData[32], frameData[33], frameData[30]] + leftHipRotEnd = [frameDataNext[31], frameDataNext[32], frameDataNext[33], frameDataNext[30]] + self._leftHipRot = bullet_client.getQuaternionSlerp(leftHipRotStart, leftHipRotEnd, + frameFraction) + self._leftHipVel = self.ComputeAngVel(leftHipRotStart, leftHipRotEnd, keyFrameDuration, + bullet_client) + leftKneeRotStart = [frameData[34]] leftKneeRotEnd = [frameDataNext[34]] - self._leftKneeRot = [leftKneeRotStart[0] +frameFraction*(leftKneeRotEnd[0]-leftKneeRotStart[0]) ] - self._leftKneeVel = [(leftKneeRotEnd[0]-leftKneeRotStart[0])/keyFrameDuration] - - leftAnkleRotStart = [frameData[36],frameData[37],frameData[38],frameData[35]] - leftAnkleRotEnd = [frameDataNext[36],frameDataNext[37],frameDataNext[38],frameDataNext[35]] - self._leftAnkleRot = bullet_client.getQuaternionSlerp(leftAnkleRotStart,leftAnkleRotEnd,frameFraction) - self._leftAnkleVel = self.ComputeAngVel(leftAnkleRotStart,leftAnkleRotEnd,keyFrameDuration,bullet_client) - - leftShoulderRotStart = [frameData[40],frameData[41],frameData[42],frameData[39]] - leftShoulderRotEnd = [frameDataNext[40],frameDataNext[41],frameDataNext[42],frameDataNext[39]] - self._leftShoulderRot = bullet_client.getQuaternionSlerp(leftShoulderRotStart,leftShoulderRotEnd,frameFraction) - self._leftShoulderVel = self.ComputeAngVel(leftShoulderRotStart,leftShoulderRotEnd,keyFrameDuration,bullet_client) + self._leftKneeRot = [ + leftKneeRotStart[0] + frameFraction * (leftKneeRotEnd[0] - leftKneeRotStart[0]) + ] + self._leftKneeVel = [(leftKneeRotEnd[0] - leftKneeRotStart[0]) / keyFrameDuration] + + leftAnkleRotStart = [frameData[36], frameData[37], frameData[38], frameData[35]] + leftAnkleRotEnd = [frameDataNext[36], frameDataNext[37], frameDataNext[38], frameDataNext[35]] + self._leftAnkleRot = bullet_client.getQuaternionSlerp(leftAnkleRotStart, leftAnkleRotEnd, + frameFraction) + self._leftAnkleVel = self.ComputeAngVel(leftAnkleRotStart, leftAnkleRotEnd, keyFrameDuration, + bullet_client) + + leftShoulderRotStart = [frameData[40], frameData[41], frameData[42], frameData[39]] + leftShoulderRotEnd = [ + frameDataNext[40], frameDataNext[41], frameDataNext[42], frameDataNext[39] + ] + self._leftShoulderRot = bullet_client.getQuaternionSlerp(leftShoulderRotStart, + leftShoulderRotEnd, frameFraction) + self._leftShoulderVel = self.ComputeAngVel(leftShoulderRotStart, leftShoulderRotEnd, + keyFrameDuration, bullet_client) leftElbowRotStart = [frameData[43]] leftElbowRotEnd = [frameDataNext[43]] - self._leftElbowRot = [leftElbowRotStart[0]+frameFraction*(leftElbowRotEnd[0]-leftElbowRotStart[0])] - self._leftElbowVel = [(leftElbowRotEnd[0]-leftElbowRotStart[0])/keyFrameDuration] - + self._leftElbowRot = [ + leftElbowRotStart[0] + frameFraction * (leftElbowRotEnd[0] - leftElbowRotStart[0]) + ] + self._leftElbowVel = [(leftElbowRotEnd[0] - leftElbowRotStart[0]) / keyFrameDuration] + class Humanoid(object): + def __init__(self, pybullet_client, motion_data, baseShift): """Constructs a humanoid and reset it to the initial states. Args: @@ -169,42 +201,46 @@ class Humanoid(object): """ self._baseShift = baseShift self._pybullet_client = pybullet_client - - self.kin_client = BulletClient(pybullet_client.DIRECT)# use SHARED_MEMORY for visual debugging, start a GUI physics server first + + self.kin_client = BulletClient( + pybullet_client.DIRECT + ) # use SHARED_MEMORY for visual debugging, start a GUI physics server first self.kin_client.resetSimulation() self.kin_client.setAdditionalSearchPath(pybullet_data.getDataPath()) - self.kin_client.configureDebugVisualizer(self.kin_client.COV_ENABLE_Y_AXIS_UP,1) - self.kin_client.setGravity(0,-9.8,0) - + self.kin_client.configureDebugVisualizer(self.kin_client.COV_ENABLE_Y_AXIS_UP, 1) + self.kin_client.setGravity(0, -9.8, 0) + self._motion_data = motion_data print("LOADING humanoid!") - self._humanoid = self._pybullet_client.loadURDF( - "humanoid/humanoid.urdf", [0,0.9,0],globalScaling=0.25, useFixedBase=False) - - self._kinematicHumanoid = self.kin_client.loadURDF( - "humanoid/humanoid.urdf", [0,0.9,0],globalScaling=0.25, useFixedBase=False) - - + self._humanoid = self._pybullet_client.loadURDF("humanoid/humanoid.urdf", [0, 0.9, 0], + globalScaling=0.25, + useFixedBase=False) + + self._kinematicHumanoid = self.kin_client.loadURDF("humanoid/humanoid.urdf", [0, 0.9, 0], + globalScaling=0.25, + useFixedBase=False) + #print("human #joints=", self._pybullet_client.getNumJoints(self._humanoid)) pose = HumanoidPose() - - for i in range (self._motion_data.NumFrames()-1): + + for i in range(self._motion_data.NumFrames() - 1): frameData = self._motion_data._motion_data['Frames'][i] pose.PostProcessMotionData(frameData) - - self._pybullet_client.resetBasePositionAndOrientation(self._humanoid,self._baseShift,[0,0,0,1]) + + self._pybullet_client.resetBasePositionAndOrientation(self._humanoid, self._baseShift, + [0, 0, 0, 1]) self._pybullet_client.changeDynamics(self._humanoid, -1, linearDamping=0, angularDamping=0) - for j in range (self._pybullet_client.getNumJoints(self._humanoid)): - ji = self._pybullet_client.getJointInfo(self._humanoid,j) + for j in range(self._pybullet_client.getNumJoints(self._humanoid)): + ji = self._pybullet_client.getJointInfo(self._humanoid, j) self._pybullet_client.changeDynamics(self._humanoid, j, linearDamping=0, angularDamping=0) - self._pybullet_client.changeVisualShape(self._humanoid, j , rgbaColor=[1,1,1,1]) + self._pybullet_client.changeVisualShape(self._humanoid, j, rgbaColor=[1, 1, 1, 1]) #print("joint[",j,"].type=",jointTypes[ji[2]]) #print("joint[",j,"].name=",ji[1]) - + self._initial_state = self._pybullet_client.saveState() - self._allowed_body_parts=[11,14] + self._allowed_body_parts = [11, 14] self.Reset() - + def Reset(self): self._pybullet_client.restoreState(self._initial_state) self.SetSimTime(0) @@ -220,7 +256,7 @@ class Humanoid(object): self.ApplyPose(pose, True, True, self._humanoid, self._pybullet_client) def CalcCycleCount(self, simTime, cycleTime): - phases = simTime / cycleTime; + phases = simTime / cycleTime count = math.floor(phases) loop = True #count = (loop) ? count : cMathUtil::Clamp(count, 0, 1); @@ -230,67 +266,67 @@ class Humanoid(object): self._simTime = t #print("SetTimeTime time =",t) keyFrameDuration = self._motion_data.KeyFrameDuraction() - cycleTime = keyFrameDuration*(self._motion_data.NumFrames()-1) + cycleTime = keyFrameDuration * (self._motion_data.NumFrames() - 1) #print("self._motion_data.NumFrames()=",self._motion_data.NumFrames()) #print("cycleTime=",cycleTime) cycles = self.CalcCycleCount(t, cycleTime) #print("cycles=",cycles) - frameTime = t - cycles*cycleTime - if (frameTime<0): + frameTime = t - cycles * cycleTime + if (frameTime < 0): frameTime += cycleTime - - #print("keyFrameDuration=",keyFrameDuration) + + #print("keyFrameDuration=",keyFrameDuration) #print("frameTime=",frameTime) - self._frame = int(frameTime/keyFrameDuration) + self._frame = int(frameTime / keyFrameDuration) #print("self._frame=",self._frame) - - self._frameNext = self._frame+1 - if (self._frameNext >= self._motion_data.NumFrames()): + + self._frameNext = self._frame + 1 + if (self._frameNext >= self._motion_data.NumFrames()): self._frameNext = self._frame - self._frameFraction = (frameTime - self._frame*keyFrameDuration)/(keyFrameDuration) + self._frameFraction = (frameTime - self._frame * keyFrameDuration) / (keyFrameDuration) #print("self._frameFraction=",self._frameFraction) def Terminates(self): #check if any non-allowed body part hits the ground - terminates=False + terminates = False pts = self._pybullet_client.getContactPoints() for p in pts: part = -1 - if (p[1]==self._humanoid): - part=p[3] - if (p[2]==self._humanoid): - part=p[4] - if (part >=0 and part not in self._allowed_body_parts): - terminates=True - + if (p[1] == self._humanoid): + part = p[3] + if (p[2] == self._humanoid): + part = p[4] + if (part >= 0 and part not in self._allowed_body_parts): + terminates = True + return terminates - + def BuildHeadingTrans(self, rootOrn): #align root transform 'forward' with world-space x axis eul = self._pybullet_client.getEulerFromQuaternion(rootOrn) - refDir = [1,0,0] + refDir = [1, 0, 0] rotVec = self._pybullet_client.rotateVector(rootOrn, refDir) heading = math.atan2(-rotVec[2], rotVec[0]) - heading2=eul[1] + heading2 = eul[1] #print("heading=",heading) - headingOrn = self._pybullet_client.getQuaternionFromAxisAngle([0,1,0],-heading) + headingOrn = self._pybullet_client.getQuaternionFromAxisAngle([0, 1, 0], -heading) return headingOrn def GetPhase(self): keyFrameDuration = self._motion_data.KeyFrameDuraction() - cycleTime = keyFrameDuration*(self._motion_data.NumFrames()-1) + cycleTime = keyFrameDuration * (self._motion_data.NumFrames() - 1) phase = self._simTime / cycleTime - phase = math.fmod(phase,1.0) - if (phase<0): + phase = math.fmod(phase, 1.0) + if (phase < 0): phase += 1 return phase def BuildOriginTrans(self): - rootPos,rootOrn = self._pybullet_client.getBasePositionAndOrientation(self._humanoid) - + rootPos, rootOrn = self._pybullet_client.getBasePositionAndOrientation(self._humanoid) + #print("rootPos=",rootPos, " rootOrn=",rootOrn) - invRootPos=[-rootPos[0], 0, -rootPos[2]] + invRootPos = [-rootPos[0], 0, -rootPos[2]] #invOrigTransPos, invOrigTransOrn = self._pybullet_client.invertTransform(rootPos,rootOrn) headingOrn = self.BuildHeadingTrans(rootOrn) #print("headingOrn=",headingOrn) @@ -298,166 +334,226 @@ class Humanoid(object): #print("headingMat=",headingMat) #dummy, rootOrnWithoutHeading = self._pybullet_client.multiplyTransforms([0,0,0],headingOrn, [0,0,0], rootOrn) #dummy, invOrigTransOrn = self._pybullet_client.multiplyTransforms([0,0,0],rootOrnWithoutHeading, invOrigTransPos, invOrigTransOrn) - - invOrigTransPos, invOrigTransOrn = self._pybullet_client.multiplyTransforms( [0,0,0],headingOrn, invRootPos,[0,0,0,1]) + + invOrigTransPos, invOrigTransOrn = self._pybullet_client.multiplyTransforms([0, 0, 0], + headingOrn, + invRootPos, + [0, 0, 0, 1]) #print("invOrigTransPos=",invOrigTransPos) #print("invOrigTransOrn=",invOrigTransOrn) invOrigTransMat = self._pybullet_client.getMatrixFromQuaternion(invOrigTransOrn) #print("invOrigTransMat =",invOrigTransMat ) return invOrigTransPos, invOrigTransOrn - + def InitializePoseFromMotionData(self): frameData = self._motion_data._motion_data['Frames'][self._frame] frameDataNext = self._motion_data._motion_data['Frames'][self._frameNext] pose = HumanoidPose() pose.Slerp(self._frameFraction, frameData, frameDataNext, self._pybullet_client) return pose - - - def ApplyAction(self, action): #turn action into pose pose = HumanoidPose() pose.Reset() - index=0 + index = 0 angle = action[index] - axis = [action[index+1],action[index+2],action[index+3]] - index+=4 - pose._chestRot = self._pybullet_client.getQuaternionFromAxisAngle(axis,angle) + axis = [action[index + 1], action[index + 2], action[index + 3]] + index += 4 + pose._chestRot = self._pybullet_client.getQuaternionFromAxisAngle(axis, angle) #print("pose._chestRot=",pose._chestRot) angle = action[index] - axis = [action[index+1],action[index+2],action[index+3]] - index+=4 - pose._neckRot = self._pybullet_client.getQuaternionFromAxisAngle(axis,angle) - + axis = [action[index + 1], action[index + 2], action[index + 3]] + index += 4 + pose._neckRot = self._pybullet_client.getQuaternionFromAxisAngle(axis, angle) + angle = action[index] - axis = [action[index+1],action[index+2],action[index+3]] - index+=4 - pose._rightHipRot = self._pybullet_client.getQuaternionFromAxisAngle(axis,angle) - + axis = [action[index + 1], action[index + 2], action[index + 3]] + index += 4 + pose._rightHipRot = self._pybullet_client.getQuaternionFromAxisAngle(axis, angle) + angle = action[index] - index+=1 + index += 1 pose._rightKneeRot = [angle] - + angle = action[index] - axis = [action[index+1],action[index+2],action[index+3]] - index+=4 - pose._rightAnkleRot = self._pybullet_client.getQuaternionFromAxisAngle(axis,angle) - + axis = [action[index + 1], action[index + 2], action[index + 3]] + index += 4 + pose._rightAnkleRot = self._pybullet_client.getQuaternionFromAxisAngle(axis, angle) + angle = action[index] - axis = [action[index+1],action[index+2],action[index+3]] - index+=4 - pose._rightShoulderRot = self._pybullet_client.getQuaternionFromAxisAngle(axis,angle) - + axis = [action[index + 1], action[index + 2], action[index + 3]] + index += 4 + pose._rightShoulderRot = self._pybullet_client.getQuaternionFromAxisAngle(axis, angle) + angle = action[index] - index+=1 + index += 1 pose._rightElbowRot = [angle] - + angle = action[index] - axis = [action[index+1],action[index+2],action[index+3]] - index+=4 - pose._leftHipRot = self._pybullet_client.getQuaternionFromAxisAngle(axis,angle) - + axis = [action[index + 1], action[index + 2], action[index + 3]] + index += 4 + pose._leftHipRot = self._pybullet_client.getQuaternionFromAxisAngle(axis, angle) + angle = action[index] - index+=1 + index += 1 pose._leftKneeRot = [angle] - - + angle = action[index] - axis = [action[index+1],action[index+2],action[index+3]] - index+=4 - pose._leftAnkleRot = self._pybullet_client.getQuaternionFromAxisAngle(axis,angle) - + axis = [action[index + 1], action[index + 2], action[index + 3]] + index += 4 + pose._leftAnkleRot = self._pybullet_client.getQuaternionFromAxisAngle(axis, angle) + angle = action[index] - axis = [action[index+1],action[index+2],action[index+3]] - index+=4 - pose._leftShoulderRot = self._pybullet_client.getQuaternionFromAxisAngle(axis,angle) - + axis = [action[index + 1], action[index + 2], action[index + 3]] + index += 4 + pose._leftShoulderRot = self._pybullet_client.getQuaternionFromAxisAngle(axis, angle) + angle = action[index] - index+=1 + index += 1 pose._leftElbowRot = [angle] - - + #print("index=",index) - + initializeBase = False initializeVelocities = False - self.ApplyPose(pose, initializeBase, initializeVelocities, self._humanoid, self._pybullet_client) - - - def ApplyPose(self, pose, initializeBase, initializeVelocities, humanoid,bc): + self.ApplyPose(pose, initializeBase, initializeVelocities, self._humanoid, + self._pybullet_client) + + def ApplyPose(self, pose, initializeBase, initializeVelocities, humanoid, bc): #todo: get tunable parametes from a json file or from URDF (kd, maxForce) if (initializeBase): - bc.changeVisualShape(humanoid, 2 , rgbaColor=[1,0,0,1]) - basePos=[pose._basePos[0]+self._baseShift[0],pose._basePos[1]+self._baseShift[1],pose._basePos[2]+self._baseShift[2]] - - bc.resetBasePositionAndOrientation(humanoid, - basePos, pose._baseOrn) + bc.changeVisualShape(humanoid, 2, rgbaColor=[1, 0, 0, 1]) + basePos = [ + pose._basePos[0] + self._baseShift[0], pose._basePos[1] + self._baseShift[1], + pose._basePos[2] + self._baseShift[2] + ] + + bc.resetBasePositionAndOrientation(humanoid, basePos, pose._baseOrn) if initializeVelocities: bc.resetBaseVelocity(humanoid, pose._baseLinVel, pose._baseAngVel) #print("resetBaseVelocity=",pose._baseLinVel) else: - bc.changeVisualShape(humanoid, 2 , rgbaColor=[1,1,1,1]) - - - - kp=0.03 - chest=1 - neck=2 - rightShoulder=3 - rightElbow=4 - leftShoulder=6 + bc.changeVisualShape(humanoid, 2, rgbaColor=[1, 1, 1, 1]) + + kp = 0.03 + chest = 1 + neck = 2 + rightShoulder = 3 + rightElbow = 4 + leftShoulder = 6 leftElbow = 7 rightHip = 9 - rightKnee=10 - rightAnkle=11 + rightKnee = 10 + rightAnkle = 11 leftHip = 12 - leftKnee=13 - leftAnkle=14 + leftKnee = 13 + leftAnkle = 14 controlMode = bc.POSITION_CONTROL - + if (initializeBase): if initializeVelocities: - bc.resetJointStateMultiDof(humanoid,chest,pose._chestRot, pose._chestVel) - bc.resetJointStateMultiDof(humanoid,neck,pose._neckRot, pose._neckVel) - bc.resetJointStateMultiDof(humanoid,rightHip,pose._rightHipRot, pose._rightHipVel) - bc.resetJointStateMultiDof(humanoid,rightKnee,pose._rightKneeRot, pose._rightKneeVel) - bc.resetJointStateMultiDof(humanoid,rightAnkle,pose._rightAnkleRot, pose._rightAnkleVel) - bc.resetJointStateMultiDof(humanoid,rightShoulder,pose._rightShoulderRot, pose._rightShoulderVel) - bc.resetJointStateMultiDof(humanoid,rightElbow, pose._rightElbowRot, pose._rightElbowVel) - bc.resetJointStateMultiDof(humanoid,leftHip, pose._leftHipRot, pose._leftHipVel) - bc.resetJointStateMultiDof(humanoid,leftKnee, pose._leftKneeRot, pose._leftKneeVel) - bc.resetJointStateMultiDof(humanoid,leftAnkle, pose._leftAnkleRot, pose._leftAnkleVel) - bc.resetJointStateMultiDof(humanoid,leftShoulder, pose._leftShoulderRot, pose._leftShoulderVel) - bc.resetJointStateMultiDof(humanoid,leftElbow, pose._leftElbowRot, pose._leftElbowVel) + bc.resetJointStateMultiDof(humanoid, chest, pose._chestRot, pose._chestVel) + bc.resetJointStateMultiDof(humanoid, neck, pose._neckRot, pose._neckVel) + bc.resetJointStateMultiDof(humanoid, rightHip, pose._rightHipRot, pose._rightHipVel) + bc.resetJointStateMultiDof(humanoid, rightKnee, pose._rightKneeRot, pose._rightKneeVel) + bc.resetJointStateMultiDof(humanoid, rightAnkle, pose._rightAnkleRot, pose._rightAnkleVel) + bc.resetJointStateMultiDof(humanoid, rightShoulder, pose._rightShoulderRot, + pose._rightShoulderVel) + bc.resetJointStateMultiDof(humanoid, rightElbow, pose._rightElbowRot, pose._rightElbowVel) + bc.resetJointStateMultiDof(humanoid, leftHip, pose._leftHipRot, pose._leftHipVel) + bc.resetJointStateMultiDof(humanoid, leftKnee, pose._leftKneeRot, pose._leftKneeVel) + bc.resetJointStateMultiDof(humanoid, leftAnkle, pose._leftAnkleRot, pose._leftAnkleVel) + bc.resetJointStateMultiDof(humanoid, leftShoulder, pose._leftShoulderRot, + pose._leftShoulderVel) + bc.resetJointStateMultiDof(humanoid, leftElbow, pose._leftElbowRot, pose._leftElbowVel) else: - bc.resetJointStateMultiDof(humanoid,chest,pose._chestRot) - bc.resetJointStateMultiDof(humanoid,neck,pose._neckRot) - bc.resetJointStateMultiDof(humanoid,rightHip,pose._rightHipRot) - bc.resetJointStateMultiDof(humanoid,rightKnee,pose._rightKneeRot) - bc.resetJointStateMultiDof(humanoid,rightAnkle,pose._rightAnkleRot) - bc.resetJointStateMultiDof(humanoid,rightShoulder,pose._rightShoulderRot) - bc.resetJointStateMultiDof(humanoid,rightElbow, pose._rightElbowRot) - bc.resetJointStateMultiDof(humanoid,leftHip, pose._leftHipRot) - bc.resetJointStateMultiDof(humanoid,leftKnee, pose._leftKneeRot) - bc.resetJointStateMultiDof(humanoid,leftAnkle, pose._leftAnkleRot) - bc.resetJointStateMultiDof(humanoid,leftShoulder, pose._leftShoulderRot) - bc.resetJointStateMultiDof(humanoid,leftElbow, pose._leftElbowRot) - - bc.setJointMotorControlMultiDof(humanoid,chest,controlMode, targetPosition=pose._chestRot,positionGain=kp, force=[200]) - bc.setJointMotorControlMultiDof(humanoid,neck,controlMode,targetPosition=pose._neckRot,positionGain=kp, force=[50]) - bc.setJointMotorControlMultiDof(humanoid,rightHip,controlMode,targetPosition=pose._rightHipRot,positionGain=kp, force=[200]) - bc.setJointMotorControlMultiDof(humanoid,rightKnee,controlMode,targetPosition=pose._rightKneeRot,positionGain=kp, force=[150]) - bc.setJointMotorControlMultiDof(humanoid,rightAnkle,controlMode,targetPosition=pose._rightAnkleRot,positionGain=kp, force=[90]) - bc.setJointMotorControlMultiDof(humanoid,rightShoulder,controlMode,targetPosition=pose._rightShoulderRot,positionGain=kp, force=[100]) - bc.setJointMotorControlMultiDof(humanoid,rightElbow, controlMode,targetPosition=pose._rightElbowRot,positionGain=kp, force=[60]) - bc.setJointMotorControlMultiDof(humanoid,leftHip, controlMode,targetPosition=pose._leftHipRot,positionGain=kp, force=[200]) - bc.setJointMotorControlMultiDof(humanoid,leftKnee, controlMode,targetPosition=pose._leftKneeRot,positionGain=kp, force=[150]) - bc.setJointMotorControlMultiDof(humanoid,leftAnkle, controlMode,targetPosition=pose._leftAnkleRot,positionGain=kp, force=[90]) - bc.setJointMotorControlMultiDof(humanoid,leftShoulder, controlMode,targetPosition=pose._leftShoulderRot,positionGain=kp, force=[100]) - bc.setJointMotorControlMultiDof(humanoid,leftElbow, controlMode,targetPosition=pose._leftElbowRot,positionGain=kp, force=[60]) + bc.resetJointStateMultiDof(humanoid, chest, pose._chestRot) + bc.resetJointStateMultiDof(humanoid, neck, pose._neckRot) + bc.resetJointStateMultiDof(humanoid, rightHip, pose._rightHipRot) + bc.resetJointStateMultiDof(humanoid, rightKnee, pose._rightKneeRot) + bc.resetJointStateMultiDof(humanoid, rightAnkle, pose._rightAnkleRot) + bc.resetJointStateMultiDof(humanoid, rightShoulder, pose._rightShoulderRot) + bc.resetJointStateMultiDof(humanoid, rightElbow, pose._rightElbowRot) + bc.resetJointStateMultiDof(humanoid, leftHip, pose._leftHipRot) + bc.resetJointStateMultiDof(humanoid, leftKnee, pose._leftKneeRot) + bc.resetJointStateMultiDof(humanoid, leftAnkle, pose._leftAnkleRot) + bc.resetJointStateMultiDof(humanoid, leftShoulder, pose._leftShoulderRot) + bc.resetJointStateMultiDof(humanoid, leftElbow, pose._leftElbowRot) + + bc.setJointMotorControlMultiDof(humanoid, + chest, + controlMode, + targetPosition=pose._chestRot, + positionGain=kp, + force=[200]) + bc.setJointMotorControlMultiDof(humanoid, + neck, + controlMode, + targetPosition=pose._neckRot, + positionGain=kp, + force=[50]) + bc.setJointMotorControlMultiDof(humanoid, + rightHip, + controlMode, + targetPosition=pose._rightHipRot, + positionGain=kp, + force=[200]) + bc.setJointMotorControlMultiDof(humanoid, + rightKnee, + controlMode, + targetPosition=pose._rightKneeRot, + positionGain=kp, + force=[150]) + bc.setJointMotorControlMultiDof(humanoid, + rightAnkle, + controlMode, + targetPosition=pose._rightAnkleRot, + positionGain=kp, + force=[90]) + bc.setJointMotorControlMultiDof(humanoid, + rightShoulder, + controlMode, + targetPosition=pose._rightShoulderRot, + positionGain=kp, + force=[100]) + bc.setJointMotorControlMultiDof(humanoid, + rightElbow, + controlMode, + targetPosition=pose._rightElbowRot, + positionGain=kp, + force=[60]) + bc.setJointMotorControlMultiDof(humanoid, + leftHip, + controlMode, + targetPosition=pose._leftHipRot, + positionGain=kp, + force=[200]) + bc.setJointMotorControlMultiDof(humanoid, + leftKnee, + controlMode, + targetPosition=pose._leftKneeRot, + positionGain=kp, + force=[150]) + bc.setJointMotorControlMultiDof(humanoid, + leftAnkle, + controlMode, + targetPosition=pose._leftAnkleRot, + positionGain=kp, + force=[90]) + bc.setJointMotorControlMultiDof(humanoid, + leftShoulder, + controlMode, + targetPosition=pose._leftShoulderRot, + positionGain=kp, + force=[100]) + bc.setJointMotorControlMultiDof(humanoid, + leftElbow, + controlMode, + targetPosition=pose._leftElbowRot, + positionGain=kp, + force=[60]) #debug space #if (False): @@ -467,51 +563,58 @@ class Humanoid(object): # jsm = bc.getJointStateMultiDof(self._humanoid, j) # if (len(jsm[0])>0): # bc.resetJointStateMultiDof(self._humanoidDebug,j,jsm[0]) - + def GetState(self): stateVector = [] phase = self.GetPhase() #print("phase=",phase) stateVector.append(phase) - - rootTransPos, rootTransOrn=self.BuildOriginTrans() - basePos,baseOrn = self._pybullet_client.getBasePositionAndOrientation(self._humanoid) - - rootPosRel, dummy = self._pybullet_client.multiplyTransforms(rootTransPos, rootTransOrn, basePos,[0,0,0,1]) + + rootTransPos, rootTransOrn = self.BuildOriginTrans() + basePos, baseOrn = self._pybullet_client.getBasePositionAndOrientation(self._humanoid) + + rootPosRel, dummy = self._pybullet_client.multiplyTransforms(rootTransPos, rootTransOrn, + basePos, [0, 0, 0, 1]) #print("!!!rootPosRel =",rootPosRel ) #print("rootTransPos=",rootTransPos) #print("basePos=",basePos) - localPos,localOrn = self._pybullet_client.multiplyTransforms( rootTransPos, rootTransOrn , basePos,baseOrn ) - - localPos=[localPos[0]-rootPosRel[0],localPos[1]-rootPosRel[1],localPos[2]-rootPosRel[2]] + localPos, localOrn = self._pybullet_client.multiplyTransforms(rootTransPos, rootTransOrn, + basePos, baseOrn) + + localPos = [ + localPos[0] - rootPosRel[0], localPos[1] - rootPosRel[1], localPos[2] - rootPosRel[2] + ] #print("localPos=",localPos) - + stateVector.append(rootPosRel[1]) - - self.pb2dmJoints=[0,1,2,9,10,11,3,4,5,12,13,14,6,7,8] - - for pbJoint in range (self._pybullet_client.getNumJoints(self._humanoid)): + + self.pb2dmJoints = [0, 1, 2, 9, 10, 11, 3, 4, 5, 12, 13, 14, 6, 7, 8] + + for pbJoint in range(self._pybullet_client.getNumJoints(self._humanoid)): j = self.pb2dmJoints[pbJoint] #print("joint order:",j) ls = self._pybullet_client.getLinkState(self._humanoid, j, computeForwardKinematics=True) linkPos = ls[0] linkOrn = ls[1] - linkPosLocal, linkOrnLocal = self._pybullet_client.multiplyTransforms(rootTransPos, rootTransOrn, linkPos,linkOrn) - if (linkOrnLocal[3]<0): - linkOrnLocal=[-linkOrnLocal[0],-linkOrnLocal[1],-linkOrnLocal[2],-linkOrnLocal[3]] - linkPosLocal=[linkPosLocal[0]-rootPosRel[0],linkPosLocal[1]-rootPosRel[1],linkPosLocal[2]-rootPosRel[2]] + linkPosLocal, linkOrnLocal = self._pybullet_client.multiplyTransforms( + rootTransPos, rootTransOrn, linkPos, linkOrn) + if (linkOrnLocal[3] < 0): + linkOrnLocal = [-linkOrnLocal[0], -linkOrnLocal[1], -linkOrnLocal[2], -linkOrnLocal[3]] + linkPosLocal = [ + linkPosLocal[0] - rootPosRel[0], linkPosLocal[1] - rootPosRel[1], + linkPosLocal[2] - rootPosRel[2] + ] for l in linkPosLocal: stateVector.append(l) - + #re-order the quaternion, DeepMimic uses w,x,y,z stateVector.append(linkOrnLocal[3]) stateVector.append(linkOrnLocal[0]) stateVector.append(linkOrnLocal[1]) stateVector.append(linkOrnLocal[2]) - - - for pbJoint in range (self._pybullet_client.getNumJoints(self._humanoid)): + + for pbJoint in range(self._pybullet_client.getNumJoints(self._humanoid)): j = self.pb2dmJoints[pbJoint] ls = self._pybullet_client.getLinkState(self._humanoid, j, computeLinkVelocity=True) linkLinVel = ls[6] @@ -520,19 +623,18 @@ class Humanoid(object): stateVector.append(l) for l in linkAngVel: stateVector.append(l) - - #print("stateVector len=",len(stateVector)) + + #print("stateVector len=",len(stateVector)) #for st in range (len(stateVector)): # print("state[",st,"]=",stateVector[st]) return stateVector - - + def GetReward(self): #from DeepMimic double cSceneImitate::CalcRewardImitate pose_w = 0.5 vel_w = 0.05 - end_eff_w = 0 #0.15 - root_w = 0 #0.2 + end_eff_w = 0 #0.15 + root_w = 0 #0.2 com_w = 0.1 total_w = pose_w + vel_w + end_eff_w + root_w + com_w @@ -557,9 +659,9 @@ class Humanoid(object): root_err = 0 com_err = 0 heading_err = 0 - + #create a mimic reward, comparing the dynamics humanoid with a kinematic one - + pose = self.InitializePoseFromMotionData() #print("self._kinematicHumanoid=",self._kinematicHumanoid) #print("kinematicHumanoid #joints=",self.kin_client.getNumJoints(self._kinematicHumanoid)) @@ -588,46 +690,49 @@ class Humanoid(object): #tVector root_ang_vel0 = cKinTree::GetRootAngVel(joint_mat, vel0); #tVector root_ang_vel1 = cKinTree::GetRootAngVel(joint_mat, vel1); - mJointWeights = [0.20833,0.10416, 0.0625, 0.10416, - 0.0625, 0.041666666666666671, 0.0625, 0.0416, - 0.00, 0.10416, 0.0625, 0.0416, 0.0625, 0.0416, 0.0000] - + mJointWeights = [ + 0.20833, 0.10416, 0.0625, 0.10416, 0.0625, 0.041666666666666671, 0.0625, 0.0416, 0.00, + 0.10416, 0.0625, 0.0416, 0.0625, 0.0416, 0.0000 + ] + num_end_effs = 0 num_joints = 15 - + root_rot_w = mJointWeights[root_id] #pose_err += root_rot_w * cKinTree::CalcRootRotErr(joint_mat, pose0, pose1) #vel_err += root_rot_w * cKinTree::CalcRootAngVelErr(joint_mat, vel0, vel1) - for j in range (num_joints): + for j in range(num_joints): curr_pose_err = 0 curr_vel_err = 0 - w = mJointWeights[j]; - + w = mJointWeights[j] + simJointInfo = self._pybullet_client.getJointStateMultiDof(self._humanoid, j) - + #print("simJointInfo.pos=",simJointInfo[0]) #print("simJointInfo.vel=",simJointInfo[1]) - kinJointInfo = self.kin_client.getJointStateMultiDof(self._kinematicHumanoid,j) + kinJointInfo = self.kin_client.getJointStateMultiDof(self._kinematicHumanoid, j) #print("kinJointInfo.pos=",kinJointInfo[0]) #print("kinJointInfo.vel=",kinJointInfo[1]) - if (len(simJointInfo[0])==1): - angle = simJointInfo[0][0]-kinJointInfo[0][0] - curr_pose_err = angle*angle - velDiff = simJointInfo[1][0]-kinJointInfo[1][0] - curr_vel_err = velDiff*velDiff - if (len(simJointInfo[0])==4): + if (len(simJointInfo[0]) == 1): + angle = simJointInfo[0][0] - kinJointInfo[0][0] + curr_pose_err = angle * angle + velDiff = simJointInfo[1][0] - kinJointInfo[1][0] + curr_vel_err = velDiff * velDiff + if (len(simJointInfo[0]) == 4): #print("quaternion diff") - diffQuat = self._pybullet_client.getDifferenceQuaternion(simJointInfo[0],kinJointInfo[0]) - axis,angle = self._pybullet_client.getAxisAngleFromQuaternion(diffQuat) - curr_pose_err = angle*angle - diffVel = [simJointInfo[1][0]-kinJointInfo[1][0],simJointInfo[1][1]-kinJointInfo[1][1],simJointInfo[1][2]-kinJointInfo[1][2]] - curr_vel_err = diffVel[0]*diffVel[0]+diffVel[1]*diffVel[1]+diffVel[2]*diffVel[2] - - + diffQuat = self._pybullet_client.getDifferenceQuaternion(simJointInfo[0], kinJointInfo[0]) + axis, angle = self._pybullet_client.getAxisAngleFromQuaternion(diffQuat) + curr_pose_err = angle * angle + diffVel = [ + simJointInfo[1][0] - kinJointInfo[1][0], simJointInfo[1][1] - kinJointInfo[1][1], + simJointInfo[1][2] - kinJointInfo[1][2] + ] + curr_vel_err = diffVel[0] * diffVel[0] + diffVel[1] * diffVel[1] + diffVel[2] * diffVel[2] + pose_err += w * curr_pose_err vel_err += w * curr_vel_err - + # bool is_end_eff = sim_char.IsEndEffector(j) # if (is_end_eff) # { @@ -657,7 +762,7 @@ class Humanoid(object): #root_pos0[1] -= root_ground_h0 #root_pos1[1] -= root_ground_h1 #root_pos_err = (root_pos0 - root_pos1).squaredNorm() - # + # #root_rot_err = cMathUtil::QuatDiffTheta(root_rot0, root_rot1) #root_rot_err *= root_rot_err @@ -669,7 +774,7 @@ class Humanoid(object): # + 0.01 * root_vel_err # + 0.001 * root_ang_vel_err #com_err = 0.1 * (com_vel1_world - com_vel0_world).squaredNorm() - + #print("pose_err=",pose_err) #print("vel_err=",vel_err) pose_reward = math.exp(-err_scale * pose_scale * pose_err) @@ -682,10 +787,9 @@ class Humanoid(object): #print("reward = %f (pose_reward=%f, vel_reward=%f, end_eff_reward=%f, root_reward=%f, com_reward=%f)\n", reward, # pose_reward,vel_reward,end_eff_reward, root_reward, com_reward); - + return reward def GetBasePosition(self): - pos,orn = self._pybullet_client.getBasePositionAndOrientation(self._humanoid) + pos, orn = self._pybullet_client.getBasePositionAndOrientation(self._humanoid) return pos - diff --git a/examples/pybullet/gym/pybullet_envs/deep_mimic/mocap/inverse_kinematics.py b/examples/pybullet/gym/pybullet_envs/deep_mimic/mocap/inverse_kinematics.py index 74f7686b7..5d00c5a4d 100644 --- a/examples/pybullet/gym/pybullet_envs/deep_mimic/mocap/inverse_kinematics.py +++ b/examples/pybullet/gym/pybullet_envs/deep_mimic/mocap/inverse_kinematics.py @@ -9,142 +9,138 @@ from transformation import * from pyquaternion import Quaternion + def get_angle(vec1, vec2): - cos_theta = np.dot(vec1, vec2)/(np.linalg.norm(vec1) * np.linalg.norm(vec2)) - return acos(cos_theta) + cos_theta = np.dot(vec1, vec2) / (np.linalg.norm(vec1) * np.linalg.norm(vec2)) + return acos(cos_theta) def get_quaternion(ox, oy, oz, x, y, z): - # given transformed axis in x-y-z order return a quaternion - ox /= np.linalg.norm(ox) - oy /= np.linalg.norm(oy) - oz /= np.linalg.norm(oz) + # given transformed axis in x-y-z order return a quaternion + ox /= np.linalg.norm(ox) + oy /= np.linalg.norm(oy) + oz /= np.linalg.norm(oz) - set1 = np.vstack((ox,oy,oz)) + set1 = np.vstack((ox, oy, oz)) - x /= np.linalg.norm(x) - y /= np.linalg.norm(y) - z /= np.linalg.norm(z) + x /= np.linalg.norm(x) + y /= np.linalg.norm(y) + z /= np.linalg.norm(z) - set2 = np.vstack((x,y,z)) - rot_mat = superimposition_matrix(set1, set2, scale=False, usesvd=True) - rot_qua = quaternion_from_matrix(rot_mat) + set2 = np.vstack((x, y, z)) + rot_mat = superimposition_matrix(set1, set2, scale=False, usesvd=True) + rot_qua = quaternion_from_matrix(rot_mat) - return rot_qua + return rot_qua # 3D coord to deepmimic rotations def coord_to_rot(frameNum, frame, frame_duration): - eps = 0.001 - axis_rotate_rate = 0.3 - - frame = np.array(frame) - tmp = [[] for i in range(15)] - # duration of frame in seconds (1D), - tmp[0] = [frame_duration] - # root position (3D), - tmp[1] = frame[0] - # root rotation (4D), - root_y = (frame[7] - frame[0]) - root_z = (frame[1] - frame[0]) - root_x = np.cross(root_y, root_z) - - x = np.array([1.0,0,0]) - y = np.array([0,1.0,0]) - z = np.array([0,0,1.0]) - - rot_qua = get_quaternion(root_x, root_y, root_z, x, y, z) - tmp[2] = list(rot_qua) - - # chest rotation (4D), - chest_y = (frame[8] - frame[7]) - chest_z = (frame[14] - frame[8]) - chest_x = np.cross(chest_y, chest_z) - rot_qua = get_quaternion(chest_x, chest_y, chest_z, root_x, root_y, root_z) - tmp[3] = list(rot_qua) - - # neck rotation (4D), - neck_y = (frame[10] - frame[8]) - neck_z = np.cross(frame[10]-frame[9], frame[8]-frame[9]) - neck_x = np.cross(neck_y, neck_z) - rot_qua = get_quaternion(neck_x, neck_y, neck_z, chest_x, chest_y, chest_z) - tmp[4] = list(rot_qua) - - # right hip rotation (4D), - r_hip_y = (frame[1] - frame[2]) - r_hip_z = np.cross(frame[1]-frame[2], frame[3]-frame[2]) - r_hip_x = np.cross(r_hip_y, r_hip_z) - rot_qua = get_quaternion(r_hip_x, r_hip_y, r_hip_z, root_x, root_y, root_z) - tmp[5] = list(rot_qua) - - # right knee rotation (1D), - vec1 = frame[1] - frame[2] - vec2 = frame[3] - frame[2] - angle1 = get_angle(vec1, vec2) - tmp[6] = [angle1-pi] - - # right ankle rotation (4D), - tmp[7] = [1,0,0,0] - - # right shoulder rotation (4D), - r_shou_y = (frame[14] - frame[15]) - r_shou_z = np.cross(frame[16]-frame[15], frame[14]-frame[15]) - r_shou_x = np.cross(r_shou_y, r_shou_z) - rot_qua = get_quaternion(r_shou_x, r_shou_y, r_shou_z, chest_x, chest_y, chest_z) - tmp[8] = list(rot_qua) - - # right elbow rotation (1D), - vec1 = frame[14] - frame[15] - vec2 = frame[16] - frame[15] - angle1 = get_angle(vec1, vec2) - tmp[9] = [pi-angle1] - - # left hip rotation (4D), - l_hip_y = (frame[4] - frame[5]) - l_hip_z = np.cross(frame[4]-frame[5], frame[6]-frame[5]) - l_hip_x = np.cross(l_hip_y, l_hip_z) - rot_qua = get_quaternion(l_hip_x, l_hip_y, l_hip_z, root_x, root_y, root_z) - tmp[10] = list(rot_qua) - - # left knee rotation (1D), - vec1 = frame[4] - frame[5] - vec2 = frame[6] - frame[5] - angle1 = get_angle(vec1, vec2) - tmp[11] = [angle1-pi] - - # left ankle rotation (4D), - tmp[12] = [1,0,0,0] - - # left shoulder rotation (4D), - l_shou_y = (frame[11] - frame[12]) - l_shou_z = np.cross(frame[13]-frame[12], frame[11]-frame[12]) - l_shou_x = np.cross(l_shou_y, l_shou_z) - rot_qua = get_quaternion(l_shou_x, l_shou_y, l_shou_z, chest_x, chest_y, chest_z) - tmp[13] = list(rot_qua) - - # left elbow rotation (1D) - vec1 = frame[11] - frame[12] - vec2 = frame[13] - frame[12] - angle1 = get_angle(vec1, vec2) - tmp[14] = [pi-angle1] - - ret = [] - for i in tmp: - ret += list(i) - return np.array(ret) + eps = 0.001 + axis_rotate_rate = 0.3 + + frame = np.array(frame) + tmp = [[] for i in range(15)] + # duration of frame in seconds (1D), + tmp[0] = [frame_duration] + # root position (3D), + tmp[1] = frame[0] + # root rotation (4D), + root_y = (frame[7] - frame[0]) + root_z = (frame[1] - frame[0]) + root_x = np.cross(root_y, root_z) + + x = np.array([1.0, 0, 0]) + y = np.array([0, 1.0, 0]) + z = np.array([0, 0, 1.0]) + + rot_qua = get_quaternion(root_x, root_y, root_z, x, y, z) + tmp[2] = list(rot_qua) + + # chest rotation (4D), + chest_y = (frame[8] - frame[7]) + chest_z = (frame[14] - frame[8]) + chest_x = np.cross(chest_y, chest_z) + rot_qua = get_quaternion(chest_x, chest_y, chest_z, root_x, root_y, root_z) + tmp[3] = list(rot_qua) + + # neck rotation (4D), + neck_y = (frame[10] - frame[8]) + neck_z = np.cross(frame[10] - frame[9], frame[8] - frame[9]) + neck_x = np.cross(neck_y, neck_z) + rot_qua = get_quaternion(neck_x, neck_y, neck_z, chest_x, chest_y, chest_z) + tmp[4] = list(rot_qua) + + # right hip rotation (4D), + r_hip_y = (frame[1] - frame[2]) + r_hip_z = np.cross(frame[1] - frame[2], frame[3] - frame[2]) + r_hip_x = np.cross(r_hip_y, r_hip_z) + rot_qua = get_quaternion(r_hip_x, r_hip_y, r_hip_z, root_x, root_y, root_z) + tmp[5] = list(rot_qua) + + # right knee rotation (1D), + vec1 = frame[1] - frame[2] + vec2 = frame[3] - frame[2] + angle1 = get_angle(vec1, vec2) + tmp[6] = [angle1 - pi] + + # right ankle rotation (4D), + tmp[7] = [1, 0, 0, 0] + + # right shoulder rotation (4D), + r_shou_y = (frame[14] - frame[15]) + r_shou_z = np.cross(frame[16] - frame[15], frame[14] - frame[15]) + r_shou_x = np.cross(r_shou_y, r_shou_z) + rot_qua = get_quaternion(r_shou_x, r_shou_y, r_shou_z, chest_x, chest_y, chest_z) + tmp[8] = list(rot_qua) + + # right elbow rotation (1D), + vec1 = frame[14] - frame[15] + vec2 = frame[16] - frame[15] + angle1 = get_angle(vec1, vec2) + tmp[9] = [pi - angle1] + + # left hip rotation (4D), + l_hip_y = (frame[4] - frame[5]) + l_hip_z = np.cross(frame[4] - frame[5], frame[6] - frame[5]) + l_hip_x = np.cross(l_hip_y, l_hip_z) + rot_qua = get_quaternion(l_hip_x, l_hip_y, l_hip_z, root_x, root_y, root_z) + tmp[10] = list(rot_qua) + + # left knee rotation (1D), + vec1 = frame[4] - frame[5] + vec2 = frame[6] - frame[5] + angle1 = get_angle(vec1, vec2) + tmp[11] = [angle1 - pi] + + # left ankle rotation (4D), + tmp[12] = [1, 0, 0, 0] + + # left shoulder rotation (4D), + l_shou_y = (frame[11] - frame[12]) + l_shou_z = np.cross(frame[13] - frame[12], frame[11] - frame[12]) + l_shou_x = np.cross(l_shou_y, l_shou_z) + rot_qua = get_quaternion(l_shou_x, l_shou_y, l_shou_z, chest_x, chest_y, chest_z) + tmp[13] = list(rot_qua) + + # left elbow rotation (1D) + vec1 = frame[11] - frame[12] + vec2 = frame[13] - frame[12] + angle1 = get_angle(vec1, vec2) + tmp[14] = [pi - angle1] + + ret = [] + for i in tmp: + ret += list(i) + return np.array(ret) + # In[6]: def coord_seq_to_rot_seq(coord_seq, frame_duration): - ret = [] - for i in range(len(coord_seq)): - tmp = coord_to_rot( i, coord_seq[i], frame_duration) - ret.append(list(tmp)) - return ret - - - - - - + ret = [] + for i in range(len(coord_seq)): + tmp = coord_to_rot(i, coord_seq[i], frame_duration) + ret.append(list(tmp)) + return ret diff --git a/examples/pybullet/gym/pybullet_envs/deep_mimic/mocap/mocap_dataset.py b/examples/pybullet/gym/pybullet_envs/deep_mimic/mocap/mocap_dataset.py index 18bc27ae4..c297220db 100644 --- a/examples/pybullet/gym/pybullet_envs/deep_mimic/mocap/mocap_dataset.py +++ b/examples/pybullet/gym/pybullet_envs/deep_mimic/mocap/mocap_dataset.py @@ -1,36 +1,37 @@ import numpy as np from skeleton import Skeleton + class MocapDataset: - def __init__(self, fps, skeleton): - self._skeleton = skeleton - self._fps = fps - self._data = None # Must be filled by subclass - self._cameras = None # Must be filled by subclass - - def remove_joints(self, joints_to_remove): - kept_joints = self._skeleton.remove_joints(joints_to_remove) - for subject in self._data.keys(): - for action in self._data[subject].keys(): - s = self._data[subject][action] - s['positions'] = s['positions'][:, kept_joints] - - - def __getitem__(self, key): - return self._data[key] - - def subjects(self): - return self._data.keys() - - def fps(self): - return self._fps - - def skeleton(self): - return self._skeleton - - def cameras(self): - return self._cameras - - def supports_semi_supervised(self): - # This method can be overridden - return False + + def __init__(self, fps, skeleton): + self._skeleton = skeleton + self._fps = fps + self._data = None # Must be filled by subclass + self._cameras = None # Must be filled by subclass + + def remove_joints(self, joints_to_remove): + kept_joints = self._skeleton.remove_joints(joints_to_remove) + for subject in self._data.keys(): + for action in self._data[subject].keys(): + s = self._data[subject][action] + s['positions'] = s['positions'][:, kept_joints] + + def __getitem__(self, key): + return self._data[key] + + def subjects(self): + return self._data.keys() + + def fps(self): + return self._fps + + def skeleton(self): + return self._skeleton + + def cameras(self): + return self._cameras + + def supports_semi_supervised(self): + # This method can be overridden + return False diff --git a/examples/pybullet/gym/pybullet_envs/deep_mimic/mocap/quaternion.py b/examples/pybullet/gym/pybullet_envs/deep_mimic/mocap/quaternion.py index e4e9a7753..c972846ce 100644 --- a/examples/pybullet/gym/pybullet_envs/deep_mimic/mocap/quaternion.py +++ b/examples/pybullet/gym/pybullet_envs/deep_mimic/mocap/quaternion.py @@ -1,30 +1,31 @@ import numpy as np + def qrot(q, v): - """ + """ Rotate vector(s) v about the rotation described by quaternion(s) q. Expects a tensor of shape (*, 4) for q and a tensor of shape (*, 3) for v, where * denotes any number of dimensions. Returns a tensor of shape (*, 3). """ - assert q.shape[-1] == 4 - assert v.shape[-1] == 3 - assert q.shape[:-1] == v.shape[:-1] + assert q.shape[-1] == 4 + assert v.shape[-1] == 3 + assert q.shape[:-1] == v.shape[:-1] + + qvec = q[..., 1:] + + uv = np.cross(qvec, v) + uuv = np.cross(qvec, uv) + + return (v + 2 * (q[..., :1] * uv + uuv)) - qvec = q[..., 1:] - uv = np.cross(qvec, v) - uuv = np.cross(qvec, uv) - - return (v + 2 * (q[..., :1] * uv + uuv)) - def qinverse(q, inplace=False): - # We assume the quaternion to be normalized - if inplace: - q[..., 1:] *= -1 - return q - else: - w = q[..., :1] - xyz = q[..., 1:] - return np.hstack((w, -xyz)) -
\ No newline at end of file + # We assume the quaternion to be normalized + if inplace: + q[..., 1:] *= -1 + return q + else: + w = q[..., :1] + xyz = q[..., 1:] + return np.hstack((w, -xyz)) diff --git a/examples/pybullet/gym/pybullet_envs/deep_mimic/mocap/render_reference.py b/examples/pybullet/gym/pybullet_envs/deep_mimic/mocap/render_reference.py index c3882bf07..61fe8878d 100644 --- a/examples/pybullet/gym/pybullet_envs/deep_mimic/mocap/render_reference.py +++ b/examples/pybullet/gym/pybullet_envs/deep_mimic/mocap/render_reference.py @@ -1,7 +1,7 @@ -import os, inspect +import os, inspect currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe()))) parentdir = os.path.dirname(os.path.dirname(currentdir)) -os.sys.path.insert(0,parentdir) +os.sys.path.insert(0, parentdir) print('parent:', parentdir) @@ -10,7 +10,6 @@ import pybullet import time import random - from pybullet_utils.bullet_client import BulletClient from deep_mimic.env.motion_capture_data import MotionCaptureData @@ -26,21 +25,28 @@ import pybullet as p import numpy as np import argparse - - parser = argparse.ArgumentParser(description='Arguments for loading reference for learning.') # General arguments -parser.add_argument('--dataset_path', default='data/data_3d_h36m.npz', type=str, help='target dataset') # h36m or humaneva -parser.add_argument('--json_path', default='data/Walking.json', type=str, help='json file path for storing the deepmimic-format json created by inverse-kinect.') +parser.add_argument('--dataset_path', + default='data/data_3d_h36m.npz', + type=str, + help='target dataset') # h36m or humaneva +parser.add_argument( + '--json_path', + default='data/Walking.json', + type=str, + help='json file path for storing the deepmimic-format json created by inverse-kinect.') parser.add_argument('--fps', default=24, type=int, help='frame per second') parser.add_argument('--subject', default='S11', type=str, help='camera subject.') -parser.add_argument('--action', default='Walking', type=str, help='name of the action.') -parser.add_argument('--loop', default='wrap', type=str, help='loop information in deepmimic, wrap or none.') +parser.add_argument('--action', default='Walking', type=str, help='name of the action.') +parser.add_argument('--loop', + default='wrap', + type=str, + help='loop information in deepmimic, wrap or none.') parser.add_argument('--draw_gt', action='store_true', help='draw ground truth or not.') -args = parser.parse_args() - +args = parser.parse_args() dataset_path = args.dataset_path json_path = args.json_path @@ -52,93 +58,75 @@ draw_gt = args.draw_gt def draw_ground_truth(coord_seq, frame, duration, shift): - global joint_info - joint = coord_seq[frame] - shift = np.array(shift) - for i in range(1, 17): - # print(x[11], x[14]) - joint_fa = joint_info['father'][i] - if joint_info['side'][i] == 'right': - p.addUserDebugLine(lineFromXYZ=joint[i]+shift, - lineToXYZ=joint[joint_fa]+shift, - lineColorRGB=(255,0,0), - lineWidth=1, - lifeTime=duration) - else: - p.addUserDebugLine(lineFromXYZ=joint[i]+shift, - lineToXYZ=joint[joint_fa]+shift, - lineColorRGB=(0,0,0), - lineWidth=1, - lifeTime=duration) + global joint_info + joint = coord_seq[frame] + shift = np.array(shift) + for i in range(1, 17): + # print(x[11], x[14]) + joint_fa = joint_info['father'][i] + if joint_info['side'][i] == 'right': + p.addUserDebugLine(lineFromXYZ=joint[i] + shift, + lineToXYZ=joint[joint_fa] + shift, + lineColorRGB=(255, 0, 0), + lineWidth=1, + lifeTime=duration) + else: + p.addUserDebugLine(lineFromXYZ=joint[i] + shift, + lineToXYZ=joint[joint_fa] + shift, + lineColorRGB=(0, 0, 0), + lineWidth=1, + lifeTime=duration) dataset = init_fb_h36m_dataset(dataset_path) -ground_truth = pose3D_from_fb_h36m(dataset, - subject = subject, - action = action, - shift = [1.0,0.0,0.0]) - -rot_seq = coord_seq_to_rot_seq(coord_seq = ground_truth, - frame_duration = 1/fps) - - -rot_seq_to_deepmimic_json( rot_seq = rot_seq, - loop = loop, - json_path = json_path) +ground_truth = pose3D_from_fb_h36m(dataset, subject=subject, action=action, shift=[1.0, 0.0, 0.0]) +rot_seq = coord_seq_to_rot_seq(coord_seq=ground_truth, frame_duration=1 / fps) +rot_seq_to_deepmimic_json(rot_seq=rot_seq, loop=loop, json_path=json_path) bc = BulletClient(connection_mode=pybullet.GUI) bc.setAdditionalSearchPath(pybullet_data.getDataPath()) -bc.configureDebugVisualizer(bc.COV_ENABLE_Y_AXIS_UP,1) -bc.setGravity(0,-9.8,0) -motion=MotionCaptureData() +bc.configureDebugVisualizer(bc.COV_ENABLE_Y_AXIS_UP, 1) +bc.setGravity(0, -9.8, 0) +motion = MotionCaptureData() motionPath = json_path motion.Load(motionPath) print("numFrames = ", motion.NumFrames()) +simTimeId = bc.addUserDebugParameter("simTime", 0, motion.NumFrames() - 1.1, 0) +y2zOrn = bc.getQuaternionFromEuler([-1.57, 0, 0]) +bc.loadURDF("plane.urdf", [0, -0.04, 0], y2zOrn) -simTimeId= bc.addUserDebugParameter("simTime",0,motion.NumFrames()-1.1,0) - -y2zOrn = bc.getQuaternionFromEuler([-1.57,0,0]) -bc.loadURDF("plane.urdf",[0,-0.04,0], y2zOrn) - -humanoid = Humanoid(bc, motion, [0,0,0]) #这是初始位置的坐标 - - +humanoid = Humanoid(bc, motion, [0, 0, 0]) #这是初始位置的坐标 print(p.getBasePositionAndOrientation(humanoid._humanoid)) - - simTime = 0 keyFrameDuration = motion.KeyFrameDuraction() -print("keyFrameDuration=",keyFrameDuration) +print("keyFrameDuration=", keyFrameDuration) for utNum in range(motion.NumFrames()): - bc.stepSimulation() - humanoid.RenderReference(utNum * keyFrameDuration) - if draw_gt: - draw_ground_truth(coord_seq = ground_truth, - frame = utNum, - duration = keyFrameDuration, - shift = [-1.0, 0.0, 1.0]) - time.sleep(0.001) + bc.stepSimulation() + humanoid.RenderReference(utNum * keyFrameDuration) + if draw_gt: + draw_ground_truth(coord_seq=ground_truth, + frame=utNum, + duration=keyFrameDuration, + shift=[-1.0, 0.0, 1.0]) + time.sleep(0.001) stage = 0 - def Reset(humanoid): - global simTime - humanoid.Reset() - simTime = 0 - humanoid.SetSimTime(simTime) - pose = humanoid.InitializePoseFromMotionData() - humanoid.ApplyPose(pose, True, True, humanoid._humanoid,bc) + global simTime + humanoid.Reset() + simTime = 0 + humanoid.SetSimTime(simTime) + pose = humanoid.InitializePoseFromMotionData() + humanoid.ApplyPose(pose, True, True, humanoid._humanoid, bc) Reset(humanoid) p.disconnect() - - diff --git a/examples/pybullet/gym/pybullet_envs/deep_mimic/mocap/skeleton.py b/examples/pybullet/gym/pybullet_envs/deep_mimic/mocap/skeleton.py index fa6d1e622..6e8fe8ba6 100644 --- a/examples/pybullet/gym/pybullet_envs/deep_mimic/mocap/skeleton.py +++ b/examples/pybullet/gym/pybullet_envs/deep_mimic/mocap/skeleton.py @@ -1,81 +1,82 @@ import numpy as np + class Skeleton: - def __init__(self, parents, joints_left, joints_right): - assert len(joints_left) == len(joints_right) - - self._parents = np.array(parents) - self._joints_left = joints_left - self._joints_right = joints_right - self._compute_metadata() - - def num_joints(self): - return len(self._parents) - - def parents(self): - return self._parents - - def has_children(self): - return self._has_children - - def children(self): - return self._children - - def remove_joints(self, joints_to_remove): - """ + + def __init__(self, parents, joints_left, joints_right): + assert len(joints_left) == len(joints_right) + + self._parents = np.array(parents) + self._joints_left = joints_left + self._joints_right = joints_right + self._compute_metadata() + + def num_joints(self): + return len(self._parents) + + def parents(self): + return self._parents + + def has_children(self): + return self._has_children + + def children(self): + return self._children + + def remove_joints(self, joints_to_remove): + """ Remove the joints specified in 'joints_to_remove'. """ - valid_joints = [] - for joint in range(len(self._parents)): - if joint not in joints_to_remove: - valid_joints.append(joint) - - for i in range(len(self._parents)): - while self._parents[i] in joints_to_remove: - self._parents[i] = self._parents[self._parents[i]] - - index_offsets = np.zeros(len(self._parents), dtype=int) - new_parents = [] - for i, parent in enumerate(self._parents): - if i not in joints_to_remove: - new_parents.append(parent - index_offsets[parent]) - else: - index_offsets[i:] += 1 - self._parents = np.array(new_parents) - - - if self._joints_left is not None: - new_joints_left = [] - for joint in self._joints_left: - if joint in valid_joints: - new_joints_left.append(joint - index_offsets[joint]) - self._joints_left = new_joints_left - if self._joints_right is not None: - new_joints_right = [] - for joint in self._joints_right: - if joint in valid_joints: - new_joints_right.append(joint - index_offsets[joint]) - self._joints_right = new_joints_right - - self._compute_metadata() - - return valid_joints - - def joints_left(self): - return self._joints_left - - def joints_right(self): - return self._joints_right - - def _compute_metadata(self): - self._has_children = np.zeros(len(self._parents)).astype(bool) - for i, parent in enumerate(self._parents): - if parent != -1: - self._has_children[parent] = True - - self._children = [] - for i, parent in enumerate(self._parents): - self._children.append([]) - for i, parent in enumerate(self._parents): - if parent != -1: - self._children[parent].append(i) + valid_joints = [] + for joint in range(len(self._parents)): + if joint not in joints_to_remove: + valid_joints.append(joint) + + for i in range(len(self._parents)): + while self._parents[i] in joints_to_remove: + self._parents[i] = self._parents[self._parents[i]] + + index_offsets = np.zeros(len(self._parents), dtype=int) + new_parents = [] + for i, parent in enumerate(self._parents): + if i not in joints_to_remove: + new_parents.append(parent - index_offsets[parent]) + else: + index_offsets[i:] += 1 + self._parents = np.array(new_parents) + + if self._joints_left is not None: + new_joints_left = [] + for joint in self._joints_left: + if joint in valid_joints: + new_joints_left.append(joint - index_offsets[joint]) + self._joints_left = new_joints_left + if self._joints_right is not None: + new_joints_right = [] + for joint in self._joints_right: + if joint in valid_joints: + new_joints_right.append(joint - index_offsets[joint]) + self._joints_right = new_joints_right + + self._compute_metadata() + + return valid_joints + + def joints_left(self): + return self._joints_left + + def joints_right(self): + return self._joints_right + + def _compute_metadata(self): + self._has_children = np.zeros(len(self._parents)).astype(bool) + for i, parent in enumerate(self._parents): + if parent != -1: + self._has_children[parent] = True + + self._children = [] + for i, parent in enumerate(self._parents): + self._children.append([]) + for i, parent in enumerate(self._parents): + if parent != -1: + self._children[parent].append(i) diff --git a/examples/pybullet/gym/pybullet_envs/deep_mimic/mocap/transformation.py b/examples/pybullet/gym/pybullet_envs/deep_mimic/mocap/transformation.py index f9745fe6e..e4807b538 100644 --- a/examples/pybullet/gym/pybullet_envs/deep_mimic/mocap/transformation.py +++ b/examples/pybullet/gym/pybullet_envs/deep_mimic/mocap/transformation.py @@ -1,13 +1,12 @@ from __future__ import division, print_function - import math import numpy def identity_matrix(): - """Return 4x4 identity/unit matrix. + """Return 4x4 identity/unit matrix. >>> I = identity_matrix() >>> numpy.allclose(I, numpy.dot(I, I)) @@ -18,24 +17,24 @@ def identity_matrix(): True """ - return numpy.identity(4) + return numpy.identity(4) def translation_matrix(direction): - """Return matrix to translate by direction vector. + """Return matrix to translate by direction vector. >>> v = numpy.random.random(3) - 0.5 >>> numpy.allclose(v, translation_matrix(v)[:3, 3]) True """ - M = numpy.identity(4) - M[:3, 3] = direction[:3] - return M + M = numpy.identity(4) + M[:3, 3] = direction[:3] + return M def translation_from_matrix(matrix): - """Return translation vector from translation matrix. + """Return translation vector from translation matrix. >>> v0 = numpy.random.random(3) - 0.5 >>> v1 = translation_from_matrix(translation_matrix(v0)) @@ -43,11 +42,11 @@ def translation_from_matrix(matrix): True """ - return numpy.array(matrix, copy=False)[:3, 3].copy() + return numpy.array(matrix, copy=False)[:3, 3].copy() def reflection_matrix(point, normal): - """Return matrix to mirror at plane defined by point and normal vector. + """Return matrix to mirror at plane defined by point and normal vector. >>> v0 = numpy.random.random(4) - 0.5 >>> v0[3] = 1. @@ -65,15 +64,15 @@ def reflection_matrix(point, normal): True """ - normal = unit_vector(normal[:3]) - M = numpy.identity(4) - M[:3, :3] -= 2.0 * numpy.outer(normal, normal) - M[:3, 3] = (2.0 * numpy.dot(point[:3], normal)) * normal - return M + normal = unit_vector(normal[:3]) + M = numpy.identity(4) + M[:3, :3] -= 2.0 * numpy.outer(normal, normal) + M[:3, 3] = (2.0 * numpy.dot(point[:3], normal)) * normal + return M def reflection_from_matrix(matrix): - """Return mirror plane point and normal vector from reflection matrix. + """Return mirror plane point and normal vector from reflection matrix. >>> v0 = numpy.random.random(3) - 0.5 >>> v1 = numpy.random.random(3) - 0.5 @@ -84,25 +83,25 @@ def reflection_from_matrix(matrix): True """ - M = numpy.array(matrix, dtype=numpy.float64, copy=False) - # normal: unit eigenvector corresponding to eigenvalue -1 - w, V = numpy.linalg.eig(M[:3, :3]) - i = numpy.where(abs(numpy.real(w) + 1.0) < 1e-8)[0] - if not len(i): - raise ValueError('no unit eigenvector corresponding to eigenvalue -1') - normal = numpy.real(V[:, i[0]]).squeeze() - # point: any unit eigenvector corresponding to eigenvalue 1 - w, V = numpy.linalg.eig(M) - i = numpy.where(abs(numpy.real(w) - 1.0) < 1e-8)[0] - if not len(i): - raise ValueError('no unit eigenvector corresponding to eigenvalue 1') - point = numpy.real(V[:, i[-1]]).squeeze() - point /= point[3] - return point, normal + M = numpy.array(matrix, dtype=numpy.float64, copy=False) + # normal: unit eigenvector corresponding to eigenvalue -1 + w, V = numpy.linalg.eig(M[:3, :3]) + i = numpy.where(abs(numpy.real(w) + 1.0) < 1e-8)[0] + if not len(i): + raise ValueError('no unit eigenvector corresponding to eigenvalue -1') + normal = numpy.real(V[:, i[0]]).squeeze() + # point: any unit eigenvector corresponding to eigenvalue 1 + w, V = numpy.linalg.eig(M) + i = numpy.where(abs(numpy.real(w) - 1.0) < 1e-8)[0] + if not len(i): + raise ValueError('no unit eigenvector corresponding to eigenvalue 1') + point = numpy.real(V[:, i[-1]]).squeeze() + point /= point[3] + return point, normal def rotation_matrix(angle, direction, point=None): - """Return matrix to rotate about axis defined by point and direction. + """Return matrix to rotate about axis defined by point and direction. >>> R = rotation_matrix(math.pi/2, [0, 0, 1], [1, 0, 0]) >>> numpy.allclose(numpy.dot(R, [0, 0, 0, 1]), [1, -1, 0, 1]) @@ -126,27 +125,26 @@ def rotation_matrix(angle, direction, point=None): True """ - sina = math.sin(angle) - cosa = math.cos(angle) - direction = unit_vector(direction[:3]) - # rotation matrix around unit vector - R = numpy.diag([cosa, cosa, cosa]) - R += numpy.outer(direction, direction) * (1.0 - cosa) - direction *= sina - R += numpy.array([[ 0.0, -direction[2], direction[1]], - [ direction[2], 0.0, -direction[0]], - [-direction[1], direction[0], 0.0]]) - M = numpy.identity(4) - M[:3, :3] = R - if point is not None: - # rotation not around origin - point = numpy.array(point[:3], dtype=numpy.float64, copy=False) - M[:3, 3] = point - numpy.dot(R, point) - return M + sina = math.sin(angle) + cosa = math.cos(angle) + direction = unit_vector(direction[:3]) + # rotation matrix around unit vector + R = numpy.diag([cosa, cosa, cosa]) + R += numpy.outer(direction, direction) * (1.0 - cosa) + direction *= sina + R += numpy.array([[0.0, -direction[2], direction[1]], [direction[2], 0.0, -direction[0]], + [-direction[1], direction[0], 0.0]]) + M = numpy.identity(4) + M[:3, :3] = R + if point is not None: + # rotation not around origin + point = numpy.array(point[:3], dtype=numpy.float64, copy=False) + M[:3, 3] = point - numpy.dot(R, point) + return M def rotation_from_matrix(matrix): - """Return rotation angle and axis from rotation matrix. + """Return rotation angle and axis from rotation matrix. >>> angle = (random.random() - 0.5) * (2*math.pi) >>> direc = numpy.random.random(3) - 0.5 @@ -158,35 +156,35 @@ def rotation_from_matrix(matrix): True """ - R = numpy.array(matrix, dtype=numpy.float64, copy=False) - R33 = R[:3, :3] - # direction: unit eigenvector of R33 corresponding to eigenvalue of 1 - w, W = numpy.linalg.eig(R33.T) - i = numpy.where(abs(numpy.real(w) - 1.0) < 1e-8)[0] - if not len(i): - raise ValueError('no unit eigenvector corresponding to eigenvalue 1') - direction = numpy.real(W[:, i[-1]]).squeeze() - # point: unit eigenvector of R33 corresponding to eigenvalue of 1 - w, Q = numpy.linalg.eig(R) - i = numpy.where(abs(numpy.real(w) - 1.0) < 1e-8)[0] - if not len(i): - raise ValueError('no unit eigenvector corresponding to eigenvalue 1') - point = numpy.real(Q[:, i[-1]]).squeeze() - point /= point[3] - # rotation angle depending on direction - cosa = (numpy.trace(R33) - 1.0) / 2.0 - if abs(direction[2]) > 1e-8: - sina = (R[1, 0] + (cosa-1.0)*direction[0]*direction[1]) / direction[2] - elif abs(direction[1]) > 1e-8: - sina = (R[0, 2] + (cosa-1.0)*direction[0]*direction[2]) / direction[1] - else: - sina = (R[2, 1] + (cosa-1.0)*direction[1]*direction[2]) / direction[0] - angle = math.atan2(sina, cosa) - return angle, direction, point + R = numpy.array(matrix, dtype=numpy.float64, copy=False) + R33 = R[:3, :3] + # direction: unit eigenvector of R33 corresponding to eigenvalue of 1 + w, W = numpy.linalg.eig(R33.T) + i = numpy.where(abs(numpy.real(w) - 1.0) < 1e-8)[0] + if not len(i): + raise ValueError('no unit eigenvector corresponding to eigenvalue 1') + direction = numpy.real(W[:, i[-1]]).squeeze() + # point: unit eigenvector of R33 corresponding to eigenvalue of 1 + w, Q = numpy.linalg.eig(R) + i = numpy.where(abs(numpy.real(w) - 1.0) < 1e-8)[0] + if not len(i): + raise ValueError('no unit eigenvector corresponding to eigenvalue 1') + point = numpy.real(Q[:, i[-1]]).squeeze() + point /= point[3] + # rotation angle depending on direction + cosa = (numpy.trace(R33) - 1.0) / 2.0 + if abs(direction[2]) > 1e-8: + sina = (R[1, 0] + (cosa - 1.0) * direction[0] * direction[1]) / direction[2] + elif abs(direction[1]) > 1e-8: + sina = (R[0, 2] + (cosa - 1.0) * direction[0] * direction[2]) / direction[1] + else: + sina = (R[2, 1] + (cosa - 1.0) * direction[1] * direction[2]) / direction[0] + angle = math.atan2(sina, cosa) + return angle, direction, point def scale_matrix(factor, origin=None, direction=None): - """Return matrix to scale by factor around origin in direction. + """Return matrix to scale by factor around origin in direction. Use factor -1 for point symmetry. @@ -202,25 +200,25 @@ def scale_matrix(factor, origin=None, direction=None): >>> S = scale_matrix(factor, origin, direct) """ - if direction is None: - # uniform scaling - M = numpy.diag([factor, factor, factor, 1.0]) - if origin is not None: - M[:3, 3] = origin[:3] - M[:3, 3] *= 1.0 - factor - else: - # nonuniform scaling - direction = unit_vector(direction[:3]) - factor = 1.0 - factor - M = numpy.identity(4) - M[:3, :3] -= factor * numpy.outer(direction, direction) - if origin is not None: - M[:3, 3] = (factor * numpy.dot(origin[:3], direction)) * direction - return M + if direction is None: + # uniform scaling + M = numpy.diag([factor, factor, factor, 1.0]) + if origin is not None: + M[:3, 3] = origin[:3] + M[:3, 3] *= 1.0 - factor + else: + # nonuniform scaling + direction = unit_vector(direction[:3]) + factor = 1.0 - factor + M = numpy.identity(4) + M[:3, :3] -= factor * numpy.outer(direction, direction) + if origin is not None: + M[:3, 3] = (factor * numpy.dot(origin[:3], direction)) * direction + return M def scale_from_matrix(matrix): - """Return scaling factor, origin and direction from scaling matrix. + """Return scaling factor, origin and direction from scaling matrix. >>> factor = random.random() * 10 - 5 >>> origin = numpy.random.random(3) - 0.5 @@ -237,32 +235,31 @@ def scale_from_matrix(matrix): True """ - M = numpy.array(matrix, dtype=numpy.float64, copy=False) - M33 = M[:3, :3] - factor = numpy.trace(M33) - 2.0 - try: - # direction: unit eigenvector corresponding to eigenvalue factor - w, V = numpy.linalg.eig(M33) - i = numpy.where(abs(numpy.real(w) - factor) < 1e-8)[0][0] - direction = numpy.real(V[:, i]).squeeze() - direction /= vector_norm(direction) - except IndexError: - # uniform scaling - factor = (factor + 2.0) / 3.0 - direction = None - # origin: any eigenvector corresponding to eigenvalue 1 - w, V = numpy.linalg.eig(M) - i = numpy.where(abs(numpy.real(w) - 1.0) < 1e-8)[0] - if not len(i): - raise ValueError('no eigenvector corresponding to eigenvalue 1') - origin = numpy.real(V[:, i[-1]]).squeeze() - origin /= origin[3] - return factor, origin, direction - - -def projection_matrix(point, normal, direction=None, - perspective=None, pseudo=False): - """Return matrix to project onto plane defined by point and normal. + M = numpy.array(matrix, dtype=numpy.float64, copy=False) + M33 = M[:3, :3] + factor = numpy.trace(M33) - 2.0 + try: + # direction: unit eigenvector corresponding to eigenvalue factor + w, V = numpy.linalg.eig(M33) + i = numpy.where(abs(numpy.real(w) - factor) < 1e-8)[0][0] + direction = numpy.real(V[:, i]).squeeze() + direction /= vector_norm(direction) + except IndexError: + # uniform scaling + factor = (factor + 2.0) / 3.0 + direction = None + # origin: any eigenvector corresponding to eigenvalue 1 + w, V = numpy.linalg.eig(M) + i = numpy.where(abs(numpy.real(w) - 1.0) < 1e-8)[0] + if not len(i): + raise ValueError('no eigenvector corresponding to eigenvalue 1') + origin = numpy.real(V[:, i[-1]]).squeeze() + origin /= origin[3] + return factor, origin, direction + + +def projection_matrix(point, normal, direction=None, perspective=None, pseudo=False): + """Return matrix to project onto plane defined by point and normal. Using either perspective point, projection direction, or none of both. @@ -292,38 +289,37 @@ def projection_matrix(point, normal, direction=None, True """ - M = numpy.identity(4) - point = numpy.array(point[:3], dtype=numpy.float64, copy=False) - normal = unit_vector(normal[:3]) - if perspective is not None: - # perspective projection - perspective = numpy.array(perspective[:3], dtype=numpy.float64, - copy=False) - M[0, 0] = M[1, 1] = M[2, 2] = numpy.dot(perspective-point, normal) - M[:3, :3] -= numpy.outer(perspective, normal) - if pseudo: - # preserve relative depth - M[:3, :3] -= numpy.outer(normal, normal) - M[:3, 3] = numpy.dot(point, normal) * (perspective+normal) - else: - M[:3, 3] = numpy.dot(point, normal) * perspective - M[3, :3] = -normal - M[3, 3] = numpy.dot(perspective, normal) - elif direction is not None: - # parallel projection - direction = numpy.array(direction[:3], dtype=numpy.float64, copy=False) - scale = numpy.dot(direction, normal) - M[:3, :3] -= numpy.outer(direction, normal) / scale - M[:3, 3] = direction * (numpy.dot(point, normal) / scale) + M = numpy.identity(4) + point = numpy.array(point[:3], dtype=numpy.float64, copy=False) + normal = unit_vector(normal[:3]) + if perspective is not None: + # perspective projection + perspective = numpy.array(perspective[:3], dtype=numpy.float64, copy=False) + M[0, 0] = M[1, 1] = M[2, 2] = numpy.dot(perspective - point, normal) + M[:3, :3] -= numpy.outer(perspective, normal) + if pseudo: + # preserve relative depth + M[:3, :3] -= numpy.outer(normal, normal) + M[:3, 3] = numpy.dot(point, normal) * (perspective + normal) else: - # orthogonal projection - M[:3, :3] -= numpy.outer(normal, normal) - M[:3, 3] = numpy.dot(point, normal) * normal - return M + M[:3, 3] = numpy.dot(point, normal) * perspective + M[3, :3] = -normal + M[3, 3] = numpy.dot(perspective, normal) + elif direction is not None: + # parallel projection + direction = numpy.array(direction[:3], dtype=numpy.float64, copy=False) + scale = numpy.dot(direction, normal) + M[:3, :3] -= numpy.outer(direction, normal) / scale + M[:3, 3] = direction * (numpy.dot(point, normal) / scale) + else: + # orthogonal projection + M[:3, :3] -= numpy.outer(normal, normal) + M[:3, 3] = numpy.dot(point, normal) * normal + return M def projection_from_matrix(matrix, pseudo=False): - """Return projection plane and perspective point from projection matrix. + """Return projection plane and perspective point from projection matrix. Return values are same as arguments for projection_matrix function: point, normal, direction, perspective, and pseudo. @@ -354,49 +350,48 @@ def projection_from_matrix(matrix, pseudo=False): True """ - M = numpy.array(matrix, dtype=numpy.float64, copy=False) - M33 = M[:3, :3] - w, V = numpy.linalg.eig(M) - i = numpy.where(abs(numpy.real(w) - 1.0) < 1e-8)[0] - if not pseudo and len(i): - # point: any eigenvector corresponding to eigenvalue 1 - point = numpy.real(V[:, i[-1]]).squeeze() - point /= point[3] - # direction: unit eigenvector corresponding to eigenvalue 0 - w, V = numpy.linalg.eig(M33) - i = numpy.where(abs(numpy.real(w)) < 1e-8)[0] - if not len(i): - raise ValueError('no eigenvector corresponding to eigenvalue 0') - direction = numpy.real(V[:, i[0]]).squeeze() - direction /= vector_norm(direction) - # normal: unit eigenvector of M33.T corresponding to eigenvalue 0 - w, V = numpy.linalg.eig(M33.T) - i = numpy.where(abs(numpy.real(w)) < 1e-8)[0] - if len(i): - # parallel projection - normal = numpy.real(V[:, i[0]]).squeeze() - normal /= vector_norm(normal) - return point, normal, direction, None, False - else: - # orthogonal projection, where normal equals direction vector - return point, direction, None, None, False + M = numpy.array(matrix, dtype=numpy.float64, copy=False) + M33 = M[:3, :3] + w, V = numpy.linalg.eig(M) + i = numpy.where(abs(numpy.real(w) - 1.0) < 1e-8)[0] + if not pseudo and len(i): + # point: any eigenvector corresponding to eigenvalue 1 + point = numpy.real(V[:, i[-1]]).squeeze() + point /= point[3] + # direction: unit eigenvector corresponding to eigenvalue 0 + w, V = numpy.linalg.eig(M33) + i = numpy.where(abs(numpy.real(w)) < 1e-8)[0] + if not len(i): + raise ValueError('no eigenvector corresponding to eigenvalue 0') + direction = numpy.real(V[:, i[0]]).squeeze() + direction /= vector_norm(direction) + # normal: unit eigenvector of M33.T corresponding to eigenvalue 0 + w, V = numpy.linalg.eig(M33.T) + i = numpy.where(abs(numpy.real(w)) < 1e-8)[0] + if len(i): + # parallel projection + normal = numpy.real(V[:, i[0]]).squeeze() + normal /= vector_norm(normal) + return point, normal, direction, None, False else: - # perspective projection - i = numpy.where(abs(numpy.real(w)) > 1e-8)[0] - if not len(i): - raise ValueError( - 'no eigenvector not corresponding to eigenvalue 0') - point = numpy.real(V[:, i[-1]]).squeeze() - point /= point[3] - normal = - M[3, :3] - perspective = M[:3, 3] / numpy.dot(point[:3], normal) - if pseudo: - perspective -= normal - return point, normal, None, perspective, pseudo + # orthogonal projection, where normal equals direction vector + return point, direction, None, None, False + else: + # perspective projection + i = numpy.where(abs(numpy.real(w)) > 1e-8)[0] + if not len(i): + raise ValueError('no eigenvector not corresponding to eigenvalue 0') + point = numpy.real(V[:, i[-1]]).squeeze() + point /= point[3] + normal = -M[3, :3] + perspective = M[:3, 3] / numpy.dot(point[:3], normal) + if pseudo: + perspective -= normal + return point, normal, None, perspective, pseudo def clip_matrix(left, right, bottom, top, near, far, perspective=False): - """Return matrix to obtain normalized device coordinates from frustum. + """Return matrix to obtain normalized device coordinates from frustum. The frustum bounds are axis-aligned along x (left, right), y (bottom, top) and z (near, far). @@ -429,26 +424,24 @@ def clip_matrix(left, right, bottom, top, near, far, perspective=False): array([ 1., 1., -1., 1.]) """ - if left >= right or bottom >= top or near >= far: - raise ValueError('invalid frustum') - if perspective: - if near <= _EPS: - raise ValueError('invalid frustum: near <= 0') - t = 2.0 * near - M = [[t/(left-right), 0.0, (right+left)/(right-left), 0.0], - [0.0, t/(bottom-top), (top+bottom)/(top-bottom), 0.0], - [0.0, 0.0, (far+near)/(near-far), t*far/(far-near)], - [0.0, 0.0, -1.0, 0.0]] - else: - M = [[2.0/(right-left), 0.0, 0.0, (right+left)/(left-right)], - [0.0, 2.0/(top-bottom), 0.0, (top+bottom)/(bottom-top)], - [0.0, 0.0, 2.0/(far-near), (far+near)/(near-far)], - [0.0, 0.0, 0.0, 1.0]] - return numpy.array(M) + if left >= right or bottom >= top or near >= far: + raise ValueError('invalid frustum') + if perspective: + if near <= _EPS: + raise ValueError('invalid frustum: near <= 0') + t = 2.0 * near + M = [[t / (left - right), 0.0, (right + left) / (right - left), 0.0], + [0.0, t / (bottom - top), (top + bottom) / (top - bottom), 0.0], + [0.0, 0.0, (far + near) / (near - far), t * far / (far - near)], [0.0, 0.0, -1.0, 0.0]] + else: + M = [[2.0 / (right - left), 0.0, 0.0, (right + left) / (left - right)], + [0.0, 2.0 / (top - bottom), 0.0, (top + bottom) / (bottom - top)], + [0.0, 0.0, 2.0 / (far - near), (far + near) / (near - far)], [0.0, 0.0, 0.0, 1.0]] + return numpy.array(M) def shear_matrix(angle, direction, point, normal): - """Return matrix to shear by angle along direction vector on shear plane. + """Return matrix to shear by angle along direction vector on shear plane. The shear plane is defined by a point and normal vector. The direction vector must be orthogonal to the plane's normal vector. @@ -467,19 +460,19 @@ def shear_matrix(angle, direction, point, normal): True """ - normal = unit_vector(normal[:3]) - direction = unit_vector(direction[:3]) - if abs(numpy.dot(normal, direction)) > 1e-6: - raise ValueError('direction and normal vectors are not orthogonal') - angle = math.tan(angle) - M = numpy.identity(4) - M[:3, :3] += angle * numpy.outer(direction, normal) - M[:3, 3] = -angle * numpy.dot(point[:3], normal) * direction - return M + normal = unit_vector(normal[:3]) + direction = unit_vector(direction[:3]) + if abs(numpy.dot(normal, direction)) > 1e-6: + raise ValueError('direction and normal vectors are not orthogonal') + angle = math.tan(angle) + M = numpy.identity(4) + M[:3, :3] += angle * numpy.outer(direction, normal) + M[:3, 3] = -angle * numpy.dot(point[:3], normal) * direction + return M def shear_from_matrix(matrix): - """Return shear angle, direction and plane from shear matrix. + """Return shear angle, direction and plane from shear matrix. >>> angle = (random.random() - 0.5) * 4*math.pi >>> direct = numpy.random.random(3) - 0.5 @@ -492,39 +485,39 @@ def shear_from_matrix(matrix): True """ - M = numpy.array(matrix, dtype=numpy.float64, copy=False) - M33 = M[:3, :3] - # normal: cross independent eigenvectors corresponding to the eigenvalue 1 - w, V = numpy.linalg.eig(M33) - i = numpy.where(abs(numpy.real(w) - 1.0) < 1e-4)[0] - if len(i) < 2: - raise ValueError('no two linear independent eigenvectors found %s' % w) - V = numpy.real(V[:, i]).squeeze().T - lenorm = -1.0 - for i0, i1 in ((0, 1), (0, 2), (1, 2)): - n = numpy.cross(V[i0], V[i1]) - w = vector_norm(n) - if w > lenorm: - lenorm = w - normal = n - normal /= lenorm - # direction and angle - direction = numpy.dot(M33 - numpy.identity(3), normal) - angle = vector_norm(direction) - direction /= angle - angle = math.atan(angle) - # point: eigenvector corresponding to eigenvalue 1 - w, V = numpy.linalg.eig(M) - i = numpy.where(abs(numpy.real(w) - 1.0) < 1e-8)[0] - if not len(i): - raise ValueError('no eigenvector corresponding to eigenvalue 1') - point = numpy.real(V[:, i[-1]]).squeeze() - point /= point[3] - return angle, direction, point, normal + M = numpy.array(matrix, dtype=numpy.float64, copy=False) + M33 = M[:3, :3] + # normal: cross independent eigenvectors corresponding to the eigenvalue 1 + w, V = numpy.linalg.eig(M33) + i = numpy.where(abs(numpy.real(w) - 1.0) < 1e-4)[0] + if len(i) < 2: + raise ValueError('no two linear independent eigenvectors found %s' % w) + V = numpy.real(V[:, i]).squeeze().T + lenorm = -1.0 + for i0, i1 in ((0, 1), (0, 2), (1, 2)): + n = numpy.cross(V[i0], V[i1]) + w = vector_norm(n) + if w > lenorm: + lenorm = w + normal = n + normal /= lenorm + # direction and angle + direction = numpy.dot(M33 - numpy.identity(3), normal) + angle = vector_norm(direction) + direction /= angle + angle = math.atan(angle) + # point: eigenvector corresponding to eigenvalue 1 + w, V = numpy.linalg.eig(M) + i = numpy.where(abs(numpy.real(w) - 1.0) < 1e-8)[0] + if not len(i): + raise ValueError('no eigenvector corresponding to eigenvalue 1') + point = numpy.real(V[:, i[-1]]).squeeze() + point /= point[3] + return angle, direction, point, normal def decompose_matrix(matrix): - """Return sequence of transformations from transformation matrix. + """Return sequence of transformations from transformation matrix. matrix : array_like Non-degenerative homogeneous transformation matrix @@ -554,63 +547,62 @@ def decompose_matrix(matrix): True """ - M = numpy.array(matrix, dtype=numpy.float64, copy=True).T - if abs(M[3, 3]) < _EPS: - raise ValueError('M[3, 3] is zero') - M /= M[3, 3] - P = M.copy() - P[:, 3] = 0.0, 0.0, 0.0, 1.0 - if not numpy.linalg.det(P): - raise ValueError('matrix is singular') - - scale = numpy.zeros((3, )) - shear = [0.0, 0.0, 0.0] - angles = [0.0, 0.0, 0.0] - - if any(abs(M[:3, 3]) > _EPS): - perspective = numpy.dot(M[:, 3], numpy.linalg.inv(P.T)) - M[:, 3] = 0.0, 0.0, 0.0, 1.0 - else: - perspective = numpy.array([0.0, 0.0, 0.0, 1.0]) - - translate = M[3, :3].copy() - M[3, :3] = 0.0 - - row = M[:3, :3].copy() - scale[0] = vector_norm(row[0]) - row[0] /= scale[0] - shear[0] = numpy.dot(row[0], row[1]) - row[1] -= row[0] * shear[0] - scale[1] = vector_norm(row[1]) - row[1] /= scale[1] - shear[0] /= scale[1] - shear[1] = numpy.dot(row[0], row[2]) - row[2] -= row[0] * shear[1] - shear[2] = numpy.dot(row[1], row[2]) - row[2] -= row[1] * shear[2] - scale[2] = vector_norm(row[2]) - row[2] /= scale[2] - shear[1:] /= scale[2] - - if numpy.dot(row[0], numpy.cross(row[1], row[2])) < 0: - numpy.negative(scale, scale) - numpy.negative(row, row) - - angles[1] = math.asin(-row[0, 2]) - if math.cos(angles[1]): - angles[0] = math.atan2(row[1, 2], row[2, 2]) - angles[2] = math.atan2(row[0, 1], row[0, 0]) - else: - # angles[0] = math.atan2(row[1, 0], row[1, 1]) - angles[0] = math.atan2(-row[2, 1], row[1, 1]) - angles[2] = 0.0 - - return scale, shear, angles, translate, perspective - - -def compose_matrix(scale=None, shear=None, angles=None, translate=None, - perspective=None): - """Return transformation matrix from sequence of transformations. + M = numpy.array(matrix, dtype=numpy.float64, copy=True).T + if abs(M[3, 3]) < _EPS: + raise ValueError('M[3, 3] is zero') + M /= M[3, 3] + P = M.copy() + P[:, 3] = 0.0, 0.0, 0.0, 1.0 + if not numpy.linalg.det(P): + raise ValueError('matrix is singular') + + scale = numpy.zeros((3,)) + shear = [0.0, 0.0, 0.0] + angles = [0.0, 0.0, 0.0] + + if any(abs(M[:3, 3]) > _EPS): + perspective = numpy.dot(M[:, 3], numpy.linalg.inv(P.T)) + M[:, 3] = 0.0, 0.0, 0.0, 1.0 + else: + perspective = numpy.array([0.0, 0.0, 0.0, 1.0]) + + translate = M[3, :3].copy() + M[3, :3] = 0.0 + + row = M[:3, :3].copy() + scale[0] = vector_norm(row[0]) + row[0] /= scale[0] + shear[0] = numpy.dot(row[0], row[1]) + row[1] -= row[0] * shear[0] + scale[1] = vector_norm(row[1]) + row[1] /= scale[1] + shear[0] /= scale[1] + shear[1] = numpy.dot(row[0], row[2]) + row[2] -= row[0] * shear[1] + shear[2] = numpy.dot(row[1], row[2]) + row[2] -= row[1] * shear[2] + scale[2] = vector_norm(row[2]) + row[2] /= scale[2] + shear[1:] /= scale[2] + + if numpy.dot(row[0], numpy.cross(row[1], row[2])) < 0: + numpy.negative(scale, scale) + numpy.negative(row, row) + + angles[1] = math.asin(-row[0, 2]) + if math.cos(angles[1]): + angles[0] = math.atan2(row[1, 2], row[2, 2]) + angles[2] = math.atan2(row[0, 1], row[0, 0]) + else: + # angles[0] = math.atan2(row[1, 0], row[1, 1]) + angles[0] = math.atan2(-row[2, 1], row[1, 1]) + angles[2] = 0.0 + + return scale, shear, angles, translate, perspective + + +def compose_matrix(scale=None, shear=None, angles=None, translate=None, perspective=None): + """Return transformation matrix from sequence of transformations. This is the inverse of the decompose_matrix function. @@ -633,36 +625,36 @@ def compose_matrix(scale=None, shear=None, angles=None, translate=None, True """ - M = numpy.identity(4) - if perspective is not None: - P = numpy.identity(4) - P[3, :] = perspective[:4] - M = numpy.dot(M, P) - if translate is not None: - T = numpy.identity(4) - T[:3, 3] = translate[:3] - M = numpy.dot(M, T) - if angles is not None: - R = euler_matrix(angles[0], angles[1], angles[2], 'sxyz') - M = numpy.dot(M, R) - if shear is not None: - Z = numpy.identity(4) - Z[1, 2] = shear[2] - Z[0, 2] = shear[1] - Z[0, 1] = shear[0] - M = numpy.dot(M, Z) - if scale is not None: - S = numpy.identity(4) - S[0, 0] = scale[0] - S[1, 1] = scale[1] - S[2, 2] = scale[2] - M = numpy.dot(M, S) - M /= M[3, 3] - return M + M = numpy.identity(4) + if perspective is not None: + P = numpy.identity(4) + P[3, :] = perspective[:4] + M = numpy.dot(M, P) + if translate is not None: + T = numpy.identity(4) + T[:3, 3] = translate[:3] + M = numpy.dot(M, T) + if angles is not None: + R = euler_matrix(angles[0], angles[1], angles[2], 'sxyz') + M = numpy.dot(M, R) + if shear is not None: + Z = numpy.identity(4) + Z[1, 2] = shear[2] + Z[0, 2] = shear[1] + Z[0, 1] = shear[0] + M = numpy.dot(M, Z) + if scale is not None: + S = numpy.identity(4) + S[0, 0] = scale[0] + S[1, 1] = scale[1] + S[2, 2] = scale[2] + M = numpy.dot(M, S) + M /= M[3, 3] + return M def orthogonalization_matrix(lengths, angles): - """Return orthogonalization matrix for crystallographic cell coordinates. + """Return orthogonalization matrix for crystallographic cell coordinates. Angles are expected in degrees. @@ -676,20 +668,18 @@ def orthogonalization_matrix(lengths, angles): True """ - a, b, c = lengths - angles = numpy.radians(angles) - sina, sinb, _ = numpy.sin(angles) - cosa, cosb, cosg = numpy.cos(angles) - co = (cosa * cosb - cosg) / (sina * sinb) - return numpy.array([ - [ a*sinb*math.sqrt(1.0-co*co), 0.0, 0.0, 0.0], - [-a*sinb*co, b*sina, 0.0, 0.0], - [ a*cosb, b*cosa, c, 0.0], - [ 0.0, 0.0, 0.0, 1.0]]) + a, b, c = lengths + angles = numpy.radians(angles) + sina, sinb, _ = numpy.sin(angles) + cosa, cosb, cosg = numpy.cos(angles) + co = (cosa * cosb - cosg) / (sina * sinb) + return numpy.array([[a * sinb * math.sqrt(1.0 - co * co), 0.0, 0.0, 0.0], + [-a * sinb * co, b * sina, 0.0, 0.0], [a * cosb, b * cosa, c, 0.0], + [0.0, 0.0, 0.0, 1.0]]) def affine_matrix_from_points(v0, v1, shear=True, scale=True, usesvd=True): - """Return affine transform matrix to register two point sets. + """Return affine transform matrix to register two point sets. v0 and v1 are shape (ndims, \*) arrays of at least ndims non-homogeneous coordinates, where ndims is the dimensionality of the coordinate space. @@ -729,76 +719,74 @@ def affine_matrix_from_points(v0, v1, shear=True, scale=True, usesvd=True): More examples in superimposition_matrix() """ - v0 = numpy.array(v0, dtype=numpy.float64, copy=True) - v1 = numpy.array(v1, dtype=numpy.float64, copy=True) - - ndims = v0.shape[0] - if ndims < 2 or v0.shape[1] < ndims or v0.shape != v1.shape: - raise ValueError('input arrays are of wrong shape or type') - - # move centroids to origin - t0 = -numpy.mean(v0, axis=1) - M0 = numpy.identity(ndims+1) - M0[:ndims, ndims] = t0 - v0 += t0.reshape(ndims, 1) - t1 = -numpy.mean(v1, axis=1) - M1 = numpy.identity(ndims+1) - M1[:ndims, ndims] = t1 - v1 += t1.reshape(ndims, 1) - - if shear: - # Affine transformation - A = numpy.concatenate((v0, v1), axis=0) - u, s, vh = numpy.linalg.svd(A.T) - vh = vh[:ndims].T - B = vh[:ndims] - C = vh[ndims:2*ndims] - t = numpy.dot(C, numpy.linalg.pinv(B)) - t = numpy.concatenate((t, numpy.zeros((ndims, 1))), axis=1) - M = numpy.vstack((t, ((0.0,)*ndims) + (1.0,))) - elif usesvd or ndims != 3: - # Rigid transformation via SVD of covariance matrix - u, s, vh = numpy.linalg.svd(numpy.dot(v1, v0.T)) - # rotation matrix from SVD orthonormal bases - R = numpy.dot(u, vh) - if numpy.linalg.det(R) < 0.0: - # R does not constitute right handed system - R -= numpy.outer(u[:, ndims-1], vh[ndims-1, :]*2.0) - s[-1] *= -1.0 - # homogeneous transformation matrix - M = numpy.identity(ndims+1) - M[:ndims, :ndims] = R - else: - # Rigid transformation matrix via quaternion - # compute symmetric matrix N - xx, yy, zz = numpy.sum(v0 * v1, axis=1) - xy, yz, zx = numpy.sum(v0 * numpy.roll(v1, -1, axis=0), axis=1) - xz, yx, zy = numpy.sum(v0 * numpy.roll(v1, -2, axis=0), axis=1) - N = [[xx+yy+zz, 0.0, 0.0, 0.0], - [yz-zy, xx-yy-zz, 0.0, 0.0], - [zx-xz, xy+yx, yy-xx-zz, 0.0], - [xy-yx, zx+xz, yz+zy, zz-xx-yy]] - # quaternion: eigenvector corresponding to most positive eigenvalue - w, V = numpy.linalg.eigh(N) - q = V[:, numpy.argmax(w)] - q /= vector_norm(q) # unit quaternion - # homogeneous transformation matrix - M = quaternion_matrix(q) - - if scale and not shear: - # Affine transformation; scale is ratio of RMS deviations from centroid - v0 *= v0 - v1 *= v1 - M[:ndims, :ndims] *= math.sqrt(numpy.sum(v1) / numpy.sum(v0)) - - # move centroids back - M = numpy.dot(numpy.linalg.inv(M1), numpy.dot(M, M0)) - M /= M[ndims, ndims] - return M + v0 = numpy.array(v0, dtype=numpy.float64, copy=True) + v1 = numpy.array(v1, dtype=numpy.float64, copy=True) + + ndims = v0.shape[0] + if ndims < 2 or v0.shape[1] < ndims or v0.shape != v1.shape: + raise ValueError('input arrays are of wrong shape or type') + + # move centroids to origin + t0 = -numpy.mean(v0, axis=1) + M0 = numpy.identity(ndims + 1) + M0[:ndims, ndims] = t0 + v0 += t0.reshape(ndims, 1) + t1 = -numpy.mean(v1, axis=1) + M1 = numpy.identity(ndims + 1) + M1[:ndims, ndims] = t1 + v1 += t1.reshape(ndims, 1) + + if shear: + # Affine transformation + A = numpy.concatenate((v0, v1), axis=0) + u, s, vh = numpy.linalg.svd(A.T) + vh = vh[:ndims].T + B = vh[:ndims] + C = vh[ndims:2 * ndims] + t = numpy.dot(C, numpy.linalg.pinv(B)) + t = numpy.concatenate((t, numpy.zeros((ndims, 1))), axis=1) + M = numpy.vstack((t, ((0.0,) * ndims) + (1.0,))) + elif usesvd or ndims != 3: + # Rigid transformation via SVD of covariance matrix + u, s, vh = numpy.linalg.svd(numpy.dot(v1, v0.T)) + # rotation matrix from SVD orthonormal bases + R = numpy.dot(u, vh) + if numpy.linalg.det(R) < 0.0: + # R does not constitute right handed system + R -= numpy.outer(u[:, ndims - 1], vh[ndims - 1, :] * 2.0) + s[-1] *= -1.0 + # homogeneous transformation matrix + M = numpy.identity(ndims + 1) + M[:ndims, :ndims] = R + else: + # Rigid transformation matrix via quaternion + # compute symmetric matrix N + xx, yy, zz = numpy.sum(v0 * v1, axis=1) + xy, yz, zx = numpy.sum(v0 * numpy.roll(v1, -1, axis=0), axis=1) + xz, yx, zy = numpy.sum(v0 * numpy.roll(v1, -2, axis=0), axis=1) + N = [[xx + yy + zz, 0.0, 0.0, 0.0], [yz - zy, xx - yy - zz, 0.0, 0.0], + [zx - xz, xy + yx, yy - xx - zz, 0.0], [xy - yx, zx + xz, yz + zy, zz - xx - yy]] + # quaternion: eigenvector corresponding to most positive eigenvalue + w, V = numpy.linalg.eigh(N) + q = V[:, numpy.argmax(w)] + q /= vector_norm(q) # unit quaternion + # homogeneous transformation matrix + M = quaternion_matrix(q) + + if scale and not shear: + # Affine transformation; scale is ratio of RMS deviations from centroid + v0 *= v0 + v1 *= v1 + M[:ndims, :ndims] *= math.sqrt(numpy.sum(v1) / numpy.sum(v0)) + + # move centroids back + M = numpy.dot(numpy.linalg.inv(M1), numpy.dot(M, M0)) + M /= M[ndims, ndims] + return M def superimposition_matrix(v0, v1, scale=False, usesvd=True): - """Return matrix to transform given 3D point set into second point set. + """Return matrix to transform given 3D point set into second point set. v0 and v1 are shape (3, \*) or (4, \*) arrays of at least 3 points. @@ -842,14 +830,13 @@ def superimposition_matrix(v0, v1, scale=False, usesvd=True): True """ - v0 = numpy.array(v0, dtype=numpy.float64, copy=False)[:3] - v1 = numpy.array(v1, dtype=numpy.float64, copy=False)[:3] - return affine_matrix_from_points(v0, v1, shear=False, - scale=scale, usesvd=usesvd) + v0 = numpy.array(v0, dtype=numpy.float64, copy=False)[:3] + v1 = numpy.array(v1, dtype=numpy.float64, copy=False)[:3] + return affine_matrix_from_points(v0, v1, shear=False, scale=scale, usesvd=usesvd) def euler_matrix(ai, aj, ak, axes='sxyz'): - """Return homogeneous rotation matrix from Euler angles and axis sequence. + """Return homogeneous rotation matrix from Euler angles and axis sequence. ai, aj, ak : Euler's roll, pitch and yaw angles axes : One of 24 axis sequences as string or encoded tuple @@ -867,52 +854,52 @@ def euler_matrix(ai, aj, ak, axes='sxyz'): ... R = euler_matrix(ai, aj, ak, axes) """ - try: - firstaxis, parity, repetition, frame = _AXES2TUPLE[axes] - except (AttributeError, KeyError): - _TUPLE2AXES[axes] # noqa: validation - firstaxis, parity, repetition, frame = axes - - i = firstaxis - j = _NEXT_AXIS[i+parity] - k = _NEXT_AXIS[i-parity+1] - - if frame: - ai, ak = ak, ai - if parity: - ai, aj, ak = -ai, -aj, -ak - - si, sj, sk = math.sin(ai), math.sin(aj), math.sin(ak) - ci, cj, ck = math.cos(ai), math.cos(aj), math.cos(ak) - cc, cs = ci*ck, ci*sk - sc, ss = si*ck, si*sk - - M = numpy.identity(4) - if repetition: - M[i, i] = cj - M[i, j] = sj*si - M[i, k] = sj*ci - M[j, i] = sj*sk - M[j, j] = -cj*ss+cc - M[j, k] = -cj*cs-sc - M[k, i] = -sj*ck - M[k, j] = cj*sc+cs - M[k, k] = cj*cc-ss - else: - M[i, i] = cj*ck - M[i, j] = sj*sc-cs - M[i, k] = sj*cc+ss - M[j, i] = cj*sk - M[j, j] = sj*ss+cc - M[j, k] = sj*cs-sc - M[k, i] = -sj - M[k, j] = cj*si - M[k, k] = cj*ci - return M + try: + firstaxis, parity, repetition, frame = _AXES2TUPLE[axes] + except (AttributeError, KeyError): + _TUPLE2AXES[axes] # noqa: validation + firstaxis, parity, repetition, frame = axes + + i = firstaxis + j = _NEXT_AXIS[i + parity] + k = _NEXT_AXIS[i - parity + 1] + + if frame: + ai, ak = ak, ai + if parity: + ai, aj, ak = -ai, -aj, -ak + + si, sj, sk = math.sin(ai), math.sin(aj), math.sin(ak) + ci, cj, ck = math.cos(ai), math.cos(aj), math.cos(ak) + cc, cs = ci * ck, ci * sk + sc, ss = si * ck, si * sk + + M = numpy.identity(4) + if repetition: + M[i, i] = cj + M[i, j] = sj * si + M[i, k] = sj * ci + M[j, i] = sj * sk + M[j, j] = -cj * ss + cc + M[j, k] = -cj * cs - sc + M[k, i] = -sj * ck + M[k, j] = cj * sc + cs + M[k, k] = cj * cc - ss + else: + M[i, i] = cj * ck + M[i, j] = sj * sc - cs + M[i, k] = sj * cc + ss + M[j, i] = cj * sk + M[j, j] = sj * ss + cc + M[j, k] = sj * cs - sc + M[k, i] = -sj + M[k, j] = cj * si + M[k, k] = cj * ci + return M def euler_from_matrix(matrix, axes='sxyz'): - """Return Euler angles from rotation matrix for specified axis sequence. + """Return Euler angles from rotation matrix for specified axis sequence. axes : One of 24 axis sequences as string or encoded tuple @@ -930,58 +917,58 @@ def euler_from_matrix(matrix, axes='sxyz'): ... if not numpy.allclose(R0, R1): print(axes, "failed") """ - try: - firstaxis, parity, repetition, frame = _AXES2TUPLE[axes.lower()] - except (AttributeError, KeyError): - _TUPLE2AXES[axes] # noqa: validation - firstaxis, parity, repetition, frame = axes - - i = firstaxis - j = _NEXT_AXIS[i+parity] - k = _NEXT_AXIS[i-parity+1] - - M = numpy.array(matrix, dtype=numpy.float64, copy=False)[:3, :3] - if repetition: - sy = math.sqrt(M[i, j]*M[i, j] + M[i, k]*M[i, k]) - if sy > _EPS: - ax = math.atan2( M[i, j], M[i, k]) - ay = math.atan2( sy, M[i, i]) - az = math.atan2( M[j, i], -M[k, i]) - else: - ax = math.atan2(-M[j, k], M[j, j]) - ay = math.atan2( sy, M[i, i]) - az = 0.0 + try: + firstaxis, parity, repetition, frame = _AXES2TUPLE[axes.lower()] + except (AttributeError, KeyError): + _TUPLE2AXES[axes] # noqa: validation + firstaxis, parity, repetition, frame = axes + + i = firstaxis + j = _NEXT_AXIS[i + parity] + k = _NEXT_AXIS[i - parity + 1] + + M = numpy.array(matrix, dtype=numpy.float64, copy=False)[:3, :3] + if repetition: + sy = math.sqrt(M[i, j] * M[i, j] + M[i, k] * M[i, k]) + if sy > _EPS: + ax = math.atan2(M[i, j], M[i, k]) + ay = math.atan2(sy, M[i, i]) + az = math.atan2(M[j, i], -M[k, i]) + else: + ax = math.atan2(-M[j, k], M[j, j]) + ay = math.atan2(sy, M[i, i]) + az = 0.0 + else: + cy = math.sqrt(M[i, i] * M[i, i] + M[j, i] * M[j, i]) + if cy > _EPS: + ax = math.atan2(M[k, j], M[k, k]) + ay = math.atan2(-M[k, i], cy) + az = math.atan2(M[j, i], M[i, i]) else: - cy = math.sqrt(M[i, i]*M[i, i] + M[j, i]*M[j, i]) - if cy > _EPS: - ax = math.atan2( M[k, j], M[k, k]) - ay = math.atan2(-M[k, i], cy) - az = math.atan2( M[j, i], M[i, i]) - else: - ax = math.atan2(-M[j, k], M[j, j]) - ay = math.atan2(-M[k, i], cy) - az = 0.0 - - if parity: - ax, ay, az = -ax, -ay, -az - if frame: - ax, az = az, ax - return ax, ay, az + ax = math.atan2(-M[j, k], M[j, j]) + ay = math.atan2(-M[k, i], cy) + az = 0.0 + + if parity: + ax, ay, az = -ax, -ay, -az + if frame: + ax, az = az, ax + return ax, ay, az def euler_from_quaternion(quaternion, axes='sxyz'): - """Return Euler angles from quaternion for specified axis sequence. + """Return Euler angles from quaternion for specified axis sequence. >>> angles = euler_from_quaternion([0.99810947, 0.06146124, 0, 0]) >>> numpy.allclose(angles, [0.123, 0, 0]) True """ - return euler_from_matrix(quaternion_matrix(quaternion), axes) + return euler_from_matrix(quaternion_matrix(quaternion), axes) def quaternion_from_euler(ai, aj, ak, axes='sxyz'): - """Return quaternion from Euler angles and axis sequence. + """Return quaternion from Euler angles and axis sequence. ai, aj, ak : Euler's roll, pitch and yaw angles axes : One of 24 axis sequences as string or encoded tuple @@ -991,70 +978,70 @@ def quaternion_from_euler(ai, aj, ak, axes='sxyz'): True """ - try: - firstaxis, parity, repetition, frame = _AXES2TUPLE[axes.lower()] - except (AttributeError, KeyError): - _TUPLE2AXES[axes] # noqa: validation - firstaxis, parity, repetition, frame = axes - - i = firstaxis + 1 - j = _NEXT_AXIS[i+parity-1] + 1 - k = _NEXT_AXIS[i-parity] + 1 - - if frame: - ai, ak = ak, ai - if parity: - aj = -aj - - ai /= 2.0 - aj /= 2.0 - ak /= 2.0 - ci = math.cos(ai) - si = math.sin(ai) - cj = math.cos(aj) - sj = math.sin(aj) - ck = math.cos(ak) - sk = math.sin(ak) - cc = ci*ck - cs = ci*sk - sc = si*ck - ss = si*sk - - q = numpy.empty((4, )) - if repetition: - q[0] = cj*(cc - ss) - q[i] = cj*(cs + sc) - q[j] = sj*(cc + ss) - q[k] = sj*(cs - sc) - else: - q[0] = cj*cc + sj*ss - q[i] = cj*sc - sj*cs - q[j] = cj*ss + sj*cc - q[k] = cj*cs - sj*sc - if parity: - q[j] *= -1.0 - - return q + try: + firstaxis, parity, repetition, frame = _AXES2TUPLE[axes.lower()] + except (AttributeError, KeyError): + _TUPLE2AXES[axes] # noqa: validation + firstaxis, parity, repetition, frame = axes + + i = firstaxis + 1 + j = _NEXT_AXIS[i + parity - 1] + 1 + k = _NEXT_AXIS[i - parity] + 1 + + if frame: + ai, ak = ak, ai + if parity: + aj = -aj + + ai /= 2.0 + aj /= 2.0 + ak /= 2.0 + ci = math.cos(ai) + si = math.sin(ai) + cj = math.cos(aj) + sj = math.sin(aj) + ck = math.cos(ak) + sk = math.sin(ak) + cc = ci * ck + cs = ci * sk + sc = si * ck + ss = si * sk + + q = numpy.empty((4,)) + if repetition: + q[0] = cj * (cc - ss) + q[i] = cj * (cs + sc) + q[j] = sj * (cc + ss) + q[k] = sj * (cs - sc) + else: + q[0] = cj * cc + sj * ss + q[i] = cj * sc - sj * cs + q[j] = cj * ss + sj * cc + q[k] = cj * cs - sj * sc + if parity: + q[j] *= -1.0 + + return q def quaternion_about_axis(angle, axis): - """Return quaternion for rotation about axis. + """Return quaternion for rotation about axis. >>> q = quaternion_about_axis(0.123, [1, 0, 0]) >>> numpy.allclose(q, [0.99810947, 0.06146124, 0, 0]) True """ - q = numpy.array([0.0, axis[0], axis[1], axis[2]]) - qlen = vector_norm(q) - if qlen > _EPS: - q *= math.sin(angle/2.0) / qlen - q[0] = math.cos(angle/2.0) - return q + q = numpy.array([0.0, axis[0], axis[1], axis[2]]) + qlen = vector_norm(q) + if qlen > _EPS: + q *= math.sin(angle / 2.0) / qlen + q[0] = math.cos(angle / 2.0) + return q def quaternion_matrix(quaternion): - """Return homogeneous rotation matrix from quaternion. + """Return homogeneous rotation matrix from quaternion. >>> M = quaternion_matrix([0.99810947, 0.06146124, 0, 0]) >>> numpy.allclose(M, rotation_matrix(0.123, [1, 0, 0])) @@ -1067,21 +1054,20 @@ def quaternion_matrix(quaternion): True """ - q = numpy.array(quaternion, dtype=numpy.float64, copy=True) - n = numpy.dot(q, q) - if n < _EPS: - return numpy.identity(4) - q *= math.sqrt(2.0 / n) - q = numpy.outer(q, q) - return numpy.array([ - [1.0-q[2, 2]-q[3, 3], q[1, 2]-q[3, 0], q[1, 3]+q[2, 0], 0.0], - [ q[1, 2]+q[3, 0], 1.0-q[1, 1]-q[3, 3], q[2, 3]-q[1, 0], 0.0], - [ q[1, 3]-q[2, 0], q[2, 3]+q[1, 0], 1.0-q[1, 1]-q[2, 2], 0.0], - [ 0.0, 0.0, 0.0, 1.0]]) + q = numpy.array(quaternion, dtype=numpy.float64, copy=True) + n = numpy.dot(q, q) + if n < _EPS: + return numpy.identity(4) + q *= math.sqrt(2.0 / n) + q = numpy.outer(q, q) + return numpy.array([[1.0 - q[2, 2] - q[3, 3], q[1, 2] - q[3, 0], q[1, 3] + q[2, 0], 0.0], + [q[1, 2] + q[3, 0], 1.0 - q[1, 1] - q[3, 3], q[2, 3] - q[1, 0], 0.0], + [q[1, 3] - q[2, 0], q[2, 3] + q[1, 0], 1.0 - q[1, 1] - q[2, 2], 0.0], + [0.0, 0.0, 0.0, 1.0]]) def quaternion_from_matrix(matrix, isprecise=False): - """Return quaternion from rotation matrix. + """Return quaternion from rotation matrix. If isprecise is True, the input matrix is assumed to be a precise rotation matrix and a faster algorithm is used. @@ -1119,71 +1105,70 @@ def quaternion_from_matrix(matrix, isprecise=False): True """ - M = numpy.array(matrix, dtype=numpy.float64, copy=False)[:4, :4] - if isprecise: - q = numpy.empty((4, )) - t = numpy.trace(M) - if t > M[3, 3]: - q[0] = t - q[3] = M[1, 0] - M[0, 1] - q[2] = M[0, 2] - M[2, 0] - q[1] = M[2, 1] - M[1, 2] - else: - i, j, k = 0, 1, 2 - if M[1, 1] > M[0, 0]: - i, j, k = 1, 2, 0 - if M[2, 2] > M[i, i]: - i, j, k = 2, 0, 1 - t = M[i, i] - (M[j, j] + M[k, k]) + M[3, 3] - q[i] = t - q[j] = M[i, j] + M[j, i] - q[k] = M[k, i] + M[i, k] - q[3] = M[k, j] - M[j, k] - q = q[[3, 0, 1, 2]] - q *= 0.5 / math.sqrt(t * M[3, 3]) + M = numpy.array(matrix, dtype=numpy.float64, copy=False)[:4, :4] + if isprecise: + q = numpy.empty((4,)) + t = numpy.trace(M) + if t > M[3, 3]: + q[0] = t + q[3] = M[1, 0] - M[0, 1] + q[2] = M[0, 2] - M[2, 0] + q[1] = M[2, 1] - M[1, 2] else: - m00 = M[0, 0] - m01 = M[0, 1] - m02 = M[0, 2] - m10 = M[1, 0] - m11 = M[1, 1] - m12 = M[1, 2] - m20 = M[2, 0] - m21 = M[2, 1] - m22 = M[2, 2] - # symmetric matrix K - K = numpy.array([[m00-m11-m22, 0.0, 0.0, 0.0], - [m01+m10, m11-m00-m22, 0.0, 0.0], - [m02+m20, m12+m21, m22-m00-m11, 0.0], - [m21-m12, m02-m20, m10-m01, m00+m11+m22]]) - K /= 3.0 - # quaternion is eigenvector of K that corresponds to largest eigenvalue - w, V = numpy.linalg.eigh(K) - q = V[[3, 0, 1, 2], numpy.argmax(w)] - if q[0] < 0.0: - numpy.negative(q, q) - return q + i, j, k = 0, 1, 2 + if M[1, 1] > M[0, 0]: + i, j, k = 1, 2, 0 + if M[2, 2] > M[i, i]: + i, j, k = 2, 0, 1 + t = M[i, i] - (M[j, j] + M[k, k]) + M[3, 3] + q[i] = t + q[j] = M[i, j] + M[j, i] + q[k] = M[k, i] + M[i, k] + q[3] = M[k, j] - M[j, k] + q = q[[3, 0, 1, 2]] + q *= 0.5 / math.sqrt(t * M[3, 3]) + else: + m00 = M[0, 0] + m01 = M[0, 1] + m02 = M[0, 2] + m10 = M[1, 0] + m11 = M[1, 1] + m12 = M[1, 2] + m20 = M[2, 0] + m21 = M[2, 1] + m22 = M[2, 2] + # symmetric matrix K + K = numpy.array([[m00 - m11 - m22, 0.0, 0.0, 0.0], [m01 + m10, m11 - m00 - m22, 0.0, 0.0], + [m02 + m20, m12 + m21, m22 - m00 - m11, 0.0], + [m21 - m12, m02 - m20, m10 - m01, m00 + m11 + m22]]) + K /= 3.0 + # quaternion is eigenvector of K that corresponds to largest eigenvalue + w, V = numpy.linalg.eigh(K) + q = V[[3, 0, 1, 2], numpy.argmax(w)] + if q[0] < 0.0: + numpy.negative(q, q) + return q def quaternion_multiply(quaternion1, quaternion0): - """Return multiplication of two quaternions. + """Return multiplication of two quaternions. >>> q = quaternion_multiply([4, 1, -2, 3], [8, -5, 6, 7]) >>> numpy.allclose(q, [28, -44, -14, 48]) True """ - w0, x0, y0, z0 = quaternion0 - w1, x1, y1, z1 = quaternion1 - return numpy.array([ - -x1*x0 - y1*y0 - z1*z0 + w1*w0, - x1*w0 + y1*z0 - z1*y0 + w1*x0, - -x1*z0 + y1*w0 + z1*x0 + w1*y0, - x1*y0 - y1*x0 + z1*w0 + w1*z0], dtype=numpy.float64) + w0, x0, y0, z0 = quaternion0 + w1, x1, y1, z1 = quaternion1 + return numpy.array([ + -x1 * x0 - y1 * y0 - z1 * z0 + w1 * w0, x1 * w0 + y1 * z0 - z1 * y0 + w1 * x0, + -x1 * z0 + y1 * w0 + z1 * x0 + w1 * y0, x1 * y0 - y1 * x0 + z1 * w0 + w1 * z0 + ], + dtype=numpy.float64) def quaternion_conjugate(quaternion): - """Return conjugate of quaternion. + """Return conjugate of quaternion. >>> q0 = random_quaternion() >>> q1 = quaternion_conjugate(q0) @@ -1191,13 +1176,13 @@ def quaternion_conjugate(quaternion): True """ - q = numpy.array(quaternion, dtype=numpy.float64, copy=True) - numpy.negative(q[1:], q[1:]) - return q + q = numpy.array(quaternion, dtype=numpy.float64, copy=True) + numpy.negative(q[1:], q[1:]) + return q def quaternion_inverse(quaternion): - """Return inverse of quaternion. + """Return inverse of quaternion. >>> q0 = random_quaternion() >>> q1 = quaternion_inverse(q0) @@ -1205,33 +1190,33 @@ def quaternion_inverse(quaternion): True """ - q = numpy.array(quaternion, dtype=numpy.float64, copy=True) - numpy.negative(q[1:], q[1:]) - return q / numpy.dot(q, q) + q = numpy.array(quaternion, dtype=numpy.float64, copy=True) + numpy.negative(q[1:], q[1:]) + return q / numpy.dot(q, q) def quaternion_real(quaternion): - """Return real part of quaternion. + """Return real part of quaternion. >>> quaternion_real([3, 0, 1, 2]) 3.0 """ - return float(quaternion[0]) + return float(quaternion[0]) def quaternion_imag(quaternion): - """Return imaginary part of quaternion. + """Return imaginary part of quaternion. >>> quaternion_imag([3, 0, 1, 2]) array([ 0., 1., 2.]) """ - return numpy.array(quaternion[1:4], dtype=numpy.float64, copy=True) + return numpy.array(quaternion[1:4], dtype=numpy.float64, copy=True) def quaternion_slerp(quat0, quat1, fraction, spin=0, shortestpath=True): - """Return spherical linear interpolation between two quaternions. + """Return spherical linear interpolation between two quaternions. >>> q0 = random_quaternion() >>> q1 = random_quaternion() @@ -1248,31 +1233,31 @@ def quaternion_slerp(quat0, quat1, fraction, spin=0, shortestpath=True): True """ - q0 = unit_vector(quat0[:4]) - q1 = unit_vector(quat1[:4]) - if fraction == 0.0: - return q0 - elif fraction == 1.0: - return q1 - d = numpy.dot(q0, q1) - if abs(abs(d) - 1.0) < _EPS: - return q0 - if shortestpath and d < 0.0: - # invert rotation - d = -d - numpy.negative(q1, q1) - angle = math.acos(d) + spin * math.pi - if abs(angle) < _EPS: - return q0 - isin = 1.0 / math.sin(angle) - q0 *= math.sin((1.0 - fraction) * angle) * isin - q1 *= math.sin(fraction * angle) * isin - q0 += q1 + q0 = unit_vector(quat0[:4]) + q1 = unit_vector(quat1[:4]) + if fraction == 0.0: + return q0 + elif fraction == 1.0: + return q1 + d = numpy.dot(q0, q1) + if abs(abs(d) - 1.0) < _EPS: + return q0 + if shortestpath and d < 0.0: + # invert rotation + d = -d + numpy.negative(q1, q1) + angle = math.acos(d) + spin * math.pi + if abs(angle) < _EPS: return q0 + isin = 1.0 / math.sin(angle) + q0 *= math.sin((1.0 - fraction) * angle) * isin + q1 *= math.sin(fraction * angle) * isin + q0 += q1 + return q0 def random_quaternion(rand=None): - """Return uniform random unit quaternion. + """Return uniform random unit quaternion. rand: array like or None Three independent random variables that are uniformly distributed @@ -1286,21 +1271,24 @@ def random_quaternion(rand=None): (1, True) """ - if rand is None: - rand = numpy.random.rand(3) - else: - assert len(rand) == 3 - r1 = numpy.sqrt(1.0 - rand[0]) - r2 = numpy.sqrt(rand[0]) - pi2 = math.pi * 2.0 - t1 = pi2 * rand[1] - t2 = pi2 * rand[2] - return numpy.array([numpy.cos(t2)*r2, numpy.sin(t1)*r1, - numpy.cos(t1)*r1, numpy.sin(t2)*r2]) + if rand is None: + rand = numpy.random.rand(3) + else: + assert len(rand) == 3 + r1 = numpy.sqrt(1.0 - rand[0]) + r2 = numpy.sqrt(rand[0]) + pi2 = math.pi * 2.0 + t1 = pi2 * rand[1] + t2 = pi2 * rand[2] + return numpy.array( + [numpy.cos(t2) * r2, + numpy.sin(t1) * r1, + numpy.cos(t1) * r1, + numpy.sin(t2) * r2]) def random_rotation_matrix(rand=None): - """Return uniform random rotation matrix. + """Return uniform random rotation matrix. rand: array like Three independent random variables that are uniformly distributed @@ -1311,7 +1299,7 @@ def random_rotation_matrix(rand=None): True """ - return quaternion_matrix(random_quaternion(rand)) + return quaternion_matrix(random_quaternion(rand)) # epsilon for testing whether a number is close to zero @@ -1322,20 +1310,37 @@ _NEXT_AXIS = [1, 2, 0, 1] # map axes strings to/from tuples of inner axis, parity, repetition, frame _AXES2TUPLE = { - 'sxyz': (0, 0, 0, 0), 'sxyx': (0, 0, 1, 0), 'sxzy': (0, 1, 0, 0), - 'sxzx': (0, 1, 1, 0), 'syzx': (1, 0, 0, 0), 'syzy': (1, 0, 1, 0), - 'syxz': (1, 1, 0, 0), 'syxy': (1, 1, 1, 0), 'szxy': (2, 0, 0, 0), - 'szxz': (2, 0, 1, 0), 'szyx': (2, 1, 0, 0), 'szyz': (2, 1, 1, 0), - 'rzyx': (0, 0, 0, 1), 'rxyx': (0, 0, 1, 1), 'ryzx': (0, 1, 0, 1), - 'rxzx': (0, 1, 1, 1), 'rxzy': (1, 0, 0, 1), 'ryzy': (1, 0, 1, 1), - 'rzxy': (1, 1, 0, 1), 'ryxy': (1, 1, 1, 1), 'ryxz': (2, 0, 0, 1), - 'rzxz': (2, 0, 1, 1), 'rxyz': (2, 1, 0, 1), 'rzyz': (2, 1, 1, 1)} + 'sxyz': (0, 0, 0, 0), + 'sxyx': (0, 0, 1, 0), + 'sxzy': (0, 1, 0, 0), + 'sxzx': (0, 1, 1, 0), + 'syzx': (1, 0, 0, 0), + 'syzy': (1, 0, 1, 0), + 'syxz': (1, 1, 0, 0), + 'syxy': (1, 1, 1, 0), + 'szxy': (2, 0, 0, 0), + 'szxz': (2, 0, 1, 0), + 'szyx': (2, 1, 0, 0), + 'szyz': (2, 1, 1, 0), + 'rzyx': (0, 0, 0, 1), + 'rxyx': (0, 0, 1, 1), + 'ryzx': (0, 1, 0, 1), + 'rxzx': (0, 1, 1, 1), + 'rxzy': (1, 0, 0, 1), + 'ryzy': (1, 0, 1, 1), + 'rzxy': (1, 1, 0, 1), + 'ryxy': (1, 1, 1, 1), + 'ryxz': (2, 0, 0, 1), + 'rzxz': (2, 0, 1, 1), + 'rxyz': (2, 1, 0, 1), + 'rzyz': (2, 1, 1, 1) +} _TUPLE2AXES = dict((v, k) for k, v in _AXES2TUPLE.items()) def vector_norm(data, axis=None, out=None): - """Return length, i.e. Euclidean norm, of ndarray along axis. + """Return length, i.e. Euclidean norm, of ndarray along axis. >>> v = numpy.random.random(3) >>> n = vector_norm(v) @@ -1359,22 +1364,22 @@ def vector_norm(data, axis=None, out=None): 1.0 """ - data = numpy.array(data, dtype=numpy.float64, copy=True) - if out is None: - if data.ndim == 1: - return math.sqrt(numpy.dot(data, data)) - data *= data - out = numpy.atleast_1d(numpy.sum(data, axis=axis)) - numpy.sqrt(out, out) - return out - else: - data *= data - numpy.sum(data, axis=axis, out=out) - numpy.sqrt(out, out) + data = numpy.array(data, dtype=numpy.float64, copy=True) + if out is None: + if data.ndim == 1: + return math.sqrt(numpy.dot(data, data)) + data *= data + out = numpy.atleast_1d(numpy.sum(data, axis=axis)) + numpy.sqrt(out, out) + return out + else: + data *= data + numpy.sum(data, axis=axis, out=out) + numpy.sqrt(out, out) def unit_vector(data, axis=None, out=None): - """Return ndarray normalized by length, i.e. Euclidean norm, along axis. + """Return ndarray normalized by length, i.e. Euclidean norm, along axis. >>> v0 = numpy.random.random(3) >>> v1 = unit_vector(v0) @@ -1399,26 +1404,26 @@ def unit_vector(data, axis=None, out=None): [1.0] """ - if out is None: - data = numpy.array(data, dtype=numpy.float64, copy=True) - if data.ndim == 1: - data /= math.sqrt(numpy.dot(data, data)) - return data - else: - if out is not data: - out[:] = numpy.array(data, copy=False) - data = out - length = numpy.atleast_1d(numpy.sum(data*data, axis)) - numpy.sqrt(length, length) - if axis is not None: - length = numpy.expand_dims(length, axis) - data /= length - if out is None: - return data + if out is None: + data = numpy.array(data, dtype=numpy.float64, copy=True) + if data.ndim == 1: + data /= math.sqrt(numpy.dot(data, data)) + return data + else: + if out is not data: + out[:] = numpy.array(data, copy=False) + data = out + length = numpy.atleast_1d(numpy.sum(data * data, axis)) + numpy.sqrt(length, length) + if axis is not None: + length = numpy.expand_dims(length, axis) + data /= length + if out is None: + return data def random_vector(size): - """Return array of random doubles in the half-open interval [0.0, 1.0). + """Return array of random doubles in the half-open interval [0.0, 1.0). >>> v = random_vector(10000) >>> numpy.all(v >= 0) and numpy.all(v < 1) @@ -1429,11 +1434,11 @@ def random_vector(size): False """ - return numpy.random.random(size) + return numpy.random.random(size) def vector_product(v0, v1, axis=0): - """Return vector perpendicular to vectors. + """Return vector perpendicular to vectors. >>> v = vector_product([2, 0, 0], [0, 3, 0]) >>> numpy.allclose(v, [0, 0, 6]) @@ -1450,11 +1455,11 @@ def vector_product(v0, v1, axis=0): True """ - return numpy.cross(v0, v1, axis=axis) + return numpy.cross(v0, v1, axis=axis) def angle_between_vectors(v0, v1, directed=True, axis=0): - """Return angle between vectors. + """Return angle between vectors. If directed is False, the input vectors are interpreted as undirected axes, i.e. the maximum angle is pi/2. @@ -1477,16 +1482,16 @@ def angle_between_vectors(v0, v1, directed=True, axis=0): True """ - v0 = numpy.array(v0, dtype=numpy.float64, copy=False) - v1 = numpy.array(v1, dtype=numpy.float64, copy=False) - dot = numpy.sum(v0 * v1, axis=axis) - dot /= vector_norm(v0, axis=axis) * vector_norm(v1, axis=axis) - dot = numpy.clip(dot, -1.0, 1.0) - return numpy.arccos(dot if directed else numpy.fabs(dot)) + v0 = numpy.array(v0, dtype=numpy.float64, copy=False) + v1 = numpy.array(v1, dtype=numpy.float64, copy=False) + dot = numpy.sum(v0 * v1, axis=axis) + dot /= vector_norm(v0, axis=axis) * vector_norm(v1, axis=axis) + dot = numpy.clip(dot, -1.0, 1.0) + return numpy.arccos(dot if directed else numpy.fabs(dot)) def inverse_matrix(matrix): - """Return inverse of square transformation matrix. + """Return inverse of square transformation matrix. >>> M0 = random_rotation_matrix() >>> M1 = inverse_matrix(M0.T) @@ -1498,11 +1503,11 @@ def inverse_matrix(matrix): ... if not numpy.allclose(M1, numpy.linalg.inv(M0)): print(size) """ - return numpy.linalg.inv(matrix) + return numpy.linalg.inv(matrix) def concatenate_matrices(*matrices): - """Return concatenation of series of transformation matrices. + """Return concatenation of series of transformation matrices. >>> M = numpy.random.rand(16).reshape((4, 4)) - 0.5 >>> numpy.allclose(M, concatenate_matrices(M)) @@ -1511,14 +1516,14 @@ def concatenate_matrices(*matrices): True """ - M = numpy.identity(4) - for i in matrices: - M = numpy.dot(M, i) - return M + M = numpy.identity(4) + for i in matrices: + M = numpy.dot(M, i) + return M def is_same_transform(matrix0, matrix1): - """Return True if two matrices perform same transformation. + """Return True if two matrices perform same transformation. >>> is_same_transform(numpy.identity(4), numpy.identity(4)) True @@ -1526,22 +1531,22 @@ def is_same_transform(matrix0, matrix1): False """ - matrix0 = numpy.array(matrix0, dtype=numpy.float64, copy=True) - matrix0 /= matrix0[3, 3] - matrix1 = numpy.array(matrix1, dtype=numpy.float64, copy=True) - matrix1 /= matrix1[3, 3] - return numpy.allclose(matrix0, matrix1) + matrix0 = numpy.array(matrix0, dtype=numpy.float64, copy=True) + matrix0 /= matrix0[3, 3] + matrix1 = numpy.array(matrix1, dtype=numpy.float64, copy=True) + matrix1 /= matrix1[3, 3] + return numpy.allclose(matrix0, matrix1) def is_same_quaternion(q0, q1): - """Return True if two quaternions are equal.""" - q0 = numpy.array(q0) - q1 = numpy.array(q1) - return numpy.allclose(q0, q1) or numpy.allclose(q0, -q1) + """Return True if two quaternions are equal.""" + q0 = numpy.array(q0) + q1 = numpy.array(q1) + return numpy.allclose(q0, q1) or numpy.allclose(q0, -q1) def _import_module(name, package=None, warn=True, postfix='_py', ignore='_'): - """Try import all public attributes from module into global namespace. + """Try import all public attributes from module into global namespace. Existing attributes with name clashes are renamed with prefix. Attributes starting with underscore are ignored by default. @@ -1549,32 +1554,31 @@ def _import_module(name, package=None, warn=True, postfix='_py', ignore='_'): Return True on successful import. """ - import warnings - from importlib import import_module - try: - if not package: - module = import_module(name) - else: - module = import_module('.' + name, package=package) - except ImportError as err: - if warn: - warnings.warn(str(err)) + import warnings + from importlib import import_module + try: + if not package: + module = import_module(name) else: - for attr in dir(module): - if ignore and attr.startswith(ignore): - continue - if postfix: - if attr in globals(): - globals()[attr + postfix] = globals()[attr] - elif warn: - warnings.warn('no Python implementation of ' + attr) - globals()[attr] = getattr(module, attr) - return True + module = import_module('.' + name, package=package) + except ImportError as err: + if warn: + warnings.warn(str(err)) + else: + for attr in dir(module): + if ignore and attr.startswith(ignore): + continue + if postfix: + if attr in globals(): + globals()[attr + postfix] = globals()[attr] + elif warn: + warnings.warn('no Python implementation of ' + attr) + globals()[attr] = getattr(module, attr) + return True # _import_module('_transformations', __package__) - # if __name__ == '__main__': # import doctest # import random # noqa: used in doctests @@ -1582,4 +1586,4 @@ def _import_module(name, package=None, warn=True, postfix='_py', ignore='_'): # numpy.set_printoptions(suppress=True, precision=5, legacy='1.13') # except TypeError: # numpy.set_printoptions(suppress=True, precision=5) -# doctest.testmod()
\ No newline at end of file +# doctest.testmod() diff --git a/examples/pybullet/gym/pybullet_envs/deep_mimic/mpi_run.py b/examples/pybullet/gym/pybullet_envs/deep_mimic/mpi_run.py index 85d70abb1..0d30f5d5e 100644 --- a/examples/pybullet/gym/pybullet_envs/deep_mimic/mpi_run.py +++ b/examples/pybullet/gym/pybullet_envs/deep_mimic/mpi_run.py @@ -3,21 +3,23 @@ import subprocess from pybullet_utils.arg_parser import ArgParser from pybullet_utils.logger import Logger + def main(): - # Command line arguments - args = sys.argv[1:] - arg_parser = ArgParser() - arg_parser.load_args(args) + # Command line arguments + args = sys.argv[1:] + arg_parser = ArgParser() + arg_parser.load_args(args) + + num_workers = arg_parser.parse_int('num_workers', 1) + assert (num_workers > 0) - num_workers = arg_parser.parse_int('num_workers', 1) - assert(num_workers > 0) + Logger.print2('Running with {:d} workers'.format(num_workers)) + cmd = 'mpiexec -n {:d} python3 DeepMimic_Optimizer.py '.format(num_workers) + cmd += ' '.join(args) + Logger.print2('cmd: ' + cmd) + subprocess.call(cmd, shell=True) + return - Logger.print2('Running with {:d} workers'.format(num_workers)) - cmd = 'mpiexec -n {:d} python3 DeepMimic_Optimizer.py '.format(num_workers) - cmd += ' '.join(args) - Logger.print2('cmd: ' + cmd) - subprocess.call(cmd, shell=True) - return if __name__ == '__main__': - main() + main() diff --git a/examples/pybullet/gym/pybullet_envs/deep_mimic/testrl.py b/examples/pybullet/gym/pybullet_envs/deep_mimic/testrl.py index 38a9469e9..1693fe7d4 100644 --- a/examples/pybullet/gym/pybullet_envs/deep_mimic/testrl.py +++ b/examples/pybullet/gym/pybullet_envs/deep_mimic/testrl.py @@ -2,8 +2,8 @@ import os import inspect currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe()))) parentdir = os.path.dirname(os.path.dirname(currentdir)) -os.sys.path.insert(0,parentdir) -print("parentdir=",parentdir) +os.sys.path.insert(0, parentdir) +print("parentdir=", parentdir) import json from pybullet_envs.deep_mimic.learning.rl_world import RLWorld from pybullet_envs.deep_mimic.learning.ppo_agent import PPOAgent @@ -15,81 +15,78 @@ from pybullet_envs.deep_mimic.env.pybullet_deep_mimic_env import PyBulletDeepMim import sys import random -update_timestep = 1./240. +update_timestep = 1. / 240. animating = True + def update_world(world, time_elapsed): - timeStep = update_timestep - world.update(timeStep) - reward = world.env.calc_reward(agent_id=0) - #print("reward=",reward) - end_episode = world.env.is_episode_end() - if (end_episode): - world.end_episode() - world.reset() - return + timeStep = update_timestep + world.update(timeStep) + reward = world.env.calc_reward(agent_id=0) + #print("reward=",reward) + end_episode = world.env.is_episode_end() + if (end_episode): + world.end_episode() + world.reset() + return + def build_arg_parser(args): - arg_parser = ArgParser() - arg_parser.load_args(args) + arg_parser = ArgParser() + arg_parser.load_args(args) - arg_file = arg_parser.parse_string('arg_file', '') - if (arg_file != ''): - path = pybullet_data.getDataPath()+"/args/"+arg_file - succ = arg_parser.load_file(path) - Logger.print2(arg_file) - assert succ, Logger.print2('Failed to load args from: ' + arg_file) - return arg_parser + arg_file = arg_parser.parse_string('arg_file', '') + if (arg_file != ''): + path = pybullet_data.getDataPath() + "/args/" + arg_file + succ = arg_parser.load_file(path) + Logger.print2(arg_file) + assert succ, Logger.print2('Failed to load args from: ' + arg_file) + return arg_parser -args = sys.argv[1:] +args = sys.argv[1:] def build_world(args, enable_draw): - arg_parser = build_arg_parser(args) - print("enable_draw=",enable_draw) - env = PyBulletDeepMimicEnv(arg_parser, enable_draw) - world = RLWorld(env, arg_parser) - #world.env.set_playback_speed(playback_speed) - - motion_file = arg_parser.parse_string("motion_file") - print("motion_file=",motion_file) - bodies = arg_parser.parse_ints("fall_contact_bodies") - print("bodies=",bodies) - int_output_path = arg_parser.parse_string("int_output_path") - print("int_output_path=",int_output_path) - agent_files = pybullet_data.getDataPath()+"/"+arg_parser.parse_string("agent_files") - - AGENT_TYPE_KEY = "AgentType" - - print("agent_file=",agent_files) - with open(agent_files) as data_file: - json_data = json.load(data_file) - print("json_data=",json_data) - assert AGENT_TYPE_KEY in json_data - agent_type = json_data[AGENT_TYPE_KEY] - print("agent_type=",agent_type) - agent = PPOAgent(world, id, json_data) - - agent.set_enable_training(False) - world.reset() - return world - - - - - + arg_parser = build_arg_parser(args) + print("enable_draw=", enable_draw) + env = PyBulletDeepMimicEnv(arg_parser, enable_draw) + world = RLWorld(env, arg_parser) + #world.env.set_playback_speed(playback_speed) + + motion_file = arg_parser.parse_string("motion_file") + print("motion_file=", motion_file) + bodies = arg_parser.parse_ints("fall_contact_bodies") + print("bodies=", bodies) + int_output_path = arg_parser.parse_string("int_output_path") + print("int_output_path=", int_output_path) + agent_files = pybullet_data.getDataPath() + "/" + arg_parser.parse_string("agent_files") + + AGENT_TYPE_KEY = "AgentType" + + print("agent_file=", agent_files) + with open(agent_files) as data_file: + json_data = json.load(data_file) + print("json_data=", json_data) + assert AGENT_TYPE_KEY in json_data + agent_type = json_data[AGENT_TYPE_KEY] + print("agent_type=", agent_type) + agent = PPOAgent(world, id, json_data) + + agent.set_enable_training(False) + world.reset() + return world + + if __name__ == '__main__': - - world = build_world(args, True) - while (world.env._pybullet_client.isConnected()): - timeStep = update_timestep - keys = world.env.getKeyboardEvents() - - - if world.env.isKeyTriggered(keys, ' '): - animating = not animating - if (animating): - update_world(world, timeStep) - #animating=False + world = build_world(args, True) + while (world.env._pybullet_client.isConnected()): + timeStep = update_timestep + keys = world.env.getKeyboardEvents() + + if world.env.isKeyTriggered(keys, ' '): + animating = not animating + if (animating): + update_world(world, timeStep) + #animating=False diff --git a/examples/pybullet/gym/pybullet_envs/env_bases.py b/examples/pybullet/gym/pybullet_envs/env_bases.py index aeef7fda5..a352ebe4c 100644 --- a/examples/pybullet/gym/pybullet_envs/env_bases.py +++ b/examples/pybullet/gym/pybullet_envs/env_bases.py @@ -5,134 +5,134 @@ from pybullet_utils import bullet_client from pkg_resources import parse_version + class MJCFBaseBulletEnv(gym.Env): - """ + """ Base class for Bullet physics simulation loading MJCF (MuJoCo .xml) environments in a Scene. These environments create single-player scenes and behave like normal Gym environments, if you don't use multiplayer. """ - metadata = { - 'render.modes': ['human', 'rgb_array'], - 'video.frames_per_second': 60 - } - - def __init__(self, robot, render=False): - self.scene = None - self.physicsClientId = -1 - self.ownsPhysicsClient = 0 - self.camera = Camera() - self.isRender = render - self.robot = robot - self.seed() - self._cam_dist = 3 - self._cam_yaw = 0 - self._cam_pitch = -30 - self._render_width =320 - self._render_height = 240 - - self.action_space = robot.action_space - self.observation_space = robot.observation_space - - def configure(self, args): - self.robot.args = args - - def seed(self, seed=None): - self.np_random, seed = gym.utils.seeding.np_random(seed) - self.robot.np_random = self.np_random # use the same np_randomizer for robot as for env - return [seed] - - def reset(self): - if (self.physicsClientId<0): - self.ownsPhysicsClient = True - - if self.isRender: - self._p = bullet_client.BulletClient(connection_mode=pybullet.GUI) - else: - self._p = bullet_client.BulletClient() - - self.physicsClientId = self._p._client - self._p.configureDebugVisualizer(pybullet.COV_ENABLE_GUI,0) - - if self.scene is None: - self.scene = self.create_single_player_scene(self._p) - if not self.scene.multiplayer and self.ownsPhysicsClient: - self.scene.episode_restart(self._p) - - self.robot.scene = self.scene - - self.frame = 0 - self.done = 0 - self.reward = 0 - dump = 0 - s = self.robot.reset(self._p) - self.potential = self.robot.calc_potential() - return s - - def render(self, mode='human', close=False): - if mode == "human": - self.isRender = True - if mode != "rgb_array": - return np.array([]) - - base_pos=[0,0,0] - if (hasattr(self,'robot')): - if (hasattr(self.robot,'body_xyz')): - base_pos = self.robot.body_xyz - - view_matrix = self._p.computeViewMatrixFromYawPitchRoll( - cameraTargetPosition=base_pos, - distance=self._cam_dist, - yaw=self._cam_yaw, - pitch=self._cam_pitch, - roll=0, - upAxisIndex=2) - proj_matrix = self._p.computeProjectionMatrixFOV( - fov=60, aspect=float(self._render_width)/self._render_height, - nearVal=0.1, farVal=100.0) - (_, _, px, _, _) = self._p.getCameraImage( - width=self._render_width, height=self._render_height, viewMatrix=view_matrix, - projectionMatrix=proj_matrix, - renderer=pybullet.ER_BULLET_HARDWARE_OPENGL - ) - rgb_array = np.array(px) - rgb_array = np.reshape(np.array(px), (self._render_height, self._render_width, -1)) - rgb_array = rgb_array[:, :, :3] - return rgb_array - - - def close(self): - if (self.ownsPhysicsClient): - if (self.physicsClientId>=0): - self._p.disconnect() - self.physicsClientId = -1 - - def HUD(self, state, a, done): - pass - - # def step(self, *args, **kwargs): - # if self.isRender: - # base_pos=[0,0,0] - # if (hasattr(self,'robot')): - # if (hasattr(self.robot,'body_xyz')): - # base_pos = self.robot.body_xyz - # # Keep the previous orientation of the camera set by the user. - # #[yaw, pitch, dist] = self._p.getDebugVisualizerCamera()[8:11] - # self._p.resetDebugVisualizerCamera(3,0,0, base_pos) - # - # - # return self.step(*args, **kwargs) - if parse_version(gym.__version__) < parse_version('0.9.6'): - _render = render - _reset = reset - _seed = seed + metadata = {'render.modes': ['human', 'rgb_array'], 'video.frames_per_second': 60} + + def __init__(self, robot, render=False): + self.scene = None + self.physicsClientId = -1 + self.ownsPhysicsClient = 0 + self.camera = Camera() + self.isRender = render + self.robot = robot + self.seed() + self._cam_dist = 3 + self._cam_yaw = 0 + self._cam_pitch = -30 + self._render_width = 320 + self._render_height = 240 + + self.action_space = robot.action_space + self.observation_space = robot.observation_space + + def configure(self, args): + self.robot.args = args + + def seed(self, seed=None): + self.np_random, seed = gym.utils.seeding.np_random(seed) + self.robot.np_random = self.np_random # use the same np_randomizer for robot as for env + return [seed] + + def reset(self): + if (self.physicsClientId < 0): + self.ownsPhysicsClient = True + + if self.isRender: + self._p = bullet_client.BulletClient(connection_mode=pybullet.GUI) + else: + self._p = bullet_client.BulletClient() + + self.physicsClientId = self._p._client + self._p.configureDebugVisualizer(pybullet.COV_ENABLE_GUI, 0) + + if self.scene is None: + self.scene = self.create_single_player_scene(self._p) + if not self.scene.multiplayer and self.ownsPhysicsClient: + self.scene.episode_restart(self._p) + + self.robot.scene = self.scene + + self.frame = 0 + self.done = 0 + self.reward = 0 + dump = 0 + s = self.robot.reset(self._p) + self.potential = self.robot.calc_potential() + return s + + def render(self, mode='human', close=False): + if mode == "human": + self.isRender = True + if mode != "rgb_array": + return np.array([]) + + base_pos = [0, 0, 0] + if (hasattr(self, 'robot')): + if (hasattr(self.robot, 'body_xyz')): + base_pos = self.robot.body_xyz + + view_matrix = self._p.computeViewMatrixFromYawPitchRoll(cameraTargetPosition=base_pos, + distance=self._cam_dist, + yaw=self._cam_yaw, + pitch=self._cam_pitch, + roll=0, + upAxisIndex=2) + proj_matrix = self._p.computeProjectionMatrixFOV(fov=60, + aspect=float(self._render_width) / + self._render_height, + nearVal=0.1, + farVal=100.0) + (_, _, px, _, _) = self._p.getCameraImage(width=self._render_width, + height=self._render_height, + viewMatrix=view_matrix, + projectionMatrix=proj_matrix, + renderer=pybullet.ER_BULLET_HARDWARE_OPENGL) + rgb_array = np.array(px) + rgb_array = np.reshape(np.array(px), (self._render_height, self._render_width, -1)) + rgb_array = rgb_array[:, :, :3] + return rgb_array + + def close(self): + if (self.ownsPhysicsClient): + if (self.physicsClientId >= 0): + self._p.disconnect() + self.physicsClientId = -1 + + def HUD(self, state, a, done): + pass + + # def step(self, *args, **kwargs): + # if self.isRender: + # base_pos=[0,0,0] + # if (hasattr(self,'robot')): + # if (hasattr(self.robot,'body_xyz')): + # base_pos = self.robot.body_xyz + # # Keep the previous orientation of the camera set by the user. + # #[yaw, pitch, dist] = self._p.getDebugVisualizerCamera()[8:11] + # self._p.resetDebugVisualizerCamera(3,0,0, base_pos) + # + # + # return self.step(*args, **kwargs) + if parse_version(gym.__version__) < parse_version('0.9.6'): + _render = render + _reset = reset + _seed = seed + class Camera: - def __init__(self): - pass - - def move_and_look_at(self,i,j,k,x,y,z): - lookat = [x,y,z] - distance = 10 - yaw = 10 - self._p.resetDebugVisualizerCamera(distance, yaw, -20, lookat) + + def __init__(self): + pass + + def move_and_look_at(self, i, j, k, x, y, z): + lookat = [x, y, z] + distance = 10 + yaw = 10 + self._p.resetDebugVisualizerCamera(distance, yaw, -20, lookat) diff --git a/examples/pybullet/gym/pybullet_envs/examples/dominoes.py b/examples/pybullet/gym/pybullet_envs/examples/dominoes.py index c96c07ebd..e27f6aec9 100644 --- a/examples/pybullet/gym/pybullet_envs/examples/dominoes.py +++ b/examples/pybullet/gym/pybullet_envs/examples/dominoes.py @@ -7,31 +7,38 @@ import time p = bc.BulletClient(connection_mode=pybullet.GUI) p.setAdditionalSearchPath(pd.getDataPath()) p.loadURDF("plane_transparent.urdf", useMaximalCoordinates=True) -p#.setPhysicsEngineParameter(numSolverIterations=10, fixedTimeStep=0.01) - - -p.configureDebugVisualizer(p.COV_ENABLE_PLANAR_REFLECTION,1) -p.configureDebugVisualizer(p.COV_ENABLE_RENDERING,0) - -y2z = p.getQuaternionFromEuler([0,0,1.57]) -meshScale = [1,1,1] -visualShapeId = p.createVisualShape(shapeType=p.GEOM_MESH,fileName="domino/domino.obj", rgbaColor=[1,1,1,1], specularColor=[0.4,.4,0], visualFrameOrientation=y2z, meshScale=meshScale) - -boxDimensions = [0.5*0.00635, 0.5*0.0254, 0.5*0.0508] -collisionShapeId = p.createCollisionShape(p.GEOM_BOX,halfExtents=boxDimensions) - - -for j in range (12): - print("j=",j) - for i in range (35): +p #.setPhysicsEngineParameter(numSolverIterations=10, fixedTimeStep=0.01) + +p.configureDebugVisualizer(p.COV_ENABLE_PLANAR_REFLECTION, 1) +p.configureDebugVisualizer(p.COV_ENABLE_RENDERING, 0) + +y2z = p.getQuaternionFromEuler([0, 0, 1.57]) +meshScale = [1, 1, 1] +visualShapeId = p.createVisualShape(shapeType=p.GEOM_MESH, + fileName="domino/domino.obj", + rgbaColor=[1, 1, 1, 1], + specularColor=[0.4, .4, 0], + visualFrameOrientation=y2z, + meshScale=meshScale) + +boxDimensions = [0.5 * 0.00635, 0.5 * 0.0254, 0.5 * 0.0508] +collisionShapeId = p.createCollisionShape(p.GEOM_BOX, halfExtents=boxDimensions) + +for j in range(12): + print("j=", j) + for i in range(35): #p.loadURDF("domino/domino.urdf",[i*0.04,0, 0.06]) - p.createMultiBody(baseMass=0.025,baseCollisionShapeIndex = collisionShapeId,baseVisualShapeIndex = visualShapeId, basePosition = [i*0.04,j*0.05, 0.06], useMaximalCoordinates=True) - -p.configureDebugVisualizer(p.COV_ENABLE_RENDERING,1) + p.createMultiBody(baseMass=0.025, + baseCollisionShapeIndex=collisionShapeId, + baseVisualShapeIndex=visualShapeId, + basePosition=[i * 0.04, j * 0.05, 0.06], + useMaximalCoordinates=True) + +p.configureDebugVisualizer(p.COV_ENABLE_RENDERING, 1) -p.setGravity(0,0,-9.8) +p.setGravity(0, 0, -9.8) p.setRealTimeSimulation(1) while (1): - p.setGravity(0,0,-9.8) + p.setGravity(0, 0, -9.8) #p.stepSimulation(1./100.) - time.sleep(1./240.)
\ No newline at end of file + time.sleep(1. / 240.) diff --git a/examples/pybullet/gym/pybullet_envs/examples/enjoy_TF_AntBulletEnv_v0_2017may.py b/examples/pybullet/gym/pybullet_envs/examples/enjoy_TF_AntBulletEnv_v0_2017may.py index 6b1379c16..9fc5d7962 100644 --- a/examples/pybullet/gym/pybullet_envs/examples/enjoy_TF_AntBulletEnv_v0_2017may.py +++ b/examples/pybullet/gym/pybullet_envs/examples/enjoy_TF_AntBulletEnv_v0_2017may.py @@ -3,300 +3,2167 @@ import os import inspect currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe()))) parentdir = os.path.dirname(os.path.dirname(currentdir)) -os.sys.path.insert(0,parentdir) +os.sys.path.insert(0, parentdir) import pybullet import gym import numpy as np import pybullet_envs import time + def relu(x): - return np.maximum(x, 0) + return np.maximum(x, 0) + class SmallReactivePolicy: - "Simple multi-layer perceptron policy, no internal state" - def __init__(self, observation_space, action_space): - assert weights_dense1_w.shape == (observation_space.shape[0], 128) - assert weights_dense2_w.shape == (128, 64) - assert weights_final_w.shape == (64, action_space.shape[0]) + "Simple multi-layer perceptron policy, no internal state" + + def __init__(self, observation_space, action_space): + assert weights_dense1_w.shape == (observation_space.shape[0], 128) + assert weights_dense2_w.shape == (128, 64) + assert weights_final_w.shape == (64, action_space.shape[0]) + + def act(self, ob): + x = ob + x = relu(np.dot(x, weights_dense1_w) + weights_dense1_b) + x = relu(np.dot(x, weights_dense2_w) + weights_dense2_b) + x = np.dot(x, weights_final_w) + weights_final_b + return x - def act(self, ob): - x = ob - x = relu(np.dot(x, weights_dense1_w) + weights_dense1_b) - x = relu(np.dot(x, weights_dense2_w) + weights_dense2_b) - x = np.dot(x, weights_final_w) + weights_final_b - return x def main(): - pybullet.connect(pybullet.DIRECT) - env = gym.make("AntBulletEnv-v0") - env.render(mode="human") - - pi = SmallReactivePolicy(env.observation_space, env.action_space) - env.reset() + pybullet.connect(pybullet.DIRECT) + env = gym.make("AntBulletEnv-v0") + env.render(mode="human") + + pi = SmallReactivePolicy(env.observation_space, env.action_space) + env.reset() + + while 1: + frame = 0 + score = 0 + restart_delay = 0 + obs = env.reset() while 1: - frame = 0 - score = 0 - restart_delay = 0 - obs = env.reset() - - while 1: - time.sleep(1./60.) - a = pi.act(obs) - obs, r, done, _ = env.step(a) - #print("reward") - #print(r) - score += r - frame += 1 - distance=5 - yaw = 0 + time.sleep(1. / 60.) + a = pi.act(obs) + obs, r, done, _ = env.step(a) + #print("reward") + #print(r) + score += r + frame += 1 + distance = 5 + yaw = 0 - still_open = env.render("human") - if still_open==False: - return - if not done: continue - if restart_delay==0: - print("score=%0.2f in %i frames" % (score, frame)) - restart_delay = 60*2 # 2 sec at 60 fps - else: - restart_delay -= 1 - if restart_delay==0: break + still_open = env.render("human") + if still_open == False: + return + if not done: continue + if restart_delay == 0: + print("score=%0.2f in %i frames" % (score, frame)) + restart_delay = 60 * 2 # 2 sec at 60 fps + else: + restart_delay -= 1 + if restart_delay == 0: break -weights_dense1_w = np.array([ -[ +0.2856, -0.4091, +0.3380, -0.1083, +0.1049, -0.0806, -0.0827, +0.0402, +0.2415, +0.0927, +0.1812, +0.5455, +0.3157, +0.1043, +0.0527, +0.3768, -0.2083, +0.1218, -0.0633, +0.3236, +0.2198, +0.0513, +0.3505, -0.1165, -0.1732, +0.2760, +0.0006, +0.0249, +0.5887, -0.2324, +0.1503, -0.0108, +0.1220, +0.2527, +0.0984, +0.5360, -0.0579, -0.0847, +0.1649, +0.0589, -0.2046, +0.5981, -0.1063, -0.1088, +0.3424, +0.3479, -0.1751, +0.2924, -0.0610, +0.0807, +0.3291, +0.0059, +0.0339, -0.2823, +0.2533, +0.3156, +0.1679, +0.2417, +0.1265, +0.0024, +0.0802, +0.2531, -0.2576, +0.3894, +0.3206, +0.2015, +0.3482, +0.1042, -0.2418, -0.0002, +0.1277, +0.1811, +0.1551, +0.5460, +0.1714, +0.1021, -0.1252, 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+0.1321, -0.1934, -0.2208, +0.0057, +0.1504, +0.3798], + [+0.1586, +0.0557, +0.1042, -0.4201, +0.0613, -0.0152, -0.3855, +0.1035], + [-0.0969, -0.0936, +0.1289, -0.4031, +0.1148, +0.3258, +0.2017, -0.2499], + [+0.5422, -0.3985, -0.0758, -0.0241, +0.1124, +0.0043, -0.0495, -0.1127], + [+0.1679, -0.1337, -0.2102, -0.0678, +0.0768, -0.5881, +0.0327, -0.1429], + [+0.2756, -0.1772, -0.3729, -0.0931, +0.6402, -0.0739, -0.1221, +0.5423], + [-0.2310, -0.6409, -0.0729, -0.2143, +0.0735, -0.3279, -0.1317, -0.2060], + [-0.0864, +0.0344, -0.5635, +0.3137, -0.0566, -0.3874, -0.0119, +0.0187], + [+0.4066, +0.1192, +0.2065, +0.0546, +0.2182, +0.6743, +0.2802, -0.0638], + [+0.2526, +0.6688, -0.0943, -0.2531, +0.1259, +0.1115, +0.0581, -0.0340], + [+0.1820, +0.2927, -0.2457, +0.0310, -0.3335, -0.5889, +0.0088, +0.3045], + [+0.0072, -0.0702, +0.1246, +0.0875, +0.0488, +0.2379, -0.1760, -0.5422], + [+0.1416, +0.1796, -0.7311, +0.0016, -0.4996, +0.0724, -0.4854, +0.2310], + [-0.5525, +0.1246, +0.8667, +0.3521, -0.7227, +0.4473, +0.0805, -0.1707], + [-0.3557, +0.0798, +0.5322, -0.0827, +0.1836, +0.2784, -0.1676, -0.1012], + [-0.6738, +0.5545, -0.3111, +0.2171, -0.3377, -0.2039, -0.1205, +0.1212], + [-0.2249, -0.1037, -0.4715, -0.3616, +0.2250, +0.0999, +0.4772, -0.0679], + [-0.4895, -0.2557, +0.0892, -0.0000, -0.4443, +0.2913, -0.0450, -0.3303], + [-0.2729, +0.0973, +0.2516, -0.4313, -0.1743, -0.2386, -0.1142, -0.0440], + [-0.6820, +0.2449, -0.1079, +0.3047, +0.0325, -0.0980, +0.1868, -0.1600], + [-0.2085, -0.2897, -0.4539, -0.0506, +0.1225, +0.1976, -0.2360, +0.6553], + [+0.5299, +0.2876, +0.2354, -0.0884, -0.1821, +0.3841, -0.0701, +0.0278], + [-0.1594, -0.4837, +0.1483, -0.1280, +0.0962, -0.1932, +0.1730, +0.0544], + [+0.2879, -0.1846, +0.1474, -0.3383, +0.1483, +0.1159, +0.3761, +0.2326], + [-0.0960, -0.0727, -0.3099, +0.0548, +0.2339, -0.2232, +0.6066, +0.3826], + [-0.0171, +0.1490, -0.3324, -0.0231, +0.0700, -0.6364, -0.1944, +0.3094], + [+0.0804, -0.0278, +0.0182, -0.3807, -0.3628, -0.3564, +0.2279, -0.0226], + [+0.4219, +0.0741, +0.1299, +0.4700, +0.1927, +0.0849, -0.1574, +0.5607], + [+0.0810, -0.0815, +0.1356, +0.2048, -0.1404, -0.0222, -0.2464, +0.4991], + [+0.3000, +0.0862, -0.3917, +0.4117, +0.0306, +0.0788, -0.0403, -0.6423], + [-0.2445, +0.4847, +0.0478, -0.2170, -0.3700, +0.0189, +0.0001, -0.0335], + [+0.3710, -0.0115, -0.4207, -0.4245, +0.2041, -0.0875, -0.2369, -0.6424], + [-0.3608, +0.0539, -0.3928, -0.0352, +0.2832, +0.1003, -0.0082, +0.3201], + [+0.2221, -0.0399, +0.2092, -0.1670, -0.5146, +0.2273, +0.3011, -0.2879], + [+0.1500, +0.3236, +0.1295, -0.0763, -0.2708, +0.6542, +0.1379, -0.0646], + [+0.1291, +0.0417, +0.1386, +0.0166, +0.1373, +0.1956, +0.4986, +0.5690]]) + +weights_final_b = np.array( + [-0.0680, +0.1401, -0.0628, -0.1317, +0.1489, +0.1844, -0.1147, +0.0137]) +# yapf: enable -if __name__=="__main__": - main() +if __name__ == "__main__": + main() diff --git a/examples/pybullet/gym/pybullet_envs/examples/enjoy_TF_HalfCheetahBulletEnv_v0_2017may.py b/examples/pybullet/gym/pybullet_envs/examples/enjoy_TF_HalfCheetahBulletEnv_v0_2017may.py index 1b68afd6f..50eacc552 100644 --- a/examples/pybullet/gym/pybullet_envs/examples/enjoy_TF_HalfCheetahBulletEnv_v0_2017may.py +++ b/examples/pybullet/gym/pybullet_envs/examples/enjoy_TF_HalfCheetahBulletEnv_v0_2017may.py @@ -2,293 +2,2120 @@ import os, inspect currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe()))) parentdir = os.path.dirname(os.path.dirname(currentdir)) -os.sys.path.insert(0,parentdir) +os.sys.path.insert(0, parentdir) import gym import numpy as np import pybullet_envs import time + def relu(x): - return np.maximum(x, 0) + return np.maximum(x, 0) + class SmallReactivePolicy: - "Simple multi-layer perceptron policy, no internal state" - def __init__(self, observation_space, action_space): - assert weights_dense1_w.shape == (observation_space.shape[0], 128) - assert weights_dense2_w.shape == (128, 64) - assert weights_final_w.shape == (64, action_space.shape[0]) + "Simple multi-layer perceptron policy, no internal state" + + def __init__(self, observation_space, action_space): + assert weights_dense1_w.shape == (observation_space.shape[0], 128) + assert weights_dense2_w.shape == (128, 64) + assert weights_final_w.shape == (64, action_space.shape[0]) + + def act(self, ob): + x = ob + x = relu(np.dot(x, weights_dense1_w) + weights_dense1_b) + x = relu(np.dot(x, weights_dense2_w) + weights_dense2_b) + x = np.dot(x, weights_final_w) + weights_final_b + return x - def act(self, ob): - x = ob - x = relu(np.dot(x, weights_dense1_w) + weights_dense1_b) - x = relu(np.dot(x, weights_dense2_w) + weights_dense2_b) - x = np.dot(x, weights_final_w) + weights_final_b - return x def main(): - env = gym.make("HalfCheetahBulletEnv-v0") - env.render(mode="human") + env = gym.make("HalfCheetahBulletEnv-v0") + env.render(mode="human") - pi = SmallReactivePolicy(env.observation_space, env.action_space) - #disable rendering during reset, makes loading much faster - env.reset() + pi = SmallReactivePolicy(env.observation_space, env.action_space) + #disable rendering during reset, makes loading much faster + env.reset() + while 1: + frame = 0 + score = 0 + restart_delay = 0 + obs = env.reset() while 1: - frame = 0 - score = 0 - restart_delay = 0 - obs = env.reset() - while 1: - time.sleep(1./60.) - a = pi.act(obs) - obs, r, done, _ = env.step(a) - score += r - frame += 1 + time.sleep(1. / 60.) + a = pi.act(obs) + obs, r, done, _ = env.step(a) + score += r + frame += 1 - still_open = env.render("human") - if still_open==False: - return - if not done: continue - if restart_delay==0: - print("score=%0.2f in %i frames" % (score, frame)) - restart_delay = 60*2 # 2 sec at 60 fps - else: - restart_delay -= 1 - if restart_delay==0: break + still_open = env.render("human") + if still_open == False: + return + if not done: continue + if restart_delay == 0: + print("score=%0.2f in %i frames" % (score, frame)) + restart_delay = 60 * 2 # 2 sec at 60 fps + else: + restart_delay -= 1 + if restart_delay == 0: break -weights_dense1_w = np.array([ -[ -0.4105, +0.5540, +0.2791, +0.4953, -0.0309, +0.8480, +0.1630, +0.0630, +0.7107, +0.0597, +0.2675, -0.1931, -0.2436, +0.2909, +0.3563, +0.4564, -0.0445, -0.2228, +0.7005, +0.8368, +0.3631, +0.7365, +0.2453, +0.4687, +0.4819, +0.4940, +0.5096, +0.5929, +0.5504, +0.2265, +0.4118, +0.5993, +0.2402, -0.5087, -0.0756, +0.7520, -0.0086, -0.0648, +0.0564, -0.0812, -0.0093, +0.8482, +0.7333, +0.4401, -0.1273, -0.2974, +0.1284, -0.4948, +0.1120, +0.7938, +0.1297, +1.0807, +0.2480, +0.2444, +0.4258, +0.1515, -0.0126, +0.3098, -0.2786, +0.4913, +0.3065, +0.3356, +0.6884, +0.3117, +0.3163, +0.4234, +0.1172, +0.8810, +0.3574, -0.3993, -0.5597, +0.2233, +0.8058, +0.3391, -0.1113, +0.1884, +0.1324, +0.0503, +0.1807, +0.0441, -0.2065, +0.1978, +0.0852, -0.3531, +0.2180, +0.7946, -0.0318, +0.0650, -0.3174, -0.1715, -0.0486, +0.2131, -0.3901, +0.1995, +0.8593, +0.1786, +0.3595, +0.7852, +0.3491, -0.4206, +0.6117, -0.1991, +0.1876, -0.3114, -0.2007, -0.2738, -0.0388, +0.0465, +0.2726, -0.5321, +0.4720, -0.0555, +0.3415, +0.1447, -0.1132, -0.2958, -0.6729, -0.5003, -0.4960, -0.2600, -0.4158, -0.4476, -0.8244, -0.0574, +0.2194, +0.0541, -0.3526, -0.1131], -[ -0.2662, +0.1763, +0.0520, +0.3624, +0.0564, +0.2756, +0.0743, +0.0155, +0.1861, -0.5311, +0.2181, -0.3643, +0.1433, +0.1801, -0.1781, -0.3576, -0.2487, +0.0588, +0.3906, -0.3994, +0.1343, +0.2701, +0.1613, +0.1962, -0.0277, +0.0303, +0.0845, -0.1033, -0.1092, -0.1026, -0.2261, +0.3324, +0.1881, -0.2780, -0.0865, -0.2131, -0.1238, -0.1727, +0.1086, +0.1383, +0.1794, +0.0677, -0.0582, -0.0758, +0.0834, +0.0847, +0.0945, +0.1888, -0.0696, +0.0111, -0.0028, +0.0439, -0.3847, -0.1564, -0.2226, +0.0002, +0.0926, -0.2819, -0.0868, +0.2233, +0.0738, +0.1671, -0.0326, +0.3784, -0.0117, +0.0836, +0.0285, +0.0376, +0.0157, +0.1297, +0.0556, -0.0533, -0.0103, +0.0740, -0.1825, -0.2399, -0.0609, -0.0030, +0.1811, -0.2784, -0.2249, +0.3637, +0.1895, +0.3102, -0.0767, +0.1143, -0.0840, +0.2981, -0.3040, +0.0135, +0.2495, +0.0036, -0.1493, -0.1144, -0.0006, +0.1195, +0.0961, -0.4227, -0.3022, +0.2102, +0.2024, -0.2111, +0.1507, +0.2948, -0.1822, +0.2263, -0.3213, +0.0380, -0.1188, +0.1237, +0.0731, +0.0044, -0.1637, -0.3173, +0.0895, -0.0130, +0.0703, -0.2186, +0.1401, +0.1191, +0.0225, -0.3208, -0.0578, -0.3113, +0.1041, -0.1957, -0.3054, -0.0797], -[ +0.3485, -0.3161, -0.4092, +0.1190, -0.5706, -0.3627, +0.0576, -0.1068, +0.1367, +0.1686, +0.0284, -0.1694, -0.0496, -0.1647, +0.0111, -0.1897, -0.4613, -0.5996, -0.0601, -0.1417, -0.0236, -0.0898, +0.1239, -0.2693, -0.1709, +0.3592, -0.1284, -0.0376, -0.1308, -0.1832, -0.0538, -0.0467, -0.1181, -0.0994, -0.3281, +0.0527, -0.4579, -0.1062, -0.1058, -0.4459, -0.0901, -0.0246, -0.1125, +0.1228, -0.3428, -0.3385, +0.0366, -0.0988, +0.1345, +0.0299, +0.2921, 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[+0.3725, +0.2281, +0.3031, +0.0774, -0.3138, +0.0967], + [+0.3016, +0.0322, +0.0556, -0.0571, -0.1177, -0.2442], + [-0.2769, -0.5119, -0.0336, -0.1588, +0.0503, -0.2331], + [+0.1518, +0.4363, +0.0093, -0.3960, -0.1347, +0.2931], + [+0.1897, -0.0655, +0.1977, +0.4321, +0.6998, -0.0863], + [+0.0391, -0.0305, +0.3576, -0.4996, -0.0640, -0.0039], + [+0.2922, +0.3815, +0.2834, +0.1990, -0.0568, +0.2077], + [-0.5371, +0.2796, +0.2952, +0.1182, +0.0523, -0.2151], + [-0.5111, +0.2231, -0.1034, +0.0479, -0.0543, +0.0605], + [+0.0822, -0.4530, -0.0345, +0.1621, -0.2487, +0.1516]]) + +weights_final_b = np.array( + [+0.1367, +0.1107, -0.0148, +0.1158, -0.0820, +0.3047]) +# yapf: enable -if __name__=="__main__": - main() +if __name__ == "__main__": + main() diff --git a/examples/pybullet/gym/pybullet_envs/examples/enjoy_TF_HopperBulletEnv_v0_2017may.py b/examples/pybullet/gym/pybullet_envs/examples/enjoy_TF_HopperBulletEnv_v0_2017may.py index 367e80299..a58b79411 100644 --- a/examples/pybullet/gym/pybullet_envs/examples/enjoy_TF_HopperBulletEnv_v0_2017may.py +++ b/examples/pybullet/gym/pybullet_envs/examples/enjoy_TF_HopperBulletEnv_v0_2017may.py @@ -2,285 +2,1889 @@ import os, inspect currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe()))) parentdir = os.path.dirname(os.path.dirname(currentdir)) -os.sys.path.insert(0,parentdir) +os.sys.path.insert(0, parentdir) import gym import numpy as np import pybullet_envs import time + def relu(x): - return np.maximum(x, 0) + return np.maximum(x, 0) + class SmallReactivePolicy: - """ + """ Simple multi-layer perceptron policy, no internal state """ - def __init__(self, observation_space, action_space): - assert weights_dense1_w.shape == (observation_space.shape[0], 128) - assert weights_dense2_w.shape == (128, 64) - assert weights_final_w.shape == (64, action_space.shape[0]) - def act(self, ob): - x = ob - x = relu(np.dot(x, weights_dense1_w) + weights_dense1_b) - x = relu(np.dot(x, weights_dense2_w) + weights_dense2_b) - x = np.dot(x, weights_final_w) + weights_final_b - return x + def __init__(self, observation_space, action_space): + assert weights_dense1_w.shape == (observation_space.shape[0], 128) + assert weights_dense2_w.shape == (128, 64) + assert weights_final_w.shape == (64, action_space.shape[0]) + + def act(self, ob): + x = ob + x = relu(np.dot(x, weights_dense1_w) + weights_dense1_b) + x = relu(np.dot(x, weights_dense2_w) + weights_dense2_b) + x = np.dot(x, weights_final_w) + weights_final_b + return x + def main(): - env = gym.make("HopperBulletEnv-v0") - env.render(mode="human") - - pi = SmallReactivePolicy(env.observation_space, env.action_space) - env.reset() - while 1: - frame = 0 - score = 0 - restart_delay = 0 - #disable rendering during reset, makes loading much faster - obs = env.reset() - - while 1: - time.sleep(1./60.) - a = pi.act(obs) - obs, r, done, _ = env.step(a) - score += r - frame += 1 + env = gym.make("HopperBulletEnv-v0") + env.render(mode="human") + pi = SmallReactivePolicy(env.observation_space, env.action_space) + env.reset() + while 1: + frame = 0 + score = 0 + restart_delay = 0 + #disable rendering during reset, makes loading much faster + obs = env.reset() - still_open = env.render("human") - if still_open==False: - return - if not done: continue - if restart_delay==0: - print("score=%0.2f in %i frames" % (score, frame)) - restart_delay = 60*2 # 2 sec at 60 fps - else: - restart_delay -= 1 - if restart_delay==0: break + while 1: + time.sleep(1. / 60.) + a = pi.act(obs) + obs, r, done, _ = env.step(a) + score += r + frame += 1 -weights_dense1_w = np.array([ -[ +0.6734, +1.1364, +0.6117, +0.5185, +0.5099, -0.2038, -0.0045, -0.1448, +0.5081, +1.1685, -0.7904, -0.4623, +0.0027, -0.0473, -0.1144, +0.5095, -0.1913, +0.2021, +0.3485, +0.1104, -0.4992, +0.5207, -0.1013, -0.6947, +0.1624, +0.3533, -0.2485, -0.0012, +0.1674, +0.1253, +1.5485, +0.3576, +0.8236, +0.7361, +0.6604, -0.0834, +0.1212, -0.8404, 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-0.3288, -0.0070, +0.0508, -0.3687, -0.4245, -0.2751] +# yapf: disable +weights_dense1_w = np.array( + [[ + +0.6734, +1.1364, +0.6117, +0.5185, +0.5099, -0.2038, -0.0045, -0.1448, + +0.5081, +1.1685, -0.7904, -0.4623, +0.0027, -0.0473, -0.1144, +0.5095, + -0.1913, +0.2021, +0.3485, +0.1104, -0.4992, +0.5207, -0.1013, -0.6947, + +0.1624, +0.3533, -0.2485, -0.0012, +0.1674, +0.1253, +1.5485, +0.3576, + +0.8236, +0.7361, +0.6604, -0.0834, +0.1212, -0.8404, -0.8337, +0.3709, + -0.4218, -0.1011, -1.1418, -0.0554, +0.6676, +0.4739, -0.2105, +0.3187, + -0.4321, -0.7018, +0.1845, +0.2525, +0.0205, +0.9391, +0.6123, +0.6868, + +0.5116, +0.3483, +0.1148, +0.6747, -0.1590, +0.1879, +0.4836, +0.1997, + +0.5105, -0.2695, +0.0645, +0.5566, +0.0502, +0.2292, -0.4234, -0.3778, + -0.7639, -0.6084, -0.0375, -0.4799, +0.6465, -0.4097, +0.1091, -0.0681, + -0.1813, +0.3625, +0.6067, +0.1837, +0.1600, -0.1706, +0.0531, -0.3710, + +0.1320, +0.5035, +0.1106, +0.9955, -0.2657, +0.6051, -0.2525, -0.9118, + 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-0.4554], -[ +0.3463, +0.4490, -0.2689], -[ +0.5252, -0.3714, +0.2808], -[ +0.5561, +0.1137, -0.0258], -[ +0.6168, +0.0587, -0.1059], -[ -0.0737, -0.6782, -0.4729], -[ -0.6030, -0.2577, +0.4194], -[ +0.4061, -0.1966, +0.2603], -[ -0.1265, +0.3940, +0.0447], -[ +0.2460, +0.3328, +0.1654], -[ -0.1878, +0.6672, +0.0333], -[ +0.5618, -0.1013, -0.2911], -[ +0.8250, -0.0788, -0.3731], -[ +0.2661, -0.2775, +0.2854], -[ -0.1036, +0.2179, -0.6408], -[ -0.5371, -0.7837, +0.1141], -[ +0.4754, -0.0923, +0.1714], -[ +0.0448, +0.4901, -0.1578], -[ +0.8797, +0.3316, +0.4349], -[ +0.1514, -0.4129, -0.3333], -[ +0.2847, +0.2285, +0.1408], -[ +0.0045, +0.3333, +0.0694], -[ +0.1553, -0.5147, +0.0549], -[ -1.0765, -0.5130, +0.1472], -[ +0.0808, -0.2612, +0.2591], -[ +0.5104, +0.3062, +0.3979], -[ -0.0758, -0.4512, -0.4422], -[ +0.3236, +0.4479, -0.1759], -[ +0.2181, -0.0028, +0.5615], -[ -0.0254, -0.1924, +0.4608], -[ -0.1566, +0.5634, -0.2498], -[ -0.0042, +0.5403, +0.0272], -[ +0.1206, +0.3941, -1.1469], -[ +0.7642, -1.0114, +0.4905], -[ +0.5071, +0.2270, +0.0046] +weights_dense2_b = np.array([ + +0.0266, +0.3395, +0.0968, +0.0097, +0.0443, -0.1722, -0.2537, +0.1501, + +0.0233, -0.0174, +0.1783, -0.0513, -0.1531, -0.2145, +0.0299, -0.1589, + +0.1143, +0.1132, -0.2700, -0.0481, +0.2876, +0.1824, +0.2418, +0.1009, + -0.1451, +0.1637, +0.1703, +0.0336, -0.1405, -0.0162, -0.1158, +0.1691, + -0.0223, -0.1828, +0.2352, +0.0209, -0.1362, -0.2636, +0.1271, -0.0936, + -0.1436, +0.0606, +0.1311, -0.0989, -0.2877, +0.2593, +0.0781, -0.1118, + -0.1264, +0.2478, +0.2398, -0.1515, +0.0959, -0.0821, -0.1740, +0.1116, + -0.0682, +0.1743, +0.1746, -0.1950, -0.0951, +0.2190, -0.0294, +0.1950 ]) -weights_final_b = np.array([ +0.4868, -0.0987, -0.0946]) +weights_final_w = np.array([[-0.6826, +0.0812, +0.5225], + [-0.2533, +0.7043, +0.6677], + [+0.0684, +0.6807, +0.4726], + [+0.4605, +0.3736, -0.3187], + [+0.4913, +0.0049, +0.4186], + [+0.3066, +0.3756, -0.3588], + [-0.3078, +0.3643, -0.0335], + [-0.5433, +0.5926, -0.1254], + [-0.2532, +0.7950, -0.1380], + [-0.6121, -0.3012, +0.0149], + [+0.3181, +0.0973, +0.1705], + [+0.2236, -0.1020, +0.3147], + [-0.0038, -0.3193, +0.1068], + [+0.3799, -0.0215, -0.0715], + [+0.3440, -0.2333, -0.1001], + [+0.1852, +0.7445, +0.4295], + [+0.4888, -0.0469, -0.1287], + [+0.0297, -0.3913, -0.5198], + [-0.0092, -0.1013, +0.2193], + [+0.2590, +0.0846, +0.0886], + [+0.0744, +0.4771, -0.3553], + [+0.2019, +0.1670, -0.1630], + [-0.4549, -0.6127, +0.5951], + [-0.2582, -0.6401, -0.6541], + [+0.0248, +0.0646, +0.1598], + [+0.2109, -0.0794, -0.2444], + [+0.2435, -0.4159, -0.2927], + [-0.0713, +0.2441, -0.4683], + [-0.0737, +0.2075, +0.4609], + [+0.3240, -0.4084, -0.4554], + [+0.3463, +0.4490, -0.2689], + [+0.5252, -0.3714, +0.2808], + [+0.5561, +0.1137, -0.0258], + [+0.6168, +0.0587, -0.1059], + [-0.0737, -0.6782, -0.4729], + [-0.6030, -0.2577, +0.4194], + [+0.4061, -0.1966, +0.2603], + [-0.1265, +0.3940, +0.0447], + [+0.2460, +0.3328, +0.1654], + [-0.1878, +0.6672, +0.0333], + [+0.5618, -0.1013, -0.2911], + [+0.8250, -0.0788, -0.3731], + [+0.2661, -0.2775, +0.2854], + [-0.1036, +0.2179, -0.6408], + [-0.5371, -0.7837, +0.1141], + [+0.4754, -0.0923, +0.1714], + [+0.0448, +0.4901, -0.1578], + [+0.8797, +0.3316, +0.4349], + [+0.1514, -0.4129, -0.3333], + [+0.2847, +0.2285, +0.1408], + [+0.0045, +0.3333, +0.0694], + [+0.1553, -0.5147, +0.0549], + [-1.0765, -0.5130, +0.1472], + [+0.0808, -0.2612, +0.2591], + [+0.5104, +0.3062, +0.3979], + [-0.0758, -0.4512, -0.4422], + [+0.3236, +0.4479, -0.1759], + [+0.2181, -0.0028, +0.5615], + [-0.0254, -0.1924, +0.4608], + [-0.1566, +0.5634, -0.2498], + [-0.0042, +0.5403, +0.0272], + [+0.1206, +0.3941, -1.1469], + [+0.7642, -1.0114, +0.4905], + [+0.5071, +0.2270, +0.0046]]) + +weights_final_b = np.array([+0.4868, -0.0987, -0.0946]) +# yapf: enable -if __name__=="__main__": - main() +if __name__ == "__main__": + main() diff --git a/examples/pybullet/gym/pybullet_envs/examples/enjoy_TF_HumanoidBulletEnv_v0_2017may.py b/examples/pybullet/gym/pybullet_envs/examples/enjoy_TF_HumanoidBulletEnv_v0_2017may.py index 08245626b..572d495ca 100644 --- a/examples/pybullet/gym/pybullet_envs/examples/enjoy_TF_HumanoidBulletEnv_v0_2017may.py +++ b/examples/pybullet/gym/pybullet_envs/examples/enjoy_TF_HumanoidBulletEnv_v0_2017may.py @@ -2,502 +2,7856 @@ import os, inspect currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe()))) parentdir = os.path.dirname(os.path.dirname(currentdir)) -os.sys.path.insert(0,parentdir) +os.sys.path.insert(0, parentdir) import gym import numpy as np import pybullet_envs import time + def relu(x): - return np.maximum(x, 0) + return np.maximum(x, 0) + class SmallReactivePolicy: - "Simple multi-layer perceptron policy, no internal state" - def __init__(self, observation_space, action_space): - assert weights_dense1_w.shape == (observation_space.shape[0], 256) - assert weights_dense2_w.shape == (256, 128) - assert weights_final_w.shape == (128, action_space.shape[0]) + "Simple multi-layer perceptron policy, no internal state" + + def __init__(self, observation_space, action_space): + assert weights_dense1_w.shape == (observation_space.shape[0], 256) + assert weights_dense2_w.shape == (256, 128) + assert weights_final_w.shape == (128, action_space.shape[0]) + + def act(self, ob): + ob[0] += -1.4 + 0.8 + x = ob + x = relu(np.dot(x, weights_dense1_w) + weights_dense1_b) + x = relu(np.dot(x, weights_dense2_w) + weights_dense2_b) + x = np.dot(x, weights_final_w) + weights_final_b + return x - def act(self, ob): - ob[0] += -1.4 + 0.8 - x = ob - x = relu(np.dot(x, weights_dense1_w) + weights_dense1_b) - x = relu(np.dot(x, weights_dense2_w) + weights_dense2_b) - x = np.dot(x, weights_final_w) + weights_final_b - return x def main(): - env = gym.make("HumanoidBulletEnv-v0") - env.render(mode="human") - pi = SmallReactivePolicy(env.observation_space, env.action_space) - env.reset() + env = gym.make("HumanoidBulletEnv-v0") + env.render(mode="human") + pi = SmallReactivePolicy(env.observation_space, env.action_space) + env.reset() + while 1: + frame = 0 + score = 0 + restart_delay = 0 + obs = env.reset() + while 1: - frame = 0 - score = 0 - restart_delay = 0 - obs = env.reset() - - while 1: - a = pi.act(obs) - obs, r, done, _ = env.step(a) - score += r - frame += 1 - time.sleep(1./60.) + a = pi.act(obs) + obs, r, done, _ = env.step(a) + score += r + frame += 1 + time.sleep(1. / 60.) - still_open = env.render("human") - if still_open==False: - return - if not done: continue - if restart_delay==0: - print("score=%0.2f in %i frames" % (score, frame)) - restart_delay = 60*2 # 2 sec at 60 fps - else: - restart_delay -= 1 - if restart_delay==0: break + still_open = env.render("human") + if still_open == False: + return + if not done: continue + if restart_delay == 0: + print("score=%0.2f in %i frames" % (score, frame)) + restart_delay = 60 * 2 # 2 sec at 60 fps + else: + restart_delay -= 1 + if restart_delay == 0: break -weights_dense1_w = np.array([ -[ +0.2289, +0.2584, +0.2595, +0.0173, +0.1293, -0.2980, +0.1410, +0.0982, +0.0216, +0.3724, +0.2204, -0.0734, -0.2420, -0.3443, -0.2738, -0.3825, +0.1504, -0.0930, -0.2680, +0.0685, +0.1592, +0.2534, -0.0787, -0.0426, +0.2591, +0.2134, -0.1631, +0.5168, +0.1444, +0.2736, +0.3623, -0.3472, +0.0393, -0.3056, +0.3850, -0.5231, +0.4511, +0.4223, -0.0905, +0.2265, +0.1662, +0.1092, +0.0426, -0.0209, +0.3260, -0.1788, +0.5045, -0.0254, +0.6684, +0.4659, +0.2193, +0.6121, -0.1771, +0.3024, +0.3233, -0.3380, +0.1834, +0.1947, +0.2840, -0.0212, +0.0610, +0.0254, -0.0687, +0.3342, -0.2010, +0.4851, -0.5739, -0.3228, -0.2242, +0.6149, +0.2704, -0.1006, +0.3950, -0.2684, +0.0090, -0.2419, +0.1112, -0.0795, +0.0021, -0.0317, +0.1345, -0.2847, +0.2323, +0.5374, -0.0119, -0.2098, +0.2074, +0.1693, +0.4537, -0.1453, +0.2661, -0.2997, +0.1043, -0.2340, +0.4472, -0.0415, +0.6437, +0.0279, 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+ +0.2113, +0.0024, +0.2648, -0.1811, -0.0259, -0.1363, -0.2149, + -0.2199, -0.1105, -0.1616 + ], + [ + -0.0967, +0.0007, -0.2230, +0.1197, +0.1085, +0.0267, -0.0633, + +0.1611, -0.0078, +0.0088, -0.0879, -0.1362, +0.1364, -0.1784, + +0.1725, -0.0491, -0.0177 + ], + [ + +0.0724, -0.0574, +0.0696, -0.0095, -0.1601, +0.1243, +0.2373, + -0.1058, +0.1610, -0.1874, -0.0689, +0.1806, -0.2117, -0.0855, + -0.0657, +0.0366, -0.0797 + ]]) + +weights_final_b = np.array([ + -0.0356, +0.0776, -0.0344, +0.1375, +0.1048, +0.3648, +0.3240, +0.1319, + +0.1161, +0.3373, +0.3193, +0.0120, +0.0253, -0.2434, -0.1291, +0.1042, + -0.2448 +]) +# yapf: enable -if __name__=="__main__": - main() +if __name__ == "__main__": + main() diff --git a/examples/pybullet/gym/pybullet_envs/examples/enjoy_TF_HumanoidFlagrunHarderBulletEnv_v1_2017jul.py b/examples/pybullet/gym/pybullet_envs/examples/enjoy_TF_HumanoidFlagrunHarderBulletEnv_v1_2017jul.py index 916094b1f..84220d533 100644 --- a/examples/pybullet/gym/pybullet_envs/examples/enjoy_TF_HumanoidFlagrunHarderBulletEnv_v1_2017jul.py +++ b/examples/pybullet/gym/pybullet_envs/examples/enjoy_TF_HumanoidFlagrunHarderBulletEnv_v1_2017jul.py @@ -2,7 +2,7 @@ import os, inspect currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe()))) parentdir = os.path.dirname(os.path.dirname(currentdir)) -os.sys.path.insert(0,parentdir) +os.sys.path.insert(0, parentdir) import gym import numpy as np @@ -10,500 +10,7853 @@ import pybullet_envs import os.path import time + def relu(x): - return np.maximum(x, 0) + return np.maximum(x, 0) + class SmallReactivePolicy: - "Simple multi-layer perceptron policy, no internal state" - def __init__(self, observation_space, action_space): - assert weights_dense1_w.shape == (observation_space.shape[0], 256) - assert weights_dense2_w.shape == (256, 128) - assert weights_final_w.shape == (128, action_space.shape[0]) + "Simple multi-layer perceptron policy, no internal state" + + def __init__(self, observation_space, action_space): + assert weights_dense1_w.shape == (observation_space.shape[0], 256) + assert weights_dense2_w.shape == (256, 128) + assert weights_final_w.shape == (128, action_space.shape[0]) - def act(self, ob): - x = ob - x = relu(np.dot(x, weights_dense1_w) + weights_dense1_b) - x = relu(np.dot(x, weights_dense2_w) + weights_dense2_b) - x = np.dot(x, weights_final_w) + weights_final_b - return x + def act(self, ob): + x = ob + x = relu(np.dot(x, weights_dense1_w) + weights_dense1_b) + x = relu(np.dot(x, weights_dense2_w) + weights_dense2_b) + x = np.dot(x, weights_final_w) + weights_final_b + return x def demo_run(): - gui = True - env = gym.make("HumanoidFlagrunHarderBulletEnv-v0") - if (gui): - env.render(mode="human") + gui = True + env = gym.make("HumanoidFlagrunHarderBulletEnv-v0") + if (gui): + env.render(mode="human") + + pi = SmallReactivePolicy(env.observation_space, env.action_space) + while 1: + frame = 0 + score = 0 + restart_delay = 0 + obs = env.reset() - pi = SmallReactivePolicy(env.observation_space, env.action_space) while 1: - frame = 0 - score = 0 - restart_delay = 0 - obs = env.reset() + a = pi.act(obs) + obs, r, done, _ = env.step(a) + score += r + frame += 1 + if (gui): + time.sleep(1. / 60) + + still_open = env.render("human") - while 1: - a = pi.act(obs) - obs, r, done, _ = env.step(a) - score += r - frame += 1 - if (gui): - time.sleep(1./60) + if still_open == False: + return + if not done: continue + if restart_delay == 0: + print("score=%0.2f in %i frames" % (score, frame)) + if still_open != True: # not True in multiplayer or non-Roboschool environment + break + restart_delay = 60 * 2 # 2 sec at 60 fps + restart_delay -= 1 + if restart_delay == 0: break - still_open = env.render("human") - if still_open==False: - return - if not done: continue - if restart_delay==0: - print("score=%0.2f in %i frames" % (score, frame)) - if still_open!=True: # not True in multiplayer or non-Roboschool environment - break - restart_delay = 60*2 # 2 sec at 60 fps - restart_delay -= 1 - if restart_delay==0: break +# yapf: disable +weights_dense1_w = np.array( + [[ + +0.3543, -0.3833, +0.3987, +0.1828, +0.5746, +0.3807, +0.1427, +0.2510, + -0.3705, -0.2212, +0.0019, +0.7898, +0.2609, -0.5883, -0.1780, +0.1319, + +0.4746, -0.5833, -0.4251, +0.5949, +0.6170, -0.2344, +0.1624, -0.7524, + -0.1314, +0.3180, -0.3183, -0.5343, +0.6109, -0.0071, +0.0890, +0.1470, + +0.3201, -0.1915, -0.1192, -0.2254, +0.1921, +0.5020, +0.8814, +0.7632, + +0.2973, -0.0293, +0.0134, -0.1171, +0.4190, +0.2720, -0.1020, +0.2688, + -0.3085, -0.0548, +0.4256, +0.5690, -0.2243, +0.4055, +0.7467, +0.2371, + -0.1666, -0.3083, -0.2019, -0.1256, -0.2922, -0.0295, -0.1915, +0.4345, + +0.4745, +0.4599, +0.7173, +0.1671, -0.1942, +0.6437, +0.1593, -0.2243, + -0.3772, +0.0888, +0.0181, +1.5305, +0.1103, -0.0983, +0.5553, -0.2304, + +0.1091, -0.7436, -0.4132, -0.0910, -0.3729, +0.5907, -0.0080, -0.3815, + +0.4647, -0.4074, 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+0.4068, +0.9192, +0.1873, +0.0072, +0.2791, +0.2587, +0.2999, +0.2515, +0.2632, -0.2145, -0.1129], -[ -0.0104, +0.0656, +0.3292, -0.1732, -0.1230, +0.2711, +0.1398, -0.5466, -0.4228, -0.0740, +0.2116, +0.3474, +0.0030, -0.0176, +0.3739, -0.1911, -0.0056] +weights_final_b = np.array([ + +0.0149, +0.1334, -0.1019, -0.0498, +0.0919, -0.2079, +0.1914, -0.0708, + +0.0737, -0.1228, +0.0112, -0.0441, +0.0087, -0.1149, +0.0063, +0.0170, + -0.0965 ]) +# yapf: enable -weights_final_b = np.array([ +0.0149, +0.1334, -0.1019, -0.0498, +0.0919, -0.2079, +0.1914, -0.0708, +0.0737, -0.1228, +0.0112, -0.0441, +0.0087, -0.1149, +0.0063, +0.0170, -0.0965]) - -if __name__=="__main__": - demo_run() +if __name__ == "__main__": + demo_run() diff --git a/examples/pybullet/gym/pybullet_envs/examples/enjoy_TF_InvertedDoublePendulumBulletEnv_v0_2017may.py b/examples/pybullet/gym/pybullet_envs/examples/enjoy_TF_InvertedDoublePendulumBulletEnv_v0_2017may.py index 4bd7752ff..fbd35164d 100644 --- a/examples/pybullet/gym/pybullet_envs/examples/enjoy_TF_InvertedDoublePendulumBulletEnv_v0_2017may.py +++ b/examples/pybullet/gym/pybullet_envs/examples/enjoy_TF_InvertedDoublePendulumBulletEnv_v0_2017may.py @@ -2,179 +2,582 @@ import os, inspect currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe()))) parentdir = os.path.dirname(os.path.dirname(currentdir)) -os.sys.path.insert(0,parentdir) +os.sys.path.insert(0, parentdir) import gym import numpy as np import pybullet_envs import time + def relu(x): - return np.maximum(x, 0) + return np.maximum(x, 0) + class SmallReactivePolicy: - "Simple multi-layer perceptron policy, no internal state" - def __init__(self, observation_space, action_space): - assert weights_dense1_w.shape == (observation_space.shape[0], 64.0) - assert weights_dense2_w.shape == (64.0, 32.0) - assert weights_final_w.shape == (32.0, action_space.shape[0]) - - def act(self, ob): - x = ob - x = relu(np.dot(x, weights_dense1_w) + weights_dense1_b) - x = relu(np.dot(x, weights_dense2_w) + weights_dense2_b) - x = np.dot(x, weights_final_w) + weights_final_b - return x + "Simple multi-layer perceptron policy, no internal state" + + def __init__(self, observation_space, action_space): + assert weights_dense1_w.shape == (observation_space.shape[0], 64.0) + assert weights_dense2_w.shape == (64.0, 32.0) + assert weights_final_w.shape == (32.0, action_space.shape[0]) + + def act(self, ob): + x = ob + x = relu(np.dot(x, weights_dense1_w) + weights_dense1_b) + x = relu(np.dot(x, weights_dense2_w) + weights_dense2_b) + x = np.dot(x, weights_final_w) + weights_final_b + return x + def main(): - env = gym.make("InvertedDoublePendulumBulletEnv-v0") - env.render(mode="human") - - pi = SmallReactivePolicy(env.observation_space, env.action_space) + env = gym.make("InvertedDoublePendulumBulletEnv-v0") + env.render(mode="human") + + pi = SmallReactivePolicy(env.observation_space, env.action_space) + + while 1: + frame = 0 + score = 0 + restart_delay = 0 + obs = env.reset() while 1: - frame = 0 - score = 0 - restart_delay = 0 - obs = env.reset() - - while 1: - time.sleep(1./60.) - a = pi.act(obs) - obs, r, done, _ = env.step(a) - score += r - frame += 1 - still_open = env.render("human") - if still_open==False: - return - if not done: continue - if restart_delay==0: - print("score=%0.2f in %i frames" % (score, frame)) - restart_delay = 60*2 # 2 sec at 60 fps - else: - restart_delay -= 1 - if restart_delay > 0: continue - break - -weights_dense1_w = np.array([ -[ -0.5857, +0.1810, +0.2839, +0.1278, -0.4302, -0.3152, +0.5916, -0.0635, +0.6259, +0.2873, -0.0572, -0.3538, -0.8121, +0.2707, +0.1656, -0.2103, -0.1614, -0.2789, -0.5856, -0.4733, +0.1838, +0.1063, +0.7629, +0.0873, +0.1480, +0.1768, +0.6522, +0.1158, -0.0816, +0.6542, -0.8870, +0.1775, +0.1532, +0.2268, -0.0313, -0.0470, +0.5328, -0.0570, +0.4820, -0.3772, -0.7581, +0.2835, -0.3566, +0.9371, -0.0441, -0.1797, -0.2859, -0.0238, +0.0261, -0.0296, -0.1406, +0.2869, +0.1279, +0.6653, +0.5643, -0.3136, +0.7751, +0.2341, +0.1903, +0.8283, -0.0697, +0.1276, -0.0250, -0.0053], -[ +0.3741, +0.4844, -0.0638, -0.3205, +0.3137, +0.9636, +0.5329, +0.6882, +0.2983, -0.6675, -0.6372, +0.2065, -0.2645, -0.4789, +0.2326, -0.0691, -0.5905, -0.3354, +0.3428, +0.4253, +0.9111, -0.4751, -0.2124, +0.3920, +0.2897, -1.1101, +0.1894, -0.4025, -0.1125, -0.0627, +0.2347, -0.8787, +0.1019, +0.9128, +0.2544, -0.3933, +0.6485, -0.1936, -0.2402, +0.5012, -0.0918, +0.3160, -0.7860, +0.3439, -0.4268, -0.1788, -0.3930, +0.5128, +0.2338, +0.2571, +0.1343, +0.9850, -0.7074, +0.3532, +0.3048, -0.4542, +0.5539, -0.4409, -0.2003, -0.4837, -0.3554, -0.4447, -0.0817, -0.8497], -[ +0.0825, +0.5847, +0.4837, +0.5144, +0.4770, +0.0199, +0.4275, -0.4530, +0.8499, -0.2840, +0.3817, -0.5098, -0.2155, -0.1475, +0.1145, -0.1871, -0.0526, +0.3583, -0.3537, -0.7111, -0.6116, +0.3406, -0.6360, +0.7786, +0.6628, -0.0493, +0.3505, -0.0376, -0.6556, +1.0748, -0.5329, +0.6477, -0.7117, +0.3723, +0.6006, +0.0171, +0.0012, +0.4910, -0.5651, -0.6868, +0.2403, +0.0254, -0.4416, +0.7534, -0.0138, -1.1298, +0.5447, +0.0974, +0.1988, -0.2161, -0.3126, -0.5731, -0.1278, +0.2995, -0.1200, -0.7917, +0.5326, +0.4562, -0.0144, +0.5717, -0.4065, +0.1494, +0.7100, +0.2461], -[ -0.2861, +0.4314, -0.2982, -0.1401, -0.1033, +0.5287, -0.6620, -0.3975, +0.0038, +0.1991, -0.7079, -0.9000, +0.1659, +0.3623, -0.0752, -0.1907, -0.2335, -0.5143, +0.2324, -0.0487, +0.1583, -0.5989, +0.5957, +0.2150, -0.0335, +0.2154, +0.3279, -0.7976, +0.5320, -0.4438, +0.2170, -0.3841, -0.0039, -0.0847, -0.0028, -0.4278, -0.2393, -0.9239, +0.2880, -0.1437, -0.0941, -0.0796, -0.3906, -0.3224, +0.1038, -0.1929, -0.2713, -0.4157, -0.2178, +0.5729, -0.2065, +0.0059, +0.3879, +0.0590, +0.1759, +0.0677, -0.0170, -0.2252, +0.3301, -0.0599, +0.3791, -0.1455, +0.2200, -0.0499], -[ -0.4403, +0.7182, +0.7941, +0.1469, +1.5777, +0.3426, +0.0923, +0.2160, +1.1492, -0.5206, -0.2659, -0.1504, +0.2739, -1.3939, +0.8992, -1.1433, -0.3828, -0.2497, -0.2172, +0.2318, -0.3605, +0.6413, -1.9095, +1.4785, -0.1274, -0.7208, -0.0802, -0.8779, -1.6260, +0.9151, +0.8289, -0.0902, -0.3551, +0.6198, +1.7488, +0.0739, -1.2022, -0.3536, -1.5187, +0.1839, +1.4258, +0.4217, +0.1503, -0.0460, +0.2327, -0.4139, -0.3668, +0.2997, +0.6856, +0.6917, -0.3856, -0.3620, +0.1578, -0.8349, -1.0796, -0.0319, -1.1966, -0.8122, +0.5053, -0.5033, -0.9207, -0.1193, -0.7625, +0.1379], -[ -0.0321, -0.3206, -0.4516, +0.3420, +1.0964, +0.0311, +0.4654, -0.2367, +0.3347, -0.2798, -0.8169, -0.1555, +0.9397, -0.5597, +0.7113, -0.3642, -0.2840, -0.1323, -0.1000, +0.2283, +0.3612, -0.4784, +0.0504, +0.5310, -0.0887, +0.2926, +0.5069, -0.5645, -0.0976, -0.2594, +0.4425, +0.9223, -0.5637, -0.2336, -0.1316, -0.6564, -0.2780, -0.2409, -0.1637, +0.4506, +0.7018, -0.1299, +0.7172, +0.1207, +0.4375, +0.3836, +0.2781, -0.7792, -0.5317, +0.4510, +0.2423, -0.0588, -0.4254, -0.6381, -0.8205, +0.6417, +0.1904, -0.2618, +0.5900, -0.3899, -0.7851, -0.4769, -0.3688, -0.3510], -[ -0.8366, -0.3157, -0.1130, +0.2005, +0.3713, -0.4351, -0.1278, -0.5689, +0.3229, -0.5981, -0.4917, -0.4160, -0.5504, +0.2225, -0.1581, -0.6457, +0.1001, -1.0635, +0.2368, +0.2494, -0.4054, -0.1699, -0.1316, +0.2614, +0.3016, +0.4222, -0.1548, -0.0766, -0.5226, -0.3576, -0.2433, -0.5495, +0.0056, +0.0193, +0.2353, +0.3986, +0.3580, -0.7886, +0.3928, +0.1831, +0.4319, +0.2276, -0.3062, +0.0413, -0.4288, +0.1365, +0.3176, +0.3564, +0.5668, -0.4401, -0.9576, -0.1435, +0.0304, -0.5575, +0.0412, -0.1096, +0.2207, +0.1227, -0.0051, +0.5808, -0.1331, +0.1368, +0.4170, -0.8095], -[ -0.6368, -1.3221, -0.4492, -1.5414, +0.4004, -2.8780, -0.1748, -0.8166, +1.7066, +1.0714, -0.4755, +0.3020, +0.0422, +0.3466, +0.4472, -0.6209, -3.3768, -0.0806, +1.3624, -2.4155, +1.0886, +0.3412, +0.0891, +1.6821, -0.5361, +0.3952, +1.5120, +0.3910, +1.9500, -0.9065, -1.3452, +0.0904, -0.0389, +0.2817, -1.8375, +0.8131, -1.5287, +0.3115, +1.4069, -0.3424, +1.6101, +2.6775, +0.5516, +1.6500, -0.4138, -0.0170, +1.0008, -0.7865, +0.0551, +2.2068, -0.0108, +0.3207, -1.1884, +0.3792, -0.6435, +0.2858, -0.6881, +0.1554, -1.6926, -0.0975, -1.4120, -0.0827, -1.5186, +0.2526], -[ -0.2900, -0.2805, +0.9182, -0.8893, +0.7345, -0.9015, -0.2696, +0.2344, +0.3889, +0.6790, +0.3657, -0.1995, -0.6738, -0.4166, +0.1690, -0.3798, -0.9872, -0.2558, -0.4205, -0.6190, -0.0092, -0.2261, -0.2738, +0.2977, -0.7348, +0.4872, +0.4776, -0.1364, +0.5836, -0.2688, -0.4261, -0.3612, -0.3533, +0.4665, +0.0155, +1.0116, -0.7139, -0.3707, -0.4429, -0.0383, +0.6716, +0.5972, +0.3506, +0.3294, -1.3734, -0.5905, -0.1168, -0.2609, +0.3436, +0.8277, +0.4965, +0.3005, -0.2929, +0.1501, -0.2655, +0.3860, -0.3946, +0.8764, +0.7927, +0.0541, -1.0912, -0.2006, -0.6928, +0.4653] -]) + time.sleep(1. / 60.) + a = pi.act(obs) + obs, r, done, _ = env.step(a) + score += r + frame += 1 + still_open = env.render("human") + if still_open == False: + return + if not done: continue + if restart_delay == 0: + print("score=%0.2f in %i frames" % (score, frame)) + restart_delay = 60 * 2 # 2 sec at 60 fps + else: + restart_delay -= 1 + if restart_delay > 0: continue + break + -weights_dense1_b = np.array([ -0.1146, +0.2897, +0.0137, +0.0822, +0.0367, +0.0951, -0.0657, +0.0653, -0.0729, -0.0501, -0.6380, -0.4403, +0.0660, +0.0693, -0.4353, -0.2766, -0.1258, -0.6947, -0.1616, -0.0453, +0.1498, -0.2340, -0.0764, +0.2020, +0.4812, +0.0908, -0.1883, -0.0753, -0.0373, -0.4172, -0.1071, +0.0861, -0.1550, -0.0648, -0.1473, +0.1507, -0.0121, -0.5468, -0.1529, -0.3341, +0.0239, -0.0463, -0.0044, -0.0541, +0.0384, +0.3028, +0.3378, +0.0965, +0.0740, +0.1948, -0.1655, +0.1558, +0.1367, -0.1514, +0.0540, -0.0015, -0.1256, +0.3402, -0.0273, -0.2239, -0.0073, -0.6246, +0.0755, -0.2002]) +# yapf: disable +weights_dense1_w = np.array( + [[ + -0.5857, +0.1810, +0.2839, +0.1278, -0.4302, -0.3152, +0.5916, -0.0635, + +0.6259, +0.2873, -0.0572, -0.3538, -0.8121, +0.2707, +0.1656, -0.2103, + -0.1614, -0.2789, -0.5856, -0.4733, +0.1838, +0.1063, +0.7629, +0.0873, + +0.1480, +0.1768, +0.6522, +0.1158, -0.0816, +0.6542, -0.8870, +0.1775, + +0.1532, +0.2268, -0.0313, -0.0470, +0.5328, -0.0570, +0.4820, -0.3772, + -0.7581, +0.2835, -0.3566, +0.9371, -0.0441, -0.1797, -0.2859, -0.0238, + +0.0261, -0.0296, -0.1406, +0.2869, +0.1279, +0.6653, +0.5643, -0.3136, + +0.7751, +0.2341, +0.1903, +0.8283, -0.0697, +0.1276, -0.0250, -0.0053 + ], + [ + +0.3741, +0.4844, -0.0638, -0.3205, +0.3137, +0.9636, +0.5329, + +0.6882, +0.2983, -0.6675, -0.6372, +0.2065, -0.2645, -0.4789, + +0.2326, -0.0691, -0.5905, -0.3354, +0.3428, +0.4253, +0.9111, + -0.4751, -0.2124, +0.3920, +0.2897, -1.1101, +0.1894, -0.4025, + -0.1125, -0.0627, +0.2347, -0.8787, +0.1019, +0.9128, +0.2544, + -0.3933, +0.6485, -0.1936, -0.2402, +0.5012, -0.0918, +0.3160, + -0.7860, +0.3439, -0.4268, -0.1788, -0.3930, +0.5128, +0.2338, + +0.2571, +0.1343, +0.9850, -0.7074, +0.3532, +0.3048, -0.4542, + +0.5539, -0.4409, -0.2003, -0.4837, -0.3554, -0.4447, -0.0817, -0.8497 + ], + [ + +0.0825, +0.5847, +0.4837, +0.5144, +0.4770, +0.0199, +0.4275, + -0.4530, +0.8499, -0.2840, +0.3817, -0.5098, -0.2155, -0.1475, + +0.1145, -0.1871, -0.0526, +0.3583, -0.3537, -0.7111, -0.6116, + +0.3406, -0.6360, +0.7786, +0.6628, -0.0493, +0.3505, -0.0376, + -0.6556, +1.0748, -0.5329, +0.6477, -0.7117, +0.3723, +0.6006, + +0.0171, +0.0012, +0.4910, -0.5651, -0.6868, +0.2403, +0.0254, + -0.4416, +0.7534, -0.0138, -1.1298, +0.5447, +0.0974, +0.1988, + -0.2161, -0.3126, -0.5731, -0.1278, +0.2995, -0.1200, -0.7917, + +0.5326, +0.4562, -0.0144, +0.5717, -0.4065, +0.1494, +0.7100, +0.2461 + ], + [ + -0.2861, +0.4314, -0.2982, -0.1401, -0.1033, +0.5287, -0.6620, + -0.3975, +0.0038, +0.1991, -0.7079, -0.9000, +0.1659, +0.3623, + -0.0752, -0.1907, -0.2335, -0.5143, +0.2324, -0.0487, +0.1583, + -0.5989, +0.5957, +0.2150, -0.0335, +0.2154, +0.3279, -0.7976, + +0.5320, -0.4438, +0.2170, -0.3841, -0.0039, -0.0847, -0.0028, + -0.4278, -0.2393, -0.9239, +0.2880, -0.1437, -0.0941, -0.0796, + -0.3906, -0.3224, +0.1038, -0.1929, -0.2713, -0.4157, -0.2178, + +0.5729, -0.2065, +0.0059, +0.3879, +0.0590, +0.1759, +0.0677, + -0.0170, -0.2252, +0.3301, -0.0599, +0.3791, -0.1455, +0.2200, -0.0499 + ], + [ + -0.4403, +0.7182, +0.7941, +0.1469, +1.5777, +0.3426, +0.0923, + +0.2160, +1.1492, -0.5206, -0.2659, -0.1504, +0.2739, -1.3939, + +0.8992, -1.1433, -0.3828, -0.2497, -0.2172, +0.2318, -0.3605, + +0.6413, -1.9095, +1.4785, -0.1274, -0.7208, -0.0802, -0.8779, + -1.6260, +0.9151, +0.8289, -0.0902, -0.3551, +0.6198, +1.7488, + +0.0739, -1.2022, -0.3536, -1.5187, +0.1839, +1.4258, +0.4217, + +0.1503, -0.0460, +0.2327, -0.4139, -0.3668, +0.2997, +0.6856, + +0.6917, -0.3856, -0.3620, +0.1578, -0.8349, -1.0796, -0.0319, + -1.1966, -0.8122, +0.5053, -0.5033, -0.9207, -0.1193, -0.7625, +0.1379 + ], + [ + -0.0321, -0.3206, -0.4516, +0.3420, +1.0964, +0.0311, +0.4654, + -0.2367, +0.3347, -0.2798, -0.8169, -0.1555, +0.9397, -0.5597, + +0.7113, -0.3642, -0.2840, -0.1323, -0.1000, +0.2283, +0.3612, + -0.4784, +0.0504, +0.5310, -0.0887, +0.2926, +0.5069, -0.5645, + -0.0976, -0.2594, +0.4425, +0.9223, -0.5637, -0.2336, -0.1316, + -0.6564, -0.2780, -0.2409, -0.1637, +0.4506, +0.7018, -0.1299, + +0.7172, +0.1207, +0.4375, +0.3836, +0.2781, -0.7792, -0.5317, + +0.4510, +0.2423, -0.0588, -0.4254, -0.6381, -0.8205, +0.6417, + +0.1904, -0.2618, +0.5900, -0.3899, -0.7851, -0.4769, -0.3688, -0.3510 + ], + [ + -0.8366, -0.3157, -0.1130, +0.2005, +0.3713, -0.4351, -0.1278, + -0.5689, +0.3229, -0.5981, -0.4917, -0.4160, -0.5504, +0.2225, + -0.1581, -0.6457, +0.1001, -1.0635, +0.2368, +0.2494, -0.4054, + -0.1699, -0.1316, +0.2614, +0.3016, +0.4222, -0.1548, -0.0766, + -0.5226, -0.3576, -0.2433, -0.5495, +0.0056, +0.0193, +0.2353, + +0.3986, +0.3580, -0.7886, +0.3928, +0.1831, +0.4319, +0.2276, + -0.3062, +0.0413, -0.4288, +0.1365, +0.3176, +0.3564, +0.5668, + -0.4401, -0.9576, -0.1435, +0.0304, -0.5575, +0.0412, -0.1096, + +0.2207, +0.1227, -0.0051, +0.5808, -0.1331, +0.1368, +0.4170, -0.8095 + ], + [ + -0.6368, -1.3221, -0.4492, -1.5414, +0.4004, -2.8780, -0.1748, + -0.8166, +1.7066, +1.0714, -0.4755, +0.3020, +0.0422, +0.3466, + +0.4472, -0.6209, -3.3768, -0.0806, +1.3624, -2.4155, +1.0886, + +0.3412, +0.0891, +1.6821, -0.5361, +0.3952, +1.5120, +0.3910, + +1.9500, -0.9065, -1.3452, +0.0904, -0.0389, +0.2817, -1.8375, + +0.8131, -1.5287, +0.3115, +1.4069, -0.3424, +1.6101, +2.6775, + +0.5516, +1.6500, -0.4138, -0.0170, +1.0008, -0.7865, +0.0551, + +2.2068, -0.0108, +0.3207, -1.1884, +0.3792, -0.6435, +0.2858, + -0.6881, +0.1554, -1.6926, -0.0975, -1.4120, -0.0827, -1.5186, +0.2526 + ], + [ + -0.2900, -0.2805, +0.9182, -0.8893, +0.7345, -0.9015, -0.2696, + +0.2344, +0.3889, +0.6790, +0.3657, -0.1995, -0.6738, -0.4166, + +0.1690, -0.3798, -0.9872, -0.2558, -0.4205, -0.6190, -0.0092, + -0.2261, -0.2738, +0.2977, -0.7348, +0.4872, +0.4776, -0.1364, + +0.5836, -0.2688, -0.4261, -0.3612, -0.3533, +0.4665, +0.0155, + +1.0116, -0.7139, -0.3707, -0.4429, -0.0383, +0.6716, +0.5972, + +0.3506, +0.3294, -1.3734, -0.5905, -0.1168, -0.2609, +0.3436, + +0.8277, +0.4965, +0.3005, -0.2929, +0.1501, -0.2655, +0.3860, + -0.3946, +0.8764, +0.7927, +0.0541, -1.0912, -0.2006, -0.6928, +0.4653 + ]]) + +weights_dense1_b = np.array([ + -0.1146, +0.2897, +0.0137, +0.0822, +0.0367, +0.0951, -0.0657, +0.0653, + -0.0729, -0.0501, -0.6380, -0.4403, +0.0660, +0.0693, -0.4353, -0.2766, + -0.1258, -0.6947, -0.1616, -0.0453, +0.1498, -0.2340, -0.0764, +0.2020, + +0.4812, +0.0908, -0.1883, -0.0753, -0.0373, -0.4172, -0.1071, +0.0861, + -0.1550, -0.0648, -0.1473, +0.1507, -0.0121, -0.5468, -0.1529, -0.3341, + +0.0239, -0.0463, -0.0044, -0.0541, +0.0384, +0.3028, +0.3378, +0.0965, + +0.0740, +0.1948, -0.1655, +0.1558, +0.1367, -0.1514, +0.0540, -0.0015, + -0.1256, +0.3402, -0.0273, -0.2239, -0.0073, -0.6246, +0.0755, -0.2002 +]) weights_dense2_w = np.array([ -[ +0.5019, +0.3831, +0.6726, +0.3767, +0.2021, -0.1615, +0.3882, -0.0487, -0.2713, +0.1173, -0.2218, +0.0598, +0.0819, -0.1157, +0.5879, -0.3587, +0.1376, -0.2595, +0.0257, -0.1182, +0.0723, +0.5612, -0.4087, -0.4651, +0.0631, +0.1786, +0.1206, +0.4791, +0.5922, -0.4444, +0.3446, -0.0464], -[ -0.0485, +0.0739, -0.6915, +0.5446, -0.2461, +0.1557, +0.8993, -0.7537, +0.1149, +0.0575, -0.1714, -0.3796, +0.3508, -0.2315, +0.4389, -1.4456, -1.3490, -0.1598, -1.0354, -0.2320, -0.3765, +0.1070, -0.7107, +0.4150, +0.2711, -0.2915, -0.7957, +0.7753, -0.0425, -0.1352, +0.3018, -0.0069], -[ -0.4047, +1.0040, -0.4570, +0.3017, +0.1477, -0.0163, +0.4087, -0.6368, -0.0764, -0.0695, +0.0208, -0.2411, +0.1936, +0.0047, +0.0107, -0.8538, -0.5887, -0.0524, -1.4902, +0.2858, +0.4396, -0.3433, -0.6778, -0.7137, +0.4587, +0.3359, -0.7350, -1.0813, -0.1296, +0.1748, -0.3830, -0.2103], -[ +0.0503, -0.3342, -0.6057, +0.2217, +0.3164, -0.1881, -0.5867, -0.2471, -0.2527, -0.0444, +0.1874, -0.0960, +0.2039, -0.0488, +0.1741, -0.1623, -0.0758, -0.2354, -0.5986, -0.2129, -0.2470, +0.3317, -0.4795, -0.6380, +0.1494, +0.0115, -0.2746, -0.8428, -0.0118, -0.0604, +0.0886, -0.0408], -[ -0.1932, -1.3896, +0.3919, -0.4700, -0.9806, -0.1554, +0.3132, +0.4138, -0.4943, -0.1408, -0.0976, +0.1551, -0.0180, +0.0864, -0.0053, -0.2430, +0.4948, +0.2709, -0.3488, +0.2085, -0.2124, -0.3025, -0.0692, +0.3884, +0.5764, +0.5783, +0.4351, -0.2633, -0.9288, +0.2218, -0.9049, -0.2970], -[ -0.2841, -0.3393, -0.1062, -0.1415, +0.0257, +0.0816, -0.4833, -0.2775, +0.0308, -0.0344, +0.5451, +0.1588, -0.7454, -0.1444, +0.4189, -0.2001, -2.0586, -0.0616, -1.4463, +0.0076, -0.7703, +0.3279, -0.7009, +0.6046, -0.1615, -0.5188, -0.7503, +0.0615, +0.1815, -0.2512, +0.0321, -0.1834], -[ +0.3751, +0.2932, -0.6815, +0.3771, +0.0603, -0.2035, -0.2644, -1.0120, -0.0661, -0.0846, +0.1209, +0.0367, +0.0493, -0.2603, -0.1608, -0.7580, -0.8609, +0.1415, -0.7626, -1.0209, -0.7498, -0.0732, -0.8138, -0.2292, +0.5803, -0.2718, -1.4684, -0.1584, +0.2096, +0.1336, +0.3632, +0.0216], -[ -0.0625, -0.1233, -0.2715, +0.5541, +0.3121, +0.0265, +0.4501, -1.1024, -0.1160, -0.1005, -0.0844, -0.0516, +0.0916, +0.0901, +0.3710, -0.5753, -0.3728, -0.1103, -0.6285, -0.2179, +0.1570, +0.1168, -0.9312, +0.0135, -0.0376, -0.1693, -0.5358, -0.0028, +0.2105, -0.7373, +0.2776, +0.2326], -[ -0.5378, -0.3201, +0.3606, +0.1331, +0.0120, -0.2421, -0.0230, +0.4622, -0.3140, +0.0803, -0.6897, -0.4704, +0.2685, +0.0803, -0.7654, -0.1433, +0.0242, +0.0917, +0.2458, +0.0457, -0.2503, -0.1197, +0.1454, -0.1523, -0.4095, +0.1856, +0.0678, -1.0011, +0.0117, +0.1789, -0.4063, -0.0888], -[ -0.6352, -0.6358, -0.2066, +0.0758, -0.2973, -0.3014, -0.0556, -0.0912, -0.2729, -0.1492, -0.1928, -1.8768, +0.2183, +0.0801, +0.1288, -1.2493, +0.1115, +0.2797, -0.1458, +0.0062, -0.0402, -0.8945, -0.2231, -0.1154, +0.3635, -0.3021, +0.1402, -0.7347, +0.2772, +0.3182, -0.9708, +0.0376], -[ +0.6899, +0.3471, -0.5863, +0.1497, +0.1616, -0.0497, +0.3579, -0.6421, +0.4529, -0.1588, +0.9250, +0.2357, -0.0712, -0.1793, -0.0231, -0.4814, -0.7654, 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+0.1997, +0.1240 + ], + [ + -0.0067, +0.3307, -0.7751, -0.2084, +0.4740, -0.0264, -0.0768, -0.9519, + -0.0632, -0.0753, +0.3293, +0.5260, -0.6023, +0.0060, +0.2799, -0.2904, + -0.8262, -0.6644, -0.3900, -0.1461, +0.4965, +0.3996, -0.7569, +0.0612, + +0.5168, -0.5160, -0.4875, +0.3759, +0.0295, +0.1027, +0.6096, -0.0115 + ], + [ + -0.0110, +0.4652, -0.1486, -0.6029, +0.2581, -0.3184, -0.3759, +0.3213, + -0.2748, -0.0630, +0.0953, +0.2101, -1.2738, -0.1353, +0.2710, -0.2276, + +0.2586, -0.2347, -0.3320, +0.0487, -0.2318, -0.1002, +0.1236, +0.2660, + -0.1172, +0.1437, -0.0850, +0.1659, -0.2152, -0.0764, +0.2838, -0.1325 + ], + [ + +0.0152, -0.0906, -0.1897, -0.3521, -0.1836, -0.1694, -0.4150, -0.1695, + +0.0509, -0.0716, +0.3118, +0.2422, -0.5058, -0.0637, -0.1038, -0.2828, + -0.0528, -0.2051, +0.2062, -0.2105, -0.7317, +0.1881, -0.2992, -0.0883, + +0.0115, -1.5295, -0.1671, +0.0411, +0.0648, -0.0119, -0.2941, +0.0273 + ], + [ + +0.5028, +0.1780, -0.4643, -0.0373, +0.3067, -0.1974, +0.2643, -0.2365, + -0.2083, +0.0472, +0.4830, +0.0630, +0.2155, -0.0916, +0.6290, -0.4427, + -0.6266, +0.3576, -0.3541, -0.2034, +0.3733, +0.8247, -0.5837, -0.4372, + +0.2696, -0.4042, -0.3436, +0.0355, -0.2288, -0.6382, +0.7358, -0.1229 + ] ]) -weights_dense2_b = np.array([ -0.0730, +0.0456, +0.0877, -0.2607, +0.0029, -0.2705, -0.1420, +0.2403, -0.2135, -0.0646, +0.1378, +0.1105, -0.4639, -0.0583, -0.0872, -0.1473, +0.1460, -0.0234, +0.0740, -0.0745, -0.1283, +0.0316, +0.0361, -0.0726, -0.0304, +0.0417, -0.0313, +0.0935, +0.0815, +0.0814, +0.0818, -0.1111]) - -weights_final_w = np.array([ -[ +1.0397], -[ +0.7049], -[ -0.2128], -[ +0.2172], -[ +0.3027], -[ -0.1991], -[ +0.3398], -[ -0.5932], -[ -0.1439], -[ -0.0236], -[ +0.5679], -[ +0.8571], -[ +0.1934], -[ -0.1652], -[ +0.6933], -[ -0.5510], -[ -1.0587], -[ +0.6996], -[ -0.5009], -[ -0.4000], -[ -0.6958], -[ +0.7716], -[ -0.5342], -[ -0.5095], -[ +0.3040], -[ -1.1986], -[ -0.4900], -[ +0.7726], -[ +0.5871], -[ -0.2533], -[ +0.2633], -[ -0.0004] +weights_dense2_b = np.array([ + -0.0730, +0.0456, +0.0877, -0.2607, +0.0029, -0.2705, -0.1420, +0.2403, + -0.2135, -0.0646, +0.1378, +0.1105, -0.4639, -0.0583, -0.0872, -0.1473, + +0.1460, -0.0234, +0.0740, -0.0745, -0.1283, +0.0316, +0.0361, -0.0726, + -0.0304, +0.0417, -0.0313, +0.0935, +0.0815, +0.0814, +0.0818, -0.1111 ]) -weights_final_b = np.array([ +0.0190]) +weights_final_w = np.array([[+1.0397], [+0.7049], [-0.2128], [+0.2172], + [+0.3027], [-0.1991], [+0.3398], [-0.5932], + [-0.1439], [-0.0236], [+0.5679], [+0.8571], + [+0.1934], [-0.1652], [+0.6933], [-0.5510], + [-1.0587], [+0.6996], [-0.5009], [-0.4000], + [-0.6958], [+0.7716], [-0.5342], [-0.5095], + [+0.3040], [-1.1986], [-0.4900], [+0.7726], + [+0.5871], [-0.2533], [+0.2633], [-0.0004]]) + +weights_final_b = np.array([+0.0190]) +# yapf: enable -if __name__=="__main__": - main() +if __name__ == "__main__": + main() diff --git a/examples/pybullet/gym/pybullet_envs/examples/enjoy_TF_InvertedPendulumBulletEnv_v0_2017may.py b/examples/pybullet/gym/pybullet_envs/examples/enjoy_TF_InvertedPendulumBulletEnv_v0_2017may.py index 9235d167d..e4e84dd93 100644 --- a/examples/pybullet/gym/pybullet_envs/examples/enjoy_TF_InvertedPendulumBulletEnv_v0_2017may.py +++ b/examples/pybullet/gym/pybullet_envs/examples/enjoy_TF_InvertedPendulumBulletEnv_v0_2017may.py @@ -2,174 +2,537 @@ import os, inspect currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe()))) parentdir = os.path.dirname(os.path.dirname(currentdir)) -os.sys.path.insert(0,parentdir) +os.sys.path.insert(0, parentdir) import gym import numpy as np import pybullet_envs import time + def relu(x): - return np.maximum(x, 0) + return np.maximum(x, 0) + class SmallReactivePolicy: - "Simple multi-layer perceptron policy, no internal state" - def __init__(self, observation_space, action_space): - assert weights_dense1_w.shape == (observation_space.shape[0], 64) - assert weights_dense2_w.shape == (64, 32) - assert weights_final_w.shape == (32, action_space.shape[0]) - - def act(self, ob): - x = ob - x = relu(np.dot(x, weights_dense1_w) + weights_dense1_b) - x = relu(np.dot(x, weights_dense2_w) + weights_dense2_b) - x = np.dot(x, weights_final_w) + weights_final_b - return x + "Simple multi-layer perceptron policy, no internal state" + + def __init__(self, observation_space, action_space): + assert weights_dense1_w.shape == (observation_space.shape[0], 64) + assert weights_dense2_w.shape == (64, 32) + assert weights_final_w.shape == (32, action_space.shape[0]) + + def act(self, ob): + x = ob + x = relu(np.dot(x, weights_dense1_w) + weights_dense1_b) + x = relu(np.dot(x, weights_dense2_w) + weights_dense2_b) + x = np.dot(x, weights_final_w) + weights_final_b + return x + def main(): - print("create env") - env = gym.make("InvertedPendulumBulletEnv-v0") - env.render(mode="human") - pi = SmallReactivePolicy(env.observation_space, env.action_space) + print("create env") + env = gym.make("InvertedPendulumBulletEnv-v0") + env.render(mode="human") + pi = SmallReactivePolicy(env.observation_space, env.action_space) + while 1: + frame = 0 + score = 0 + restart_delay = 0 + obs = env.reset() + print("frame") while 1: - frame = 0 - score = 0 - restart_delay = 0 - obs = env.reset() - print("frame") - while 1: - time.sleep(1./60.) - a = pi.act(obs) - obs, r, done, _ = env.step(a) - score += r - frame += 1 - still_open = env.render("human") - if still_open==False: - return - if not done: continue - if restart_delay==0: - print("score=%0.2f in %i frames" % (score, frame)) - restart_delay = 60*2 # 2 sec at 60 fps - else: - restart_delay -= 1 - if restart_delay==0: break - -weights_dense1_w = np.array([ -[ +0.8621, +0.3260, +0.0986, -0.1225, +0.2038, -0.8051, -0.7498, +0.1905, -0.3418, +0.5002, -0.1093, +0.0285, +0.3480, -0.1596, -0.1781, +0.3643, -0.4283, -0.3715, -0.1571, +0.3531, +0.0934, -0.2215, -0.3085, +0.9581, +0.2485, -0.6232, -0.3175, +0.9771, +0.3651, -0.8850, -0.4212, -0.0301, +0.0432, +0.3390, +0.7537, +0.1649, -0.0128, -0.1374, +0.3793, +1.0430, +0.8043, -0.9001, +0.4334, -0.1243, +1.2373, +0.1890, +0.3333, -0.0520, +0.1654, +0.2521, -0.0168, +0.8439, -0.6960, +0.1884, +0.0991, +0.5242, -0.6837, +0.6844, -0.2593, -0.3298, +0.2212, +0.0281, +0.2608, +0.6527], -[ -0.9350, -0.2122, +0.0162, +0.5306, -0.2914, -0.8573, +0.2552, +0.7069, +0.7862, -0.0315, -1.0844, +0.2707, +0.5102, -1.1359, +0.3066, +0.0357, +0.1833, -0.1946, +0.0948, +0.6685, -0.6101, +0.4774, -0.3017, +0.3823, -0.2835, -0.6760, +1.2963, +0.4466, -0.7132, -0.9109, -0.0589, -0.8726, +0.6972, -0.2256, -0.0286, +0.4646, -0.5113, -0.1692, +0.7638, +0.2274, -0.5734, +0.7430, +0.9680, +0.7809, -0.2457, -0.4952, +0.0197, -0.6428, +0.2367, -0.5887, -0.5167, +0.2299, -0.5853, -0.4101, +0.9042, +0.0913, +0.5774, +0.2756, +0.2436, -0.6068, -0.2232, -0.1415, -0.5094, -0.1012], -[ +0.0983, -0.3266, +0.2611, +0.0664, +0.6222, +0.0773, -0.2516, -0.4416, -0.3770, +0.0535, +0.3391, -0.7475, +0.5874, -0.0405, -0.2058, -0.5957, +0.2659, -0.8477, -0.5814, -0.0494, -0.1678, +0.2650, -0.4039, +0.1414, -0.6635, +0.0447, +0.2932, +0.1167, +0.1195, +0.0669, -0.4223, +0.1196, +0.0553, -0.7123, -0.4011, +0.3557, -0.4503, +0.7047, -0.4471, +0.0807, +0.3926, -0.1427, +0.4355, -0.3678, +0.3453, +0.1597, -0.3076, +0.4689, +0.3128, -0.7050, +0.6505, +0.3427, +0.1981, +0.1190, +0.2554, +0.8283, +0.1647, -0.4257, +0.1481, +0.4361, -0.5497, -0.6114, -0.0138, +0.0932], -[ +0.1866, +0.6408, -1.8201, +1.0946, +0.7742, -0.7651, +0.1082, +0.6842, +0.3794, +0.3547, -0.8172, -0.0921, -0.6736, +1.0251, -0.9618, -0.6869, +1.8465, +0.2425, +0.7910, +1.0009, -0.8031, +1.6697, -0.8962, +0.1873, +0.4960, -0.6812, +0.6860, +1.1932, -0.7019, +0.4028, +0.4841, +0.6497, +1.6490, -0.5464, +0.7060, +1.8087, -0.6118, -0.7955, -0.3797, -1.2048, +1.2356, -0.6141, +1.2502, +0.5641, -0.1019, -1.7516, -0.1134, -0.6719, +1.5014, -0.2718, -0.5933, +0.1714, -1.3590, -0.3656, +1.0083, -0.8511, -0.5597, -0.4446, -1.7158, -0.0851, +0.3089, +0.0967, -1.0121, +0.3048], -[ +0.3329, +0.5382, +0.1585, +0.8205, +0.5510, +0.2796, -0.7120, +0.3434, +0.2931, +1.2275, +0.4191, -0.6828, -0.5091, +0.8408, -0.3101, -0.5183, +0.2651, +0.2073, -0.1383, +0.6539, -0.2167, +0.7798, -0.5690, +0.3750, +0.4358, +0.6537, -0.2202, -0.0563, +0.6605, +0.4599, -0.5327, +0.6610, +0.8387, +0.1887, -0.2593, +0.7904, -0.3567, +0.4121, +0.4378, -0.2935, +0.1291, +0.0021, -0.3416, -0.5920, -0.2895, -0.4610, +0.7380, -0.6322, +0.6738, -0.1378, -0.3304, -0.2894, -0.3582, -0.8311, +0.2660, -0.2079, -0.1765, -0.6825, -0.1754, -0.4455, +0.7202, -0.8177, -0.9900, -0.7425] -]) + time.sleep(1. / 60.) + a = pi.act(obs) + obs, r, done, _ = env.step(a) + score += r + frame += 1 + still_open = env.render("human") + if still_open == False: + return + if not done: continue + if restart_delay == 0: + print("score=%0.2f in %i frames" % (score, frame)) + restart_delay = 60 * 2 # 2 sec at 60 fps + else: + restart_delay -= 1 + if restart_delay == 0: break + -weights_dense1_b = np.array([ +0.0009, -0.2179, -0.0299, +0.2069, -0.0099, +0.0907, +0.0271, +0.1957, +0.0185, +0.1671, -0.0699, -0.0332, -0.0244, +0.0022, +0.1877, -0.0801, +0.0235, -0.0097, -0.0088, +0.1961, -0.1055, +0.0605, -0.0913, +0.0884, -0.0638, +0.0229, -0.1101, +0.1966, -0.0042, +0.0221, -0.0966, +0.1554, +0.1623, -0.0454, +0.1068, +0.0114, +0.0544, +0.0201, +0.0257, +0.0637, +0.0761, +0.2120, +0.0225, +0.2023, -0.0931, +0.0585, -0.2253, -0.0302, +0.0682, +0.0000, -0.1215, -0.1105, +0.1376, +0.0583, -0.1706, -0.0262, -0.0897, +0.0728, +0.0787, +0.0912, -0.0964, -0.0959, -0.0195, +0.0232]) +# yapf: disable +weights_dense1_w = np.array( + [[ + +0.8621, +0.3260, +0.0986, -0.1225, +0.2038, -0.8051, -0.7498, +0.1905, + -0.3418, +0.5002, -0.1093, +0.0285, +0.3480, -0.1596, -0.1781, +0.3643, + -0.4283, -0.3715, -0.1571, +0.3531, +0.0934, -0.2215, -0.3085, +0.9581, + +0.2485, -0.6232, -0.3175, +0.9771, +0.3651, -0.8850, -0.4212, -0.0301, + +0.0432, +0.3390, +0.7537, +0.1649, -0.0128, -0.1374, +0.3793, +1.0430, + +0.8043, -0.9001, +0.4334, -0.1243, +1.2373, +0.1890, +0.3333, -0.0520, + +0.1654, +0.2521, -0.0168, +0.8439, -0.6960, +0.1884, +0.0991, +0.5242, + -0.6837, +0.6844, -0.2593, -0.3298, +0.2212, +0.0281, +0.2608, +0.6527 + ], + [ + -0.9350, -0.2122, +0.0162, +0.5306, -0.2914, -0.8573, +0.2552, + +0.7069, +0.7862, -0.0315, -1.0844, +0.2707, +0.5102, -1.1359, + +0.3066, +0.0357, +0.1833, -0.1946, +0.0948, +0.6685, -0.6101, + +0.4774, -0.3017, +0.3823, -0.2835, -0.6760, +1.2963, +0.4466, + -0.7132, -0.9109, -0.0589, -0.8726, +0.6972, -0.2256, -0.0286, + +0.4646, -0.5113, -0.1692, +0.7638, +0.2274, -0.5734, +0.7430, + +0.9680, +0.7809, -0.2457, -0.4952, +0.0197, -0.6428, +0.2367, + -0.5887, -0.5167, +0.2299, -0.5853, -0.4101, +0.9042, +0.0913, + +0.5774, +0.2756, +0.2436, -0.6068, -0.2232, -0.1415, -0.5094, -0.1012 + ], + [ + +0.0983, -0.3266, +0.2611, 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-0.1134, -0.6719, +1.5014, + -0.2718, -0.5933, +0.1714, -1.3590, -0.3656, +1.0083, -0.8511, + -0.5597, -0.4446, -1.7158, -0.0851, +0.3089, +0.0967, -1.0121, +0.3048 + ], + [ + +0.3329, +0.5382, +0.1585, +0.8205, +0.5510, +0.2796, -0.7120, + +0.3434, +0.2931, +1.2275, +0.4191, -0.6828, -0.5091, +0.8408, + -0.3101, -0.5183, +0.2651, +0.2073, -0.1383, +0.6539, -0.2167, + +0.7798, -0.5690, +0.3750, +0.4358, +0.6537, -0.2202, -0.0563, + +0.6605, +0.4599, -0.5327, +0.6610, +0.8387, +0.1887, -0.2593, + +0.7904, -0.3567, +0.4121, +0.4378, -0.2935, +0.1291, +0.0021, + -0.3416, -0.5920, -0.2895, -0.4610, +0.7380, -0.6322, +0.6738, + -0.1378, -0.3304, -0.2894, -0.3582, -0.8311, +0.2660, -0.2079, + -0.1765, -0.6825, -0.1754, -0.4455, +0.7202, -0.8177, -0.9900, -0.7425 + ]]) + +weights_dense1_b = np.array([ + +0.0009, -0.2179, -0.0299, +0.2069, -0.0099, +0.0907, +0.0271, +0.1957, + +0.0185, +0.1671, -0.0699, -0.0332, -0.0244, +0.0022, +0.1877, -0.0801, + +0.0235, -0.0097, -0.0088, +0.1961, -0.1055, +0.0605, -0.0913, +0.0884, + -0.0638, +0.0229, -0.1101, +0.1966, -0.0042, +0.0221, -0.0966, +0.1554, + +0.1623, -0.0454, +0.1068, +0.0114, +0.0544, +0.0201, +0.0257, +0.0637, + +0.0761, +0.2120, +0.0225, +0.2023, -0.0931, +0.0585, -0.2253, -0.0302, + +0.0682, +0.0000, -0.1215, -0.1105, +0.1376, +0.0583, -0.1706, -0.0262, + -0.0897, +0.0728, +0.0787, +0.0912, -0.0964, -0.0959, -0.0195, +0.0232 +]) weights_dense2_w = np.array([ -[ +0.0089, +0.2241, -0.0391, +0.1459, -0.0854, -0.0878, +0.2829, -0.1620, -0.1694, -0.5211, +0.0155, -0.1298, -0.0629, +0.1074, +0.0150, -0.3583, +0.0427, +0.1813, +0.2140, -0.4230, +0.1577, +0.1223, -0.0096, +0.0183, -0.1038, -0.5612, -0.0614, -0.0820, -0.0057, -0.2471, +0.0355, -0.1525], -[ +0.1555, -0.2934, +0.2690, -0.3218, +0.0101, -0.1188, -0.1798, -0.1405, +0.2701, -0.0972, +0.2338, -0.0122, -0.2254, -0.3225, -0.0268, -0.0829, +0.4085, +0.0691, -0.1448, -0.0429, -0.2750, -0.2479, +0.0396, +0.0427, +0.0205, -0.1462, -0.1481, +0.1365, -0.0903, +0.0094, +0.3665, +0.1163], -[ +0.0119, -0.3100, +0.1201, -0.2257, +0.1246, -0.1335, -0.3369, -0.0408, +0.3145, +0.2030, +0.1506, +0.0899, -0.1192, -0.2429, +0.0356, +0.0634, -0.0706, +0.1119, -0.0402, +0.1011, +0.1281, +0.4318, -0.4644, +0.0039, +0.0932, -0.0521, -0.1528, +0.1946, -0.0921, -0.0646, -0.0241, +0.1598], -[ -0.1007, +0.3939, -0.2066, +0.0752, -0.1709, -0.0286, +0.0196, +0.1853, -0.3619, -0.0449, +0.0334, -0.2673, +0.0640, +0.3055, -0.1184, -0.4550, +0.0951, -0.2168, +0.1502, -0.4816, +0.1392, -0.3708, -0.0849, -0.4331, -0.0800, -0.0967, +0.1334, -0.3169, -0.0004, -0.3002, -0.0841, -0.1763], -[ -0.0492, +0.0308, +0.0824, +0.0568, -0.0038, +0.3196, +0.5089, +0.0391, -0.1373, -0.1579, +0.0219, -0.2990, -0.0113, -0.2136, -0.0240, +0.1241, -0.1723, -0.0064, -0.0213, -0.2213, -0.0996, -0.0333, -0.4110, -0.2074, +0.0427, +0.0323, -0.0920, -0.1846, -0.1037, -0.0381, -0.0763, +0.0875], -[ +0.0965, -0.1536, -0.0270, -0.0834, +0.0270, +0.0908, -0.0257, -0.1284, +0.1994, +0.2317, +0.0193, 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+0.0298, -0.1391, +0.1229, +0.1193, +0.0565, +0.1296, +0.0939, -0.0234], -[ +0.1817, +0.2432, -0.2712, +0.0668, -0.1836, +0.0232, -0.0793, +0.0161, -0.1585, -0.3731, -0.0243, -0.1066, +0.0928, -0.0499, -0.0692, -0.3354, +0.0754, +0.0468, -0.2522, -0.7501, +0.0235, -0.5134, -0.3031, -0.1907, -0.2166, -0.1713, -0.0422, +0.0831, +0.0664, -0.0462, +0.1627, -0.4927], -[ -0.0342, +0.2310, +0.2736, -0.0703, +0.1941, -0.0428, -0.0868, -0.2146, +0.1371, +0.0117, +0.0218, +0.0133, -0.0416, +0.1012, +0.1689, +0.3113, +0.0199, +0.1176, +0.0256, +0.0907, +0.0622, +0.3312, -0.0225, -0.0187, +0.2089, +0.1381, -0.2949, +0.1525, -0.0514, -0.1416, -0.0381, -0.0133], -[ -0.0885, +0.3841, -0.3811, +0.1388, -0.1801, -0.0434, +0.1371, -0.0393, +0.2549, -0.4207, -0.2308, +0.0187, -0.0975, +0.2137, -0.0840, -0.0491, +0.0424, +0.0060, +0.1007, +0.0315, +0.3005, +0.0501, +0.0516, -0.0521, -0.0100, +0.0984, +0.3092, +0.0031, -0.0380, +0.2344, +0.0808, -0.0694], -[ -0.0631, +0.0290, +0.1733, -0.0555, +0.1311, 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-0.4457, +0.1013, + +0.2494, +0.1487, +0.1013, -0.0403, -0.0236, -0.1965, -0.0655, +0.0818, + +0.0493, -0.0605, -0.1889, +0.1772, -0.2826, +0.2783, -0.1653, +0.3505, + +0.4192, -0.1048, -0.1459, +0.0779, -0.0154, -0.1573, -0.1254, -0.1118 + ], + [ + -0.1817, +0.0719, +0.1352, +0.3208, +0.2142, -0.1149, +0.0020, +0.1617, + +0.1055, +0.0395, -0.1802, -0.0631, -0.3172, +0.1971, +0.0197, +0.1271, + -0.2375, -0.1849, -0.0134, +0.1223, +0.2566, +0.0311, -0.2746, +0.0278, + +0.1233, +0.0167, -0.0363, +0.2146, -0.0466, +0.0732, -0.1490, +0.1040 + ], + [ + +0.1008, -0.1501, +0.0264, -0.4661, -0.0553, +0.0431, -0.3076, -0.0461, + +0.1393, -0.1225, +0.2811, -0.0363, -0.0403, -0.3370, -0.0865, -0.1179, + +0.1106, +0.2035, -0.2432, -0.0859, +0.0600, -0.0890, -0.0749, +0.0483, + +0.0615, -0.0239, -0.4674, +0.0199, +0.0669, +0.1410, +0.1846, +0.2626 + ], + [ + +0.0663, +0.1486, -0.3928, +0.3257, -0.0316, +0.1377, +0.0418, +0.1921, + -0.1616, -0.2265, -0.0917, +0.1582, -0.0537, +0.0295, -0.2264, -0.1921, + -0.0225, +0.0928, +0.0747, -0.5268, -0.0068, -0.3328, +0.0437, -0.2361, + -0.1408, -0.1234, +0.2216, -0.1372, -0.0499, +0.1940, +0.0098, -0.2953 + ], + [ + +0.0290, -0.1583, -0.0172, -0.1748, -0.0042, -0.0725, -0.2227, -0.1366, + -0.1771, +0.1987, +0.3142, +0.1889, +0.0195, -0.5461, +0.0921, +0.1407, + -0.1656, +0.1985, +0.0113, +0.2613, +0.2925, +0.1166, -0.1286, +0.1031, + -0.2228, -0.0605, -0.2151, +0.2477, +0.1602, -0.0109, +0.0207, +0.1257 + ], + [ + +0.0630, -0.1688, +0.1662, -0.2327, +0.2832, +0.1350, -0.1658, +0.0504, + -0.0502, +0.1736, +0.1002, -0.0051, -0.0311, -0.0628, +0.0039, +0.5085, + -0.2191, +0.5105, -0.0927, +0.2833, -0.2828, +0.1078, +0.0406, -0.0392, + -0.2372, +0.1508, +0.0556, +0.0313, +0.1296, +0.1315, -0.1143, +0.1632 + ] ]) -weights_dense2_b = np.array([ -0.0655, +0.0020, +0.0358, -0.0192, +0.0570, +0.0000, +0.0711, -0.0145, +0.0294, +0.0139, -0.0215, -0.0952, +0.0000, +0.0526, -0.0585, +0.0633, -0.0332, +0.0030, +0.0107, +0.0830, +0.0140, +0.0888, -0.1115, -0.0722, +0.0240, +0.0476, -0.0807, -0.0421, +0.0000, -0.0557, -0.0403, +0.0034]) - -weights_final_w = np.array([ -[ -0.0230], -[ +0.0730], -[ -0.2093], -[ +0.0463], -[ -0.1983], -[ -0.0031], -[ +0.2101], -[ -0.0066], -[ -0.1481], -[ -0.1615], -[ -0.1766], -[ +0.1332], -[ -0.0012], -[ +0.2332], -[ -0.0380], -[ -0.3066], -[ -0.1738], -[ -0.2982], -[ +0.0285], -[ -0.1548], -[ +0.2539], -[ -0.2544], -[ +0.2006], -[ -0.4121], -[ -0.2084], -[ -0.0381], -[ +0.2733], -[ -0.3076], -[ +0.0013], -[ +0.0957], -[ -0.1298], -[ -0.1112] +weights_dense2_b = np.array([ + -0.0655, +0.0020, +0.0358, -0.0192, +0.0570, +0.0000, +0.0711, -0.0145, + +0.0294, +0.0139, -0.0215, -0.0952, +0.0000, +0.0526, -0.0585, +0.0633, + -0.0332, +0.0030, +0.0107, +0.0830, +0.0140, +0.0888, -0.1115, -0.0722, + +0.0240, +0.0476, -0.0807, -0.0421, +0.0000, -0.0557, -0.0403, +0.0034 ]) -weights_final_b = np.array([ +0.0352]) +weights_final_w = np.array([[-0.0230], [+0.0730], [-0.2093], [+0.0463], + [-0.1983], [-0.0031], [+0.2101], [-0.0066], + [-0.1481], [-0.1615], [-0.1766], [+0.1332], + [-0.0012], [+0.2332], [-0.0380], [-0.3066], + [-0.1738], [-0.2982], [+0.0285], [-0.1548], + [+0.2539], [-0.2544], [+0.2006], [-0.4121], + [-0.2084], [-0.0381], [+0.2733], [-0.3076], + [+0.0013], [+0.0957], [-0.1298], [-0.1112]]) + +weights_final_b = np.array([+0.0352]) +# yapf: enable -if __name__=="__main__": - main() +if __name__ == "__main__": + main() diff --git a/examples/pybullet/gym/pybullet_envs/examples/enjoy_TF_InvertedPendulumSwingupBulletEnv_v0_2017may.py b/examples/pybullet/gym/pybullet_envs/examples/enjoy_TF_InvertedPendulumSwingupBulletEnv_v0_2017may.py index 5ce5bae7f..8c1ccd08d 100644 --- a/examples/pybullet/gym/pybullet_envs/examples/enjoy_TF_InvertedPendulumSwingupBulletEnv_v0_2017may.py +++ b/examples/pybullet/gym/pybullet_envs/examples/enjoy_TF_InvertedPendulumSwingupBulletEnv_v0_2017may.py @@ -2,175 +2,538 @@ import os, inspect currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe()))) parentdir = os.path.dirname(os.path.dirname(currentdir)) -os.sys.path.insert(0,parentdir) +os.sys.path.insert(0, parentdir) import gym import numpy as np import pybullet_envs import time + def relu(x): - return np.maximum(x, 0) + return np.maximum(x, 0) + class SmallReactivePolicy: - "Simple multi-layer perceptron policy, no internal state" - def __init__(self, observation_space, action_space): - assert weights_dense1_w.shape == (observation_space.shape[0], 64.0) - assert weights_dense2_w.shape == (64.0, 32.0) - assert weights_final_w.shape == (32.0, action_space.shape[0]) - - def act(self, ob): - x = ob - x = relu(np.dot(x, weights_dense1_w) + weights_dense1_b) - x = relu(np.dot(x, weights_dense2_w) + weights_dense2_b) - x = np.dot(x, weights_final_w) + weights_final_b - return x + "Simple multi-layer perceptron policy, no internal state" + + def __init__(self, observation_space, action_space): + assert weights_dense1_w.shape == (observation_space.shape[0], 64.0) + assert weights_dense2_w.shape == (64.0, 32.0) + assert weights_final_w.shape == (32.0, action_space.shape[0]) + + def act(self, ob): + x = ob + x = relu(np.dot(x, weights_dense1_w) + weights_dense1_b) + x = relu(np.dot(x, weights_dense2_w) + weights_dense2_b) + x = np.dot(x, weights_final_w) + weights_final_b + return x + def main(): - env = gym.make("InvertedPendulumSwingupBulletEnv-v0") - env.render(mode="human") - - pi = SmallReactivePolicy(env.observation_space, env.action_space) + env = gym.make("InvertedPendulumSwingupBulletEnv-v0") + env.render(mode="human") + + pi = SmallReactivePolicy(env.observation_space, env.action_space) + + while 1: + frame = 0 + score = 0 + restart_delay = 0 + obs = env.reset() while 1: - frame = 0 - score = 0 - restart_delay = 0 - obs = env.reset() - - while 1: - time.sleep(1./60.) - a = pi.act(obs) - obs, r, done, _ = env.step(a) - score += r - frame += 1 - still_open = env.render("human") - if still_open==False: - return - if not done: continue - if restart_delay==0: - print("score=%0.2f in %i frames" % (score, frame)) - restart_delay = 60*2 # 2 sec at 60 fps - else: - restart_delay -= 1 - if restart_delay > 0: continue - break - -weights_dense1_w = np.array([ -[ +0.5877, -0.5825, -0.5542, -0.2557, -0.4485, +1.4126, +0.2701, -0.6204, -0.2580, +0.2106, -0.2296, +0.7949, +0.6224, -0.0186, +0.4216, +1.0924, -0.1538, -0.2818, +0.4855, -0.2496, +0.7461, -0.6156, +0.0801, +0.7871, -0.4312, -0.9585, +0.1566, -0.2218, -1.0393, +0.6104, -0.5339, +0.8258, +0.4064, +0.0503, +0.4753, -0.8161, +0.0812, +0.2311, -0.9492, -1.1791, +1.2375, +0.2916, +1.2290, +0.2796, -0.8864, -1.1424, -0.5714, +0.1413, +0.7340, -0.4152, +0.2832, -0.3886, +0.4810, -0.7092, -0.5966, +0.1089, +0.1007, +0.5226, -0.3343, +0.1760, +0.4099, -0.9913, -1.1694, -1.0018], -[ +0.4054, +0.2495, +0.5483, +0.7193, -0.1833, -0.2237, -0.4353, -0.1005, +0.2848, +0.3193, +0.2551, -0.1267, -0.7200, +0.3952, +0.3390, +0.2123, +0.1388, +0.8869, -0.1095, -0.1718, -0.4128, -0.7047, -1.1383, +0.6552, -0.0037, -0.4306, +0.2749, -0.9121, +0.4406, -0.0163, +0.4852, +0.6150, +0.1354, -0.7839, +0.2261, +0.3988, -0.2867, -0.5369, -0.0788, +0.0125, +0.2645, +0.1614, +0.7531, +0.5786, +0.6903, -0.7974, -0.2934, -0.3407, -0.7366, -0.1585, +1.0333, -0.0183, +0.2690, -0.5674, -0.0266, +0.0898, -0.1441, -0.0988, +0.7260, +0.7994, +0.1521, -0.3210, -0.1403, -0.2685], -[ -0.1050, -0.1826, +0.4717, -0.3515, +0.9648, -0.6372, -0.4686, +0.6959, +0.3540, +0.3515, +0.3239, -1.6177, -0.0651, +0.4653, +0.5058, +0.3465, -0.6693, -0.1118, -0.9582, -1.5053, -0.2256, -0.1989, -0.1901, -0.4282, -1.3479, -0.5629, +0.6828, -0.3515, -0.4724, +0.4618, +0.3008, +0.1280, +0.3720, -0.0545, +0.3104, -0.2527, +0.4614, +0.4994, -0.0099, +0.4597, -0.2667, -0.0374, -0.3393, +0.2675, -0.2635, -0.6062, +0.6404, +0.4500, -0.5105, -1.5838, -0.1396, +0.8804, +0.5794, -0.6823, -0.2125, +0.4510, +0.2424, +0.3407, -0.3354, +0.1306, -1.0006, +0.2358, +0.6479, +0.2027], -[ +0.7453, +0.8937, -0.9068, +0.2950, +0.4412, -0.6005, -1.3008, -0.0299, -0.6434, +1.4992, +0.7437, +0.4271, -0.0549, +1.2337, +1.6758, -0.7335, +0.2251, -1.1287, -1.0611, -0.4609, -1.6821, -0.3495, -0.5520, +0.2407, -1.0738, +0.9423, -0.6853, -0.0193, +0.6365, +0.3979, -1.8896, -1.1404, +0.4708, -0.2113, +1.3380, +0.6163, +0.5543, +0.4372, -0.3004, +1.0200, -0.4211, +0.5034, -0.1635, +2.0363, +0.1362, -0.2348, +0.7659, -1.6971, -1.3513, -0.2940, +1.2592, -0.3885, +0.5544, +0.8858, +0.0189, -1.8006, +1.3254, +0.6919, +0.3571, -0.5189, -0.0115, -1.7036, -0.8770, +1.2328], -[ -0.3661, +0.5205, +0.6454, +0.9826, -0.2945, -0.3074, +0.6830, +0.3798, +0.0578, +0.2303, +0.0181, -0.3768, -0.1607, +0.9089, +0.2910, -0.0265, -0.7435, +0.2932, -0.4173, +0.2959, +0.2079, +0.2649, +0.4184, +0.5963, +0.2120, +0.1885, +0.3611, +0.5193, +0.4538, +0.7072, +0.2274, +0.2233, +0.3970, +0.0560, +0.2132, +0.0186, +0.1522, -0.2460, +0.6636, +0.4592, -0.5299, +1.1159, -0.2861, +0.3664, -0.0648, +0.1958, -0.0180, -0.2585, +0.1408, +0.2639, -0.3697, +0.4727, +1.0321, +0.0851, +0.8350, +0.0830, +0.1625, -0.3849, +0.3014, -0.1514, -0.5960, -0.4083, -0.1023, +0.2080] -]) + time.sleep(1. / 60.) + a = pi.act(obs) + obs, r, done, _ = env.step(a) + score += r + frame += 1 + still_open = env.render("human") + if still_open == False: + return + if not done: continue + if restart_delay == 0: + print("score=%0.2f in %i frames" % (score, frame)) + restart_delay = 60 * 2 # 2 sec at 60 fps + else: + restart_delay -= 1 + if restart_delay > 0: continue + break + -weights_dense1_b = np.array([ -0.4441, -0.2462, -0.2997, +0.3283, -0.2751, +0.0474, +0.0720, -0.2133, -0.0770, -0.0053, +0.0138, -0.3554, -0.2999, -0.2340, +0.0054, +0.4380, -0.1461, -0.2035, -0.8094, +0.0909, -0.1714, +0.2412, -0.1519, +0.0391, +0.1525, +0.1798, +0.1041, +0.4503, -0.0088, +0.0323, -0.0414, +0.4621, +0.1720, -0.1793, +0.1734, +0.1588, +0.2802, +0.1220, +0.1011, -0.1334, -0.0663, +0.4778, +0.1110, -0.1536, +0.1873, -0.0090, -0.5979, +0.3604, -0.2515, +0.4471, +0.2444, -0.2565, -0.1102, +0.0982, -0.0625, +0.3902, -0.0248, -0.2240, +0.0894, -0.0671, -0.3344, -0.0089, -0.0793, +0.2673]) +# yapf: disable +weights_dense1_w = np.array( + [[ + +0.5877, -0.5825, -0.5542, -0.2557, -0.4485, +1.4126, +0.2701, -0.6204, + -0.2580, +0.2106, -0.2296, +0.7949, +0.6224, -0.0186, +0.4216, +1.0924, + -0.1538, -0.2818, +0.4855, -0.2496, +0.7461, -0.6156, +0.0801, +0.7871, + -0.4312, -0.9585, +0.1566, -0.2218, -1.0393, +0.6104, -0.5339, +0.8258, + +0.4064, +0.0503, +0.4753, -0.8161, +0.0812, +0.2311, -0.9492, -1.1791, + +1.2375, +0.2916, +1.2290, +0.2796, -0.8864, -1.1424, -0.5714, +0.1413, + +0.7340, -0.4152, +0.2832, -0.3886, +0.4810, -0.7092, -0.5966, +0.1089, + +0.1007, +0.5226, -0.3343, +0.1760, +0.4099, -0.9913, -1.1694, -1.0018 + ], + [ + +0.4054, +0.2495, +0.5483, +0.7193, -0.1833, -0.2237, -0.4353, + -0.1005, +0.2848, +0.3193, +0.2551, -0.1267, -0.7200, +0.3952, + +0.3390, +0.2123, +0.1388, +0.8869, -0.1095, -0.1718, -0.4128, + -0.7047, -1.1383, +0.6552, -0.0037, -0.4306, +0.2749, -0.9121, + +0.4406, -0.0163, +0.4852, +0.6150, +0.1354, -0.7839, +0.2261, + +0.3988, -0.2867, -0.5369, -0.0788, +0.0125, +0.2645, +0.1614, + +0.7531, +0.5786, +0.6903, -0.7974, -0.2934, -0.3407, -0.7366, + -0.1585, +1.0333, -0.0183, +0.2690, -0.5674, -0.0266, +0.0898, + -0.1441, -0.0988, +0.7260, +0.7994, +0.1521, -0.3210, -0.1403, -0.2685 + ], + [ + -0.1050, -0.1826, +0.4717, -0.3515, +0.9648, -0.6372, -0.4686, + +0.6959, +0.3540, +0.3515, +0.3239, -1.6177, -0.0651, +0.4653, + +0.5058, +0.3465, -0.6693, -0.1118, -0.9582, -1.5053, -0.2256, + -0.1989, -0.1901, -0.4282, -1.3479, -0.5629, +0.6828, -0.3515, + -0.4724, +0.4618, +0.3008, +0.1280, +0.3720, -0.0545, +0.3104, + -0.2527, +0.4614, +0.4994, -0.0099, +0.4597, -0.2667, -0.0374, + -0.3393, +0.2675, -0.2635, -0.6062, +0.6404, +0.4500, -0.5105, + -1.5838, -0.1396, +0.8804, +0.5794, -0.6823, -0.2125, +0.4510, + +0.2424, +0.3407, -0.3354, +0.1306, -1.0006, +0.2358, +0.6479, +0.2027 + ], + [ + +0.7453, +0.8937, -0.9068, +0.2950, +0.4412, -0.6005, -1.3008, + -0.0299, -0.6434, +1.4992, +0.7437, +0.4271, -0.0549, +1.2337, + +1.6758, -0.7335, +0.2251, -1.1287, -1.0611, -0.4609, -1.6821, + -0.3495, -0.5520, +0.2407, -1.0738, +0.9423, -0.6853, -0.0193, + +0.6365, +0.3979, -1.8896, -1.1404, +0.4708, -0.2113, +1.3380, + +0.6163, +0.5543, +0.4372, -0.3004, +1.0200, -0.4211, +0.5034, + -0.1635, +2.0363, +0.1362, -0.2348, +0.7659, -1.6971, -1.3513, + -0.2940, +1.2592, -0.3885, +0.5544, +0.8858, +0.0189, -1.8006, + +1.3254, +0.6919, +0.3571, -0.5189, -0.0115, -1.7036, -0.8770, +1.2328 + ], + [ + -0.3661, +0.5205, +0.6454, +0.9826, -0.2945, -0.3074, +0.6830, + +0.3798, +0.0578, +0.2303, +0.0181, -0.3768, -0.1607, +0.9089, + +0.2910, -0.0265, -0.7435, +0.2932, -0.4173, +0.2959, +0.2079, + +0.2649, +0.4184, +0.5963, +0.2120, +0.1885, +0.3611, +0.5193, + +0.4538, +0.7072, +0.2274, +0.2233, +0.3970, +0.0560, +0.2132, + +0.0186, +0.1522, -0.2460, +0.6636, +0.4592, -0.5299, +1.1159, + -0.2861, +0.3664, -0.0648, +0.1958, -0.0180, -0.2585, +0.1408, + +0.2639, -0.3697, +0.4727, +1.0321, +0.0851, +0.8350, +0.0830, + +0.1625, -0.3849, +0.3014, -0.1514, -0.5960, -0.4083, -0.1023, +0.2080 + ]]) + +weights_dense1_b = np.array([ + -0.4441, -0.2462, -0.2997, +0.3283, -0.2751, +0.0474, +0.0720, -0.2133, + -0.0770, -0.0053, +0.0138, -0.3554, -0.2999, -0.2340, +0.0054, +0.4380, + -0.1461, -0.2035, -0.8094, +0.0909, -0.1714, +0.2412, -0.1519, +0.0391, + +0.1525, +0.1798, +0.1041, +0.4503, -0.0088, +0.0323, -0.0414, +0.4621, + +0.1720, -0.1793, +0.1734, +0.1588, +0.2802, +0.1220, +0.1011, -0.1334, + -0.0663, +0.4778, +0.1110, -0.1536, +0.1873, -0.0090, -0.5979, +0.3604, + -0.2515, +0.4471, +0.2444, -0.2565, -0.1102, +0.0982, -0.0625, +0.3902, + -0.0248, -0.2240, +0.0894, -0.0671, -0.3344, -0.0089, -0.0793, +0.2673 +]) weights_dense2_w = np.array([ -[ +0.1063, +0.2017, +0.0029, -0.2442, -0.1362, +0.2871, +0.2270, -0.1260, +0.5271, -0.1744, 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-1.5117, +0.1874, -0.5032, -0.3292, +0.3378, -0.4749, +0.0765, +0.4345, -0.1121], -[ -0.1315, +0.2873, -1.0164, +0.2925, -0.5024, +0.2321, -1.3038, -0.7796, +0.0830, -0.3378, -0.1037, +0.0033, -0.7885, +0.4841, +0.1578, +0.1771, +0.1991, -0.1073, -0.0181, +0.0496, +0.0919, +0.0585, +0.4595, +0.1634, -0.2220, -0.0226, +0.4703, -1.8576, +0.3075, -0.4581, +0.2507, +0.2085], -[ +0.2704, +0.0379, +0.2313, -0.5561, -0.7413, -0.7693, +0.4787, +0.3033, -1.3572, -0.1323, -0.5202, -0.6937, -0.6824, -0.1782, -1.1647, -0.3461, -0.8537, +0.5416, +0.0638, -0.4208, -0.4464, +0.0009, -0.4284, +0.1806, +0.4172, -0.5477, +0.5549, +0.1937, -0.6029, +0.2084, -0.8289, -0.4554], -[ +0.3719, +0.4292, +0.2655, -0.1071, -0.1848, -0.0651, -0.4942, +0.0514, -0.1364, -0.1573, -0.0880, -0.4625, -0.0889, +0.2049, -1.2166, -0.2164, -0.3680, -0.7242, -0.1208, -0.3569, +0.0591, +0.3773, -1.2525, +0.4139, -0.1203, -0.2808, -0.2460, -0.3056, -0.2309, +0.1638, +0.1502, -0.2354], -[ +0.2204, +0.3725, +0.1919, +0.1579, 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-1.5921, -0.1360, -0.0777, -0.0092, +0.0816, +0.0996, -0.0197, -0.7934, +0.0909, +0.2011, +0.1988, +0.1273, -0.0366, +0.0466, +0.1477], -[ +0.1492, +0.1446, +0.4695, -0.4109, -0.0883, -0.1199, +0.5098, +0.4549, -0.2600, +0.1315, -0.2831, +0.4905, +0.0915, +0.1289, -1.1824, +0.3743, -0.3307, -0.2300, -0.6317, -0.2493, -1.2151, -0.1466, -0.4567, -1.0819, +0.2878, -1.0267, +0.2882, +0.5518, -0.0761, +0.3592, +0.1387, -0.6827], -[ +0.1444, -0.1397, +0.1136, +0.0452, -0.3338, +0.1050, +0.2811, -0.1238, -0.4973, -0.0804, +0.0148, +0.1621, -0.8240, +0.2157, +0.1887, +0.0192, -1.2242, +0.2255, -0.2271, -0.4326, -0.0362, +0.0116, -0.2229, +0.4142, +0.3929, -0.2313, +0.1735, +0.0598, +0.1726, +0.1078, +0.1444, +0.3110], -[ -0.7894, -0.0617, +0.3583, -0.7723, +0.1178, +0.0533, -0.2285, -1.2298, +0.1727, -0.3313, +0.1193, -0.6488, +0.2996, -0.5132, -0.3868, +0.0174, +0.1475, +0.3516, -0.5912, -0.8573, +0.0606, -0.0528, -0.1981, +0.0196, -0.1688, -0.3554, -0.0234, -0.7206, +0.2903, -0.9404, 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-0.3548, +0.3451, + +0.0281, -0.4796, -0.2282, -0.3789, -0.1253, -0.0824, -0.3919, +0.1890, + -0.1683, -2.1362, -0.9594, -0.6882, +0.6158, -0.2412, +0.2336, -0.0142, + -0.9257, +0.3819, -0.1836, -0.7676, +0.3713, -0.1364, +0.3317, +0.3696 + ] ]) -weights_dense2_b = np.array([ -0.0528, +0.0930, -0.3614, +0.2145, -0.3644, -0.0033, -0.0702, -0.0928, -0.1018, +0.0424, +0.0130, +0.2634, -0.1167, +0.2412, +0.0852, +0.0047, +0.1958, -0.1322, +0.0218, +0.2207, +0.1946, +0.0936, +0.2900, +0.2404, -0.1711, +0.1214, +0.2968, -0.2935, -0.0390, +0.1330, +0.0325, +0.2185]) - -weights_final_w = np.array([ -[ -0.2378], -[ +0.1955], -[ -0.2006], -[ -0.5372], -[ -0.3298], -[ +0.0891], -[ -0.3930], -[ +0.8978], -[ +0.3177], -[ +0.5357], -[ +0.2878], -[ +0.4998], -[ +0.2550], -[ -0.2619], -[ +1.1990], -[ +0.3115], -[ +0.3655], -[ +0.5774], -[ -0.4641], -[ +0.2613], -[ +0.1928], -[ +0.1458], -[ +0.4138], -[ -0.4969], -[ +0.4147], -[ +1.0689], -[ -0.1562], -[ -0.3669], -[ -0.3073], -[ +0.3354], -[ +0.9354], -[ +0.8831] +weights_dense2_b = np.array([ + -0.0528, +0.0930, -0.3614, +0.2145, -0.3644, -0.0033, -0.0702, -0.0928, + -0.1018, +0.0424, +0.0130, +0.2634, -0.1167, +0.2412, +0.0852, +0.0047, + +0.1958, -0.1322, +0.0218, +0.2207, +0.1946, +0.0936, +0.2900, +0.2404, + -0.1711, +0.1214, +0.2968, -0.2935, -0.0390, +0.1330, +0.0325, +0.2185 ]) -weights_final_b = np.array([ +0.2753]) +weights_final_w = np.array([[-0.2378], [+0.1955], [-0.2006], [-0.5372], + [-0.3298], [+0.0891], [-0.3930], [+0.8978], + [+0.3177], [+0.5357], [+0.2878], [+0.4998], + [+0.2550], [-0.2619], [+1.1990], [+0.3115], + [+0.3655], [+0.5774], [-0.4641], [+0.2613], + [+0.1928], [+0.1458], [+0.4138], [-0.4969], + [+0.4147], [+1.0689], [-0.1562], [-0.3669], + [-0.3073], [+0.3354], [+0.9354], [+0.8831]]) + +weights_final_b = np.array([+0.2753]) +# yapf: enable -if __name__=="__main__": - main() +if __name__ == "__main__": + main() diff --git a/examples/pybullet/gym/pybullet_envs/examples/enjoy_TF_Walker2DBulletEnv_v0_2017may.py b/examples/pybullet/gym/pybullet_envs/examples/enjoy_TF_Walker2DBulletEnv_v0_2017may.py index 952af87ce..b88e19f51 100644 --- a/examples/pybullet/gym/pybullet_envs/examples/enjoy_TF_Walker2DBulletEnv_v0_2017may.py +++ b/examples/pybullet/gym/pybullet_envs/examples/enjoy_TF_Walker2DBulletEnv_v0_2017may.py @@ -2,289 +2,2036 @@ import os, inspect currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe()))) parentdir = os.path.dirname(os.path.dirname(currentdir)) -os.sys.path.insert(0,parentdir) +os.sys.path.insert(0, parentdir) import gym import numpy as np import pybullet_envs import time + def relu(x): - return np.maximum(x, 0) + return np.maximum(x, 0) + class SmallReactivePolicy: - "Simple multi-layer perceptron policy, no internal state" - def __init__(self, observation_space, action_space): - assert weights_dense1_w.shape == (observation_space.shape[0], 128) - assert weights_dense2_w.shape == (128, 64) - assert weights_final_w.shape == (64, action_space.shape[0]) + "Simple multi-layer perceptron policy, no internal state" + + def __init__(self, observation_space, action_space): + assert weights_dense1_w.shape == (observation_space.shape[0], 128) + assert weights_dense2_w.shape == (128, 64) + assert weights_final_w.shape == (64, action_space.shape[0]) + + def act(self, ob): + x = ob + x = relu(np.dot(x, weights_dense1_w) + weights_dense1_b) + x = relu(np.dot(x, weights_dense2_w) + weights_dense2_b) + x = np.dot(x, weights_final_w) + weights_final_b + return x - def act(self, ob): - x = ob - x = relu(np.dot(x, weights_dense1_w) + weights_dense1_b) - x = relu(np.dot(x, weights_dense2_w) + weights_dense2_b) - x = np.dot(x, weights_final_w) + weights_final_b - return x def main(): - env = gym.make("Walker2DBulletEnv-v0") - env.render(mode="human") - pi = SmallReactivePolicy(env.observation_space, env.action_space) + env = gym.make("Walker2DBulletEnv-v0") + env.render(mode="human") + pi = SmallReactivePolicy(env.observation_space, env.action_space) - env.reset() + env.reset() - while 1: - frame = 0 - score = 0 - restart_delay = 0 - obs = env.reset() + while 1: + frame = 0 + score = 0 + restart_delay = 0 + obs = env.reset() - while 1: - time.sleep(1./60.) - a = pi.act(obs) - obs, r, done, _ = env.step(a) - score += r - frame += 1 + while 1: + time.sleep(1. / 60.) + a = pi.act(obs) + obs, r, done, _ = env.step(a) + score += r + frame += 1 - still_open = env.render("human") - if still_open==False: - return - if not done: continue - if restart_delay==0: - print("score=%0.2f in %i frames" % (score, frame)) - restart_delay = 60*2 # 2 sec at 60 fps - else: - restart_delay -= 1 - if restart_delay==0: break + still_open = env.render("human") + if still_open == False: + return + if not done: continue + if restart_delay == 0: + print("score=%0.2f in %i frames" % (score, frame)) + restart_delay = 60 * 2 # 2 sec at 60 fps + else: + restart_delay -= 1 + if restart_delay == 0: break -weights_dense1_w = np.array([ -[ -0.5923, +0.0708, -0.2868, -0.5138, -0.1376, +0.3921, +0.3474, +0.2376, +0.6664, +0.1282, +0.1778, +0.2879, -0.3030, +0.1678, +0.1248, +0.1665, -0.1129, -0.5741, +0.1318, +0.4056, +0.2123, +0.1856, +0.3895, +0.0076, +0.4030, +0.1592, +0.2418, +0.0302, -0.2642, -0.0772, -0.5000, -0.0241, +0.3221, +0.1268, -0.1013, +0.1987, +0.2850, -0.3257, -0.1115, -0.3003, +0.0450, -0.2232, -0.1471, +0.5303, +0.6448, +0.0623, +0.4801, +0.1554, -0.0681, -0.4085, +0.6602, +0.1064, +0.1389, +0.6386, +0.5123, -0.3210, -0.0565, +0.2629, +0.5662, -0.2477, -0.1244, +0.0125, +0.1585, +0.0328, +0.0098, +0.0898, +0.4162, -0.0758, -0.4449, +0.4196, +0.0923, +0.4323, -0.2452, -0.1646, +0.8115, +0.3922, +0.2736, -0.2587, +0.6535, +0.1253, +0.0194, +0.3765, +0.2678, +0.4038, -0.1404, -0.2698, +0.1081, +0.0031, +0.4622, -0.1806, -0.2798, -0.2216, +0.2180, -0.0131, -0.3375, +0.2901, +0.2382, -0.0402, +0.4257, -0.2084, -0.1102, +0.6300, +0.0378, +0.5702, +0.0085, -0.4068, +0.5516, +0.1101, +0.4330, +0.0053, +0.0656, +0.6463, +0.2717, -0.5689, -0.2151, +0.1876, +0.6149, +0.1463, +0.2377, +0.4734, -0.5056, +0.5846, +0.1847, +0.4174, -0.0182, -0.0434, +0.4932, +0.1675], -[ -0.0323, +0.1990, +0.0035, +0.1374, -0.0162, +0.1237, -0.2231, +0.3492, -0.0100, +0.1316, -0.4408, -0.3253, +0.0478, +0.1452, +0.1131, +0.0822, +0.3286, +0.0443, -0.1344, -0.0785, -0.4665, -0.1749, -0.2719, +0.1309, +0.2622, -0.1787, +0.0132, +0.0169, +0.1424, -0.1192, -0.0486, +0.0994, +0.2762, -0.2210, -0.3417, -0.0453, +0.2207, +0.1077, -0.2448, +0.1683, +0.0924, +0.1232, -0.1016, +0.2795, +0.3878, -0.3440, -0.0133, -0.1766, -0.2442, -0.3664, +0.1455, -0.1039, -0.0158, +0.1746, -0.3620, -0.1886, +0.4197, -0.1972, -0.0783, -0.2513, +0.1576, -0.0283, +0.4125, -0.0183, +0.2840, -0.0190, -0.2548, -0.0755, +0.2639, +0.1207, -0.0633, -0.2397, +0.2823, -0.1291, +0.1105, -0.3745, -0.0536, -0.1834, +0.3941, -0.0746, -0.0950, +0.1063, 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-0.5150, +0.1257], -[ -0.0267, -0.5339, -0.2769, -0.0796, +0.0055, -0.2304], -[ +0.2994, +0.3536, -0.3556, +0.3602, -0.1715, -0.3168], -[ +0.1544, -0.5746, -0.1126, -0.4654, +0.1188, -0.0478], -[ +0.4130, -0.7358, +0.0469, +0.0414, -0.0352, -0.5242], -[ -0.3941, +0.6807, +0.0670, +0.6347, +0.0394, +0.2293], -[ -0.0101, +0.1363, +0.6806, -0.1179, +0.1445, -0.0643], -[ -0.5794, +0.1603, -0.3438, -0.5360, -0.0372, +0.1549], -[ -0.0897, +1.1163, +0.4080, +0.1319, +0.1437, +0.0088], -[ +0.2344, +0.4274, -0.1515, +0.0379, -0.2166, -0.0852] +weights_dense2_b = np.array([ + +0.3157, -0.0138, +0.1347, +0.2792, -0.0769, +0.0847, -0.0819, +0.2974, + +0.1350, -0.0025, +0.0698, -0.2427, +0.2004, +0.2309, +0.1715, +0.0219, + +0.1258, +0.1784, +0.3261, +0.1711, -0.0779, +0.1550, +0.1433, +0.0774, + -0.1676, -0.0443, -0.1684, +0.1624, +0.0597, +0.0358, +0.1652, +0.1075, + +0.0770, +0.1979, +0.1490, -0.0719, +0.1036, +0.0766, +0.2915, -0.1048, + +0.1366, +0.0610, +0.2238, +0.1660, +0.0433, +0.3754, -0.0812, -0.0851, + +0.2820, +0.2110, +0.0232, -0.0167, -0.2433, -0.0042, +0.2591, -0.0215, + +0.0927, +0.1470, -0.0324, +0.2150, -0.1883, +0.0956, +0.1331, +0.2330 ]) -weights_final_b = np.array([ +0.0090, +0.2404, +0.1022, +0.1035, +0.0621, -0.0160]) +weights_final_w = np.array( + [[+0.0101, -0.1948, +0.2128, -0.4291, +0.4975, +0.0586], + [+0.1211, -0.3286, -0.5959, +0.2402, +0.2022, +0.0405], + [-0.0318, +0.3342, +0.1579, -0.4639, -0.4456, -0.2342], + [+0.0856, +0.0638, +0.3259, +0.2046, -0.4004, +0.4999], + [-0.6462, -0.0369, -1.1351, +0.9906, +0.0352, -0.6781], + [-0.3719, -0.1071, +0.3581, +0.5559, +0.1965, -0.3970], + [+0.0816, -0.1766, -0.6672, +0.0872, -0.5773, -0.3138], + [-0.0914, +0.1054, +0.0728, +0.5491, +0.4499, +0.1875], + [+0.2443, +0.0085, -0.2355, +0.3129, -0.1762, -0.0077], + [-0.1181, -0.6764, +0.4602, +0.1629, +0.0420, +0.2435], + [+0.0605, +0.0850, -0.6874, -0.0096, +0.4599, -0.2271], + [+0.5245, -0.2946, -0.1789, +0.5927, +0.0829, +0.5251], + [+0.4504, -0.4831, +0.0163, +0.0061, +0.5226, -0.4811], + [+0.5086, -0.2366, +0.0043, -0.1165, -0.2790, -0.5078], + [+0.1296, +0.0196, +0.1396, -0.1705, +0.2290, -0.2673], + [-0.0323, -0.1554, +0.4492, -0.6847, +0.0767, +0.0235], + [-0.0142, +0.3304, -0.2269, +0.0771, +0.2548, -0.0215], + [-0.3389, +0.0605, -0.1767, +0.1649, +0.3932, +0.4187], + [+0.0447, -0.2753, +0.7056, -0.3268, -0.4054, +0.3856], + [+0.3863, +0.0820, +0.1836, -0.1025, -0.0837, +0.3728], + [+0.1172, +0.2514, -0.2447, -0.0147, +0.3737, -0.1232], + [+0.4822, -0.1587, +0.0189, +0.1085, -0.2107, +0.1819], + [-0.3112, +0.2384, -0.0983, +0.3085, +0.3343, -0.1824], + [+0.1329, -0.7842, +0.5067, -0.1467, +0.6028, -0.1696], + [+0.0913, -0.2866, +0.1866, +0.0102, -0.2893, +0.1818], + [-0.0842, +0.0903, +0.0537, +0.2681, +0.6005, -0.3763], + [+0.4081, -0.5070, -0.2256, +0.4282, -0.2310, +0.0178], + [+0.1979, +0.1552, +0.7038, -0.0276, +0.1008, +0.4311], + [+0.0105, +0.0500, -0.5326, -0.1960, -0.0029, +0.7599], + [+0.0132, +0.1029, +0.0586, +0.2432, -0.0545, -0.3576], + [-0.1861, +0.2730, -0.1026, +0.2567, +0.1899, +0.0994], + [+0.2804, +0.4976, +0.0234, -0.0693, +0.1044, -0.1782], + [+0.1651, -0.0445, -0.1732, -0.1439, +0.1910, -0.2900], + [+0.0575, +0.0506, -0.5541, -0.0837, +0.1331, +0.0596], + [-0.5366, -0.2095, +0.2158, +0.1478, +0.1552, -0.2847], + [-0.2239, -0.5064, +0.2553, +0.6997, -0.2814, -0.5053], + [-0.5875, -0.2929, -0.3054, +0.4141, +0.0547, +0.2617], + [+0.4712, +0.1022, +0.2665, +0.1481, +0.2837, +0.3440], + [+0.3937, +0.3063, +0.4430, -0.5654, +0.0932, -0.1030], + [+0.0561, +0.7068, +0.1140, -0.0612, +0.1575, -0.4454], + [-0.4082, -0.3756, -0.0447, +0.5955, +0.2170, +0.2459], + [+0.3779, +0.1477, -0.2120, -0.3360, -0.3584, +0.1333], + [-0.1877, +0.1041, -0.2141, -0.0261, +0.0121, +0.5809], + [+0.5229, +0.3220, +0.4367, -0.1934, +0.0125, +0.3250], + [-0.0197, -0.0964, +0.6821, +0.0876, +0.1429, -0.2392], + [-0.3900, +0.3254, -0.1975, +0.2021, -0.4072, +0.2072], + [+0.2660, -0.2046, +0.2172, +0.2520, +0.1637, -0.0179], + [-0.0649, -0.0451, +0.0873, +0.0077, -0.3684, -0.0634], + [+0.1304, +0.2106, +0.0804, +0.0554, -0.2718, -0.4081], + [+0.3119, +0.1979, -0.2498, +0.0328, +0.2566, +0.2054], + [+0.0622, +0.2239, -0.7600, -0.2464, -0.3213, -0.4771], + [-0.3352, +0.4833, +0.0830, +0.3854, -0.0733, -0.0983], + [+0.2465, +0.0722, -0.0776, +0.2516, +0.5810, +0.5918], + [-0.2167, -0.2138, +0.2410, -0.3535, -0.4878, -0.4168], + [-0.0991, +0.4950, +0.0633, +0.2891, -0.5150, +0.1257], + [-0.0267, -0.5339, -0.2769, -0.0796, +0.0055, -0.2304], + [+0.2994, +0.3536, -0.3556, +0.3602, -0.1715, -0.3168], + [+0.1544, -0.5746, -0.1126, -0.4654, +0.1188, -0.0478], + [+0.4130, -0.7358, +0.0469, +0.0414, -0.0352, -0.5242], + [-0.3941, +0.6807, +0.0670, +0.6347, +0.0394, +0.2293], + [-0.0101, +0.1363, +0.6806, -0.1179, +0.1445, -0.0643], + [-0.5794, +0.1603, -0.3438, -0.5360, -0.0372, +0.1549], + [-0.0897, +1.1163, +0.4080, +0.1319, +0.1437, +0.0088], + [+0.2344, +0.4274, -0.1515, +0.0379, -0.2166, -0.0852]]) + +weights_final_b = np.array( + [+0.0090, +0.2404, +0.1022, +0.1035, +0.0621, -0.0160]) +# yapf: enable -if __name__=="__main__": - main() +if __name__ == "__main__": + main() diff --git a/examples/pybullet/gym/pybullet_envs/examples/kukaCamGymEnvTest.py b/examples/pybullet/gym/pybullet_envs/examples/kukaCamGymEnvTest.py index 00fe0d6b3..6cf9909cb 100644 --- a/examples/pybullet/gym/pybullet_envs/examples/kukaCamGymEnvTest.py +++ b/examples/pybullet/gym/pybullet_envs/examples/kukaCamGymEnvTest.py @@ -2,39 +2,40 @@ import os, inspect currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe()))) parentdir = os.path.dirname(os.path.dirname(currentdir)) -os.sys.path.insert(0,parentdir) +os.sys.path.insert(0, parentdir) from pybullet_envs.bullet.kukaCamGymEnv import KukaCamGymEnv import time + def main(): - environment = KukaCamGymEnv(renders=True,isDiscrete=False) - - - motorsIds=[] - #motorsIds.append(environment._p.addUserDebugParameter("posX",0.4,0.75,0.537)) - #motorsIds.append(environment._p.addUserDebugParameter("posY",-.22,.3,0.0)) - #motorsIds.append(environment._p.addUserDebugParameter("posZ",0.1,1,0.2)) - #motorsIds.append(environment._p.addUserDebugParameter("yaw",-3.14,3.14,0)) - #motorsIds.append(environment._p.addUserDebugParameter("fingerAngle",0,0.3,.3)) - - dv = 1 - motorsIds.append(environment._p.addUserDebugParameter("posX",-dv,dv,0)) - motorsIds.append(environment._p.addUserDebugParameter("posY",-dv,dv,0)) - motorsIds.append(environment._p.addUserDebugParameter("posZ",-dv,dv,0)) - motorsIds.append(environment._p.addUserDebugParameter("yaw",-dv,dv,0)) - motorsIds.append(environment._p.addUserDebugParameter("fingerAngle",0,0.3,.3)) - - done = False - while (not done): - - action=[] - for motorId in motorsIds: - action.append(environment._p.readUserDebugParameter(motorId)) - - state, reward, done, info = environment.step(action) - obs = environment.getExtendedObservation() - -if __name__=="__main__": - main() + environment = KukaCamGymEnv(renders=True, isDiscrete=False) + + motorsIds = [] + #motorsIds.append(environment._p.addUserDebugParameter("posX",0.4,0.75,0.537)) + #motorsIds.append(environment._p.addUserDebugParameter("posY",-.22,.3,0.0)) + #motorsIds.append(environment._p.addUserDebugParameter("posZ",0.1,1,0.2)) + #motorsIds.append(environment._p.addUserDebugParameter("yaw",-3.14,3.14,0)) + #motorsIds.append(environment._p.addUserDebugParameter("fingerAngle",0,0.3,.3)) + + dv = 1 + motorsIds.append(environment._p.addUserDebugParameter("posX", -dv, dv, 0)) + motorsIds.append(environment._p.addUserDebugParameter("posY", -dv, dv, 0)) + motorsIds.append(environment._p.addUserDebugParameter("posZ", -dv, dv, 0)) + motorsIds.append(environment._p.addUserDebugParameter("yaw", -dv, dv, 0)) + motorsIds.append(environment._p.addUserDebugParameter("fingerAngle", 0, 0.3, .3)) + + done = False + while (not done): + + action = [] + for motorId in motorsIds: + action.append(environment._p.readUserDebugParameter(motorId)) + + state, reward, done, info = environment.step(action) + obs = environment.getExtendedObservation() + + +if __name__ == "__main__": + main() diff --git a/examples/pybullet/gym/pybullet_envs/examples/kukaGymEnvTest.py b/examples/pybullet/gym/pybullet_envs/examples/kukaGymEnvTest.py index 3917f08cf..818bdd0ab 100644 --- a/examples/pybullet/gym/pybullet_envs/examples/kukaGymEnvTest.py +++ b/examples/pybullet/gym/pybullet_envs/examples/kukaGymEnvTest.py @@ -2,39 +2,40 @@ import os, inspect currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe()))) parentdir = os.path.dirname(os.path.dirname(currentdir)) -os.sys.path.insert(0,parentdir) +os.sys.path.insert(0, parentdir) from pybullet_envs.bullet.kukaGymEnv import KukaGymEnv import time + def main(): - environment = KukaGymEnv(renders=True,isDiscrete=False, maxSteps = 10000000) - - - motorsIds=[] - #motorsIds.append(environment._p.addUserDebugParameter("posX",0.4,0.75,0.537)) - #motorsIds.append(environment._p.addUserDebugParameter("posY",-.22,.3,0.0)) - #motorsIds.append(environment._p.addUserDebugParameter("posZ",0.1,1,0.2)) - #motorsIds.append(environment._p.addUserDebugParameter("yaw",-3.14,3.14,0)) - #motorsIds.append(environment._p.addUserDebugParameter("fingerAngle",0,0.3,.3)) - - dv = 0.01 - motorsIds.append(environment._p.addUserDebugParameter("posX",-dv,dv,0)) - motorsIds.append(environment._p.addUserDebugParameter("posY",-dv,dv,0)) - motorsIds.append(environment._p.addUserDebugParameter("posZ",-dv,dv,0)) - motorsIds.append(environment._p.addUserDebugParameter("yaw",-dv,dv,0)) - motorsIds.append(environment._p.addUserDebugParameter("fingerAngle",0,0.3,.3)) - - done = False - while (not done): - - action=[] - for motorId in motorsIds: - action.append(environment._p.readUserDebugParameter(motorId)) - - state, reward, done, info = environment.step2(action) - obs = environment.getExtendedObservation() - -if __name__=="__main__": - main() + environment = KukaGymEnv(renders=True, isDiscrete=False, maxSteps=10000000) + + motorsIds = [] + #motorsIds.append(environment._p.addUserDebugParameter("posX",0.4,0.75,0.537)) + #motorsIds.append(environment._p.addUserDebugParameter("posY",-.22,.3,0.0)) + #motorsIds.append(environment._p.addUserDebugParameter("posZ",0.1,1,0.2)) + #motorsIds.append(environment._p.addUserDebugParameter("yaw",-3.14,3.14,0)) + #motorsIds.append(environment._p.addUserDebugParameter("fingerAngle",0,0.3,.3)) + + dv = 0.01 + motorsIds.append(environment._p.addUserDebugParameter("posX", -dv, dv, 0)) + motorsIds.append(environment._p.addUserDebugParameter("posY", -dv, dv, 0)) + motorsIds.append(environment._p.addUserDebugParameter("posZ", -dv, dv, 0)) + motorsIds.append(environment._p.addUserDebugParameter("yaw", -dv, dv, 0)) + motorsIds.append(environment._p.addUserDebugParameter("fingerAngle", 0, 0.3, .3)) + + done = False + while (not done): + + action = [] + for motorId in motorsIds: + action.append(environment._p.readUserDebugParameter(motorId)) + + state, reward, done, info = environment.step2(action) + obs = environment.getExtendedObservation() + + +if __name__ == "__main__": + main() diff --git a/examples/pybullet/gym/pybullet_envs/examples/kukaGymEnvTest2.py b/examples/pybullet/gym/pybullet_envs/examples/kukaGymEnvTest2.py index 5c7f245c3..d94702cf0 100644 --- a/examples/pybullet/gym/pybullet_envs/examples/kukaGymEnvTest2.py +++ b/examples/pybullet/gym/pybullet_envs/examples/kukaGymEnvTest2.py @@ -2,37 +2,38 @@ import os, inspect currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe()))) parentdir = os.path.dirname(os.path.dirname(currentdir)) -os.sys.path.insert(0,parentdir) +os.sys.path.insert(0, parentdir) from pybullet_envs.bullet.kukaGymEnv import KukaGymEnv import time + def main(): - environment = KukaGymEnv(renders=True,isDiscrete=False, maxSteps = 10000000) - - - motorsIds=[] - #motorsIds.append(environment._p.addUserDebugParameter("posX",-1,1,0)) - #motorsIds.append(environment._p.addUserDebugParameter("posY",-.22,.3,0.0)) - #motorsIds.append(environment._p.addUserDebugParameter("posZ",0.1,1,0.2)) - #motorsIds.append(environment._p.addUserDebugParameter("yaw",-3.14,3.14,0)) - #motorsIds.append(environment._p.addUserDebugParameter("fingerAngle",0,0.3,.3)) - - dv = 1 - motorsIds.append(environment._p.addUserDebugParameter("posX",-dv,dv,0)) - motorsIds.append(environment._p.addUserDebugParameter("posY",-dv,dv,0)) - motorsIds.append(environment._p.addUserDebugParameter("yaw",-dv,dv,0)) - - done = False - while (not done): - - action=[] - for motorId in motorsIds: - action.append(environment._p.readUserDebugParameter(motorId)) - - state, reward, done, info = environment.step(action) - obs = environment.getExtendedObservation() - -if __name__=="__main__": - main() + environment = KukaGymEnv(renders=True, isDiscrete=False, maxSteps=10000000) + + motorsIds = [] + #motorsIds.append(environment._p.addUserDebugParameter("posX",-1,1,0)) + #motorsIds.append(environment._p.addUserDebugParameter("posY",-.22,.3,0.0)) + #motorsIds.append(environment._p.addUserDebugParameter("posZ",0.1,1,0.2)) + #motorsIds.append(environment._p.addUserDebugParameter("yaw",-3.14,3.14,0)) + #motorsIds.append(environment._p.addUserDebugParameter("fingerAngle",0,0.3,.3)) + + dv = 1 + motorsIds.append(environment._p.addUserDebugParameter("posX", -dv, dv, 0)) + motorsIds.append(environment._p.addUserDebugParameter("posY", -dv, dv, 0)) + motorsIds.append(environment._p.addUserDebugParameter("yaw", -dv, dv, 0)) + + done = False + while (not done): + + action = [] + for motorId in motorsIds: + action.append(environment._p.readUserDebugParameter(motorId)) + + state, reward, done, info = environment.step(action) + obs = environment.getExtendedObservation() + + +if __name__ == "__main__": + main() diff --git a/examples/pybullet/gym/pybullet_envs/examples/kuka_setup.py b/examples/pybullet/gym/pybullet_envs/examples/kuka_setup.py index 38d32b844..1fcda0832 100644 --- a/examples/pybullet/gym/pybullet_envs/examples/kuka_setup.py +++ b/examples/pybullet/gym/pybullet_envs/examples/kuka_setup.py @@ -5,88 +5,165 @@ import pybullet_data #cid = p.connect(p.UDP,"192.168.86.100") cid = p.connect(p.SHARED_MEMORY) -if (cid<0): - p.connect(p.GUI) +if (cid < 0): + p.connect(p.GUI) p.setAdditionalSearchPath(pybullet_data.getDataPath()) p.resetSimulation() -objects = [p.loadURDF("plane.urdf", 0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,1.000000)] -objects = [p.loadURDF("samurai.urdf", 0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,1.000000)] -objects = [p.loadURDF("pr2_gripper.urdf", 0.500000,0.300006,0.700000,-0.000000,-0.000000,-0.000031,1.000000)] +objects = [ + p.loadURDF("plane.urdf", 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 1.000000) +] +objects = [ + p.loadURDF("samurai.urdf", 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, + 1.000000) +] +objects = [ + p.loadURDF("pr2_gripper.urdf", 0.500000, 0.300006, 0.700000, -0.000000, -0.000000, -0.000031, + 1.000000) +] pr2_gripper = objects[0] -print ("pr2_gripper=") -print (pr2_gripper) - -jointPositions=[ 0.550569, 0.000000, 0.549657, 0.000000 ] -for jointIndex in range (p.getNumJoints(pr2_gripper)): - p.resetJointState(pr2_gripper,jointIndex,jointPositions[jointIndex]) - -pr2_cid = p.createConstraint(pr2_gripper,-1,-1,-1,p.JOINT_FIXED,[0,0,0],[0.2,0,0],[0.500000,0.300006,0.700000]) -print ("pr2_cid") -print (pr2_cid) - -objects = [p.loadURDF("kuka_iiwa/model_vr_limits.urdf", 1.400000,-0.200000,0.600000,0.000000,0.000000,0.000000,1.000000)] +print("pr2_gripper=") +print(pr2_gripper) + +jointPositions = [0.550569, 0.000000, 0.549657, 0.000000] +for jointIndex in range(p.getNumJoints(pr2_gripper)): + p.resetJointState(pr2_gripper, jointIndex, jointPositions[jointIndex]) + +pr2_cid = p.createConstraint(pr2_gripper, -1, -1, -1, p.JOINT_FIXED, [0, 0, 0], [0.2, 0, 0], + [0.500000, 0.300006, 0.700000]) +print("pr2_cid") +print(pr2_cid) + +objects = [ + p.loadURDF("kuka_iiwa/model_vr_limits.urdf", 1.400000, -0.200000, 0.600000, 0.000000, 0.000000, + 0.000000, 1.000000) +] kuka = objects[0] -jointPositions=[ -0.000000, -0.000000, 0.000000, 1.570793, 0.000000, -1.036725, 0.000001 ] -for jointIndex in range (p.getNumJoints(kuka)): - p.resetJointState(kuka,jointIndex,jointPositions[jointIndex]) - p.setJointMotorControl2(kuka,jointIndex,p.POSITION_CONTROL,jointPositions[jointIndex],0) - -objects = [p.loadURDF("lego/lego.urdf", 1.000000,-0.200000,0.700000,0.000000,0.000000,0.000000,1.000000)] -objects = [p.loadURDF("lego/lego.urdf", 1.000000,-0.200000,0.800000,0.000000,0.000000,0.000000,1.000000)] -objects = [p.loadURDF("lego/lego.urdf", 1.000000,-0.200000,0.900000,0.000000,0.000000,0.000000,1.000000)] +jointPositions = [-0.000000, -0.000000, 0.000000, 1.570793, 0.000000, -1.036725, 0.000001] +for jointIndex in range(p.getNumJoints(kuka)): + p.resetJointState(kuka, jointIndex, jointPositions[jointIndex]) + p.setJointMotorControl2(kuka, jointIndex, p.POSITION_CONTROL, jointPositions[jointIndex], 0) + +objects = [ + p.loadURDF("lego/lego.urdf", 1.000000, -0.200000, 0.700000, 0.000000, 0.000000, 0.000000, + 1.000000) +] +objects = [ + p.loadURDF("lego/lego.urdf", 1.000000, -0.200000, 0.800000, 0.000000, 0.000000, 0.000000, + 1.000000) +] +objects = [ + p.loadURDF("lego/lego.urdf", 1.000000, -0.200000, 0.900000, 0.000000, 0.000000, 0.000000, + 1.000000) +] objects = p.loadSDF("gripper/wsg50_one_motor_gripper_new_free_base.sdf") kuka_gripper = objects[0] -print ("kuka gripper=") +print("kuka gripper=") print(kuka_gripper) -p.resetBasePositionAndOrientation(kuka_gripper,[0.923103,-0.200000,1.250036],[-0.000000,0.964531,-0.000002,-0.263970]) -jointPositions=[ 0.000000, -0.011130, -0.206421, 0.205143, -0.009999, 0.000000, -0.010055, 0.000000 ] -for jointIndex in range (p.getNumJoints(kuka_gripper)): - p.resetJointState(kuka_gripper,jointIndex,jointPositions[jointIndex]) - p.setJointMotorControl2(kuka_gripper,jointIndex,p.POSITION_CONTROL,jointPositions[jointIndex],0) - - -kuka_cid = p.createConstraint(kuka, 6, kuka_gripper,0,p.JOINT_FIXED, [0,0,0], [0,0,0.05],[0,0,0]) - -objects = [p.loadURDF("jenga/jenga.urdf", 1.300000,-0.700000,0.750000,0.000000,0.707107,0.000000,0.707107)] -objects = [p.loadURDF("jenga/jenga.urdf", 1.200000,-0.700000,0.750000,0.000000,0.707107,0.000000,0.707107)] -objects = [p.loadURDF("jenga/jenga.urdf", 1.100000,-0.700000,0.750000,0.000000,0.707107,0.000000,0.707107)] -objects = [p.loadURDF("jenga/jenga.urdf", 1.000000,-0.700000,0.750000,0.000000,0.707107,0.000000,0.707107)] -objects = [p.loadURDF("jenga/jenga.urdf", 0.900000,-0.700000,0.750000,0.000000,0.707107,0.000000,0.707107)] -objects = [p.loadURDF("jenga/jenga.urdf", 0.800000,-0.700000,0.750000,0.000000,0.707107,0.000000,0.707107)] -objects = [p.loadURDF("table/table.urdf", 1.000000,-0.200000,0.000000,0.000000,0.000000,0.707107,0.707107)] -objects = [p.loadURDF("teddy_vhacd.urdf", 1.050000,-0.500000,0.700000,0.000000,0.000000,0.707107,0.707107)] -objects = [p.loadURDF("cube_small.urdf", 0.950000,-0.100000,0.700000,0.000000,0.000000,0.707107,0.707107)] -objects = [p.loadURDF("sphere_small.urdf", 0.850000,-0.400000,0.700000,0.000000,0.000000,0.707107,0.707107)] -objects = [p.loadURDF("duck_vhacd.urdf", 0.850000,-0.400000,0.900000,0.000000,0.000000,0.707107,0.707107)] +p.resetBasePositionAndOrientation(kuka_gripper, [0.923103, -0.200000, 1.250036], + [-0.000000, 0.964531, -0.000002, -0.263970]) +jointPositions = [ + 0.000000, -0.011130, -0.206421, 0.205143, -0.009999, 0.000000, -0.010055, 0.000000 +] +for jointIndex in range(p.getNumJoints(kuka_gripper)): + p.resetJointState(kuka_gripper, jointIndex, jointPositions[jointIndex]) + p.setJointMotorControl2(kuka_gripper, jointIndex, p.POSITION_CONTROL, jointPositions[jointIndex], + 0) + +kuka_cid = p.createConstraint(kuka, 6, kuka_gripper, 0, p.JOINT_FIXED, [0, 0, 0], [0, 0, 0.05], + [0, 0, 0]) + +objects = [ + p.loadURDF("jenga/jenga.urdf", 1.300000, -0.700000, 0.750000, 0.000000, 0.707107, 0.000000, + 0.707107) +] +objects = [ + p.loadURDF("jenga/jenga.urdf", 1.200000, -0.700000, 0.750000, 0.000000, 0.707107, 0.000000, + 0.707107) +] +objects = [ + p.loadURDF("jenga/jenga.urdf", 1.100000, -0.700000, 0.750000, 0.000000, 0.707107, 0.000000, + 0.707107) +] +objects = [ + p.loadURDF("jenga/jenga.urdf", 1.000000, -0.700000, 0.750000, 0.000000, 0.707107, 0.000000, + 0.707107) +] +objects = [ + p.loadURDF("jenga/jenga.urdf", 0.900000, -0.700000, 0.750000, 0.000000, 0.707107, 0.000000, + 0.707107) +] +objects = [ + p.loadURDF("jenga/jenga.urdf", 0.800000, -0.700000, 0.750000, 0.000000, 0.707107, 0.000000, + 0.707107) +] +objects = [ + p.loadURDF("table/table.urdf", 1.000000, -0.200000, 0.000000, 0.000000, 0.000000, 0.707107, + 0.707107) +] +objects = [ + p.loadURDF("teddy_vhacd.urdf", 1.050000, -0.500000, 0.700000, 0.000000, 0.000000, 0.707107, + 0.707107) +] +objects = [ + p.loadURDF("cube_small.urdf", 0.950000, -0.100000, 0.700000, 0.000000, 0.000000, 0.707107, + 0.707107) +] +objects = [ + p.loadURDF("sphere_small.urdf", 0.850000, -0.400000, 0.700000, 0.000000, 0.000000, 0.707107, + 0.707107) +] +objects = [ + p.loadURDF("duck_vhacd.urdf", 0.850000, -0.400000, 0.900000, 0.000000, 0.000000, 0.707107, + 0.707107) +] objects = p.loadSDF("kiva_shelf/model.sdf") ob = objects[0] -p.resetBasePositionAndOrientation(ob,[0.000000,1.000000,1.204500],[0.000000,0.000000,0.000000,1.000000]) -objects = [p.loadURDF("teddy_vhacd.urdf", -0.100000,0.600000,0.850000,0.000000,0.000000,0.000000,1.000000)] -objects = [p.loadURDF("sphere_small.urdf", -0.100000,0.955006,1.169706,0.633232,-0.000000,-0.000000,0.773962)] -objects = [p.loadURDF("cube_small.urdf", 0.300000,0.600000,0.850000,0.000000,0.000000,0.000000,1.000000)] -objects = [p.loadURDF("table_square/table_square.urdf", -1.000000,0.000000,0.000000,0.000000,0.000000,0.000000,1.000000)] +p.resetBasePositionAndOrientation(ob, [0.000000, 1.000000, 1.204500], + [0.000000, 0.000000, 0.000000, 1.000000]) +objects = [ + p.loadURDF("teddy_vhacd.urdf", -0.100000, 0.600000, 0.850000, 0.000000, 0.000000, 0.000000, + 1.000000) +] +objects = [ + p.loadURDF("sphere_small.urdf", -0.100000, 0.955006, 1.169706, 0.633232, -0.000000, -0.000000, + 0.773962) +] +objects = [ + p.loadURDF("cube_small.urdf", 0.300000, 0.600000, 0.850000, 0.000000, 0.000000, 0.000000, + 1.000000) +] +objects = [ + p.loadURDF("table_square/table_square.urdf", -1.000000, 0.000000, 0.000000, 0.000000, 0.000000, + 0.000000, 1.000000) +] ob = objects[0] -jointPositions=[ 0.000000 ] -for jointIndex in range (p.getNumJoints(ob)): - p.resetJointState(ob,jointIndex,jointPositions[jointIndex]) - -objects = [p.loadURDF("husky/husky.urdf", 2.000000,-5.000000,1.000000,0.000000,0.000000,0.000000,1.000000)] +jointPositions = [0.000000] +for jointIndex in range(p.getNumJoints(ob)): + p.resetJointState(ob, jointIndex, jointPositions[jointIndex]) + +objects = [ + p.loadURDF("husky/husky.urdf", 2.000000, -5.000000, 1.000000, 0.000000, 0.000000, 0.000000, + 1.000000) +] ob = objects[0] -jointPositions=[ 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000 ] -for jointIndex in range (p.getNumJoints(ob)): - p.resetJointState(ob,jointIndex,jointPositions[jointIndex]) +jointPositions = [ + 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, + 0.000000 +] +for jointIndex in range(p.getNumJoints(ob)): + p.resetJointState(ob, jointIndex, jointPositions[jointIndex]) -p.setGravity(0.000000,0.000000,0.000000) -p.setGravity(0,0,-10) +p.setGravity(0.000000, 0.000000, 0.000000) +p.setGravity(0, 0, -10) p.setRealTimeSimulation(1) ref_time = time.time() -running_time = 60 # seconds -while (time.time() < ref_time+running_time): - p.setGravity(0,0,-10) - #p.stepSimulation() +running_time = 60 # seconds +while (time.time() < ref_time + running_time): + p.setGravity(0, 0, -10) + #p.stepSimulation() p.disconnect() diff --git a/examples/pybullet/gym/pybullet_envs/examples/laikago.py b/examples/pybullet/gym/pybullet_envs/examples/laikago.py index b256f2a04..081573cc0 100644 --- a/examples/pybullet/gym/pybullet_envs/examples/laikago.py +++ b/examples/pybullet/gym/pybullet_envs/examples/laikago.py @@ -7,58 +7,61 @@ p.connect(p.GUI) p.setAdditionalSearchPath(pd.getDataPath())
plane = p.loadURDF("plane.urdf")
-p.setGravity(0,0,-9.8)
-p.setTimeStep(1./500)
+p.setGravity(0, 0, -9.8)
+p.setTimeStep(1. / 500)
#p.setDefaultContactERP(0)
#urdfFlags = p.URDF_USE_SELF_COLLISION+p.URDF_USE_SELF_COLLISION_EXCLUDE_ALL_PARENTS
urdfFlags = p.URDF_USE_SELF_COLLISION
-quadruped = p.loadURDF("laikago/laikago.urdf",[0,0,.5],[0,0.5,0.5,0], flags = urdfFlags,useFixedBase=False)
+quadruped = p.loadURDF("laikago/laikago.urdf", [0, 0, .5], [0, 0.5, 0.5, 0],
+ flags=urdfFlags,
+ useFixedBase=False)
#enable collision between lower legs
-for j in range (p.getNumJoints(quadruped)):
- print(p.getJointInfo(quadruped,j))
+for j in range(p.getNumJoints(quadruped)):
+ print(p.getJointInfo(quadruped, j))
#2,5,8 and 11 are the lower legs
-lower_legs = [2,5,8,11]
+lower_legs = [2, 5, 8, 11]
for l0 in lower_legs:
for l1 in lower_legs:
- if (l1>l0):
+ if (l1 > l0):
enableCollision = 1
- print("collision for pair",l0,l1, p.getJointInfo(quadruped,l0)[12],p.getJointInfo(quadruped,l1)[12], "enabled=",enableCollision)
- p.setCollisionFilterPair(quadruped, quadruped, 2,5,enableCollision)
-
-jointIds=[]
-paramIds=[]
-jointOffsets=[]
-jointDirections=[-1,1,1,1,1,1,-1,1,1,1,1,1]
-jointAngles=[0,0,0,0,0,0,0,0,0,0,0,0]
-
-for i in range (4):
+ print("collision for pair", l0, l1,
+ p.getJointInfo(quadruped, l0)[12],
+ p.getJointInfo(quadruped, l1)[12], "enabled=", enableCollision)
+ p.setCollisionFilterPair(quadruped, quadruped, 2, 5, enableCollision)
+
+jointIds = []
+paramIds = []
+jointOffsets = []
+jointDirections = [-1, 1, 1, 1, 1, 1, -1, 1, 1, 1, 1, 1]
+jointAngles = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
+
+for i in range(4):
jointOffsets.append(0)
jointOffsets.append(-0.7)
jointOffsets.append(0.7)
-maxForceId = p.addUserDebugParameter("maxForce",0,100,20)
+maxForceId = p.addUserDebugParameter("maxForce", 0, 100, 20)
-for j in range (p.getNumJoints(quadruped)):
- p.changeDynamics(quadruped,j,linearDamping=0, angularDamping=0)
- info = p.getJointInfo(quadruped,j)
- #print(info)
- jointName = info[1]
- jointType = info[2]
- if (jointType==p.JOINT_PRISMATIC or jointType==p.JOINT_REVOLUTE):
- jointIds.append(j)
+for j in range(p.getNumJoints(quadruped)):
+ p.changeDynamics(quadruped, j, linearDamping=0, angularDamping=0)
+ info = p.getJointInfo(quadruped, j)
+ #print(info)
+ jointName = info[1]
+ jointType = info[2]
+ if (jointType == p.JOINT_PRISMATIC or jointType == p.JOINT_REVOLUTE):
+ jointIds.append(j)
-
-p.getCameraImage(480,320)
+p.getCameraImage(480, 320)
p.setRealTimeSimulation(0)
-joints=[]
+joints = []
-with open(pd.getDataPath()+"/laikago/data1.txt","r") as filestream:
+with open(pd.getDataPath() + "/laikago/data1.txt", "r") as filestream:
for line in filestream:
- print("line=",line)
+ print("line=", line)
maxForce = p.readUserDebugParameter(maxForceId)
currentline = line.split(",")
#print (currentline)
@@ -66,31 +69,35 @@ with open(pd.getDataPath()+"/laikago/data1.txt","r") as filestream: frame = currentline[0]
t = currentline[1]
#print("frame[",frame,"]")
- joints=currentline[2:14]
+ joints = currentline[2:14]
#print("joints=",joints)
- for j in range (12):
+ for j in range(12):
targetPos = float(joints[j])
- p.setJointMotorControl2(quadruped,jointIds[j],p.POSITION_CONTROL,jointDirections[j]*targetPos+jointOffsets[j], force=maxForce)
+ p.setJointMotorControl2(quadruped,
+ jointIds[j],
+ p.POSITION_CONTROL,
+ jointDirections[j] * targetPos + jointOffsets[j],
+ force=maxForce)
p.stepSimulation()
for lower_leg in lower_legs:
#print("points for ", quadruped, " link: ", lower_leg)
- pts = p.getContactPoints(quadruped,-1, lower_leg)
+ pts = p.getContactPoints(quadruped, -1, lower_leg)
#print("num points=",len(pts))
#for pt in pts:
# print(pt[9])
- time.sleep(1./500.)
-
-
-for j in range (p.getNumJoints(quadruped)):
- p.changeDynamics(quadruped,j,linearDamping=0, angularDamping=0)
- info = p.getJointInfo(quadruped,j)
- js = p.getJointState(quadruped,j)
- #print(info)
- jointName = info[1]
- jointType = info[2]
- if (jointType==p.JOINT_PRISMATIC or jointType==p.JOINT_REVOLUTE):
- paramIds.append(p.addUserDebugParameter(jointName.decode("utf-8"),-4,4,(js[0]-jointOffsets[j])/jointDirections[j]))
-
+ time.sleep(1. / 500.)
+
+for j in range(p.getNumJoints(quadruped)):
+ p.changeDynamics(quadruped, j, linearDamping=0, angularDamping=0)
+ info = p.getJointInfo(quadruped, j)
+ js = p.getJointState(quadruped, j)
+ #print(info)
+ jointName = info[1]
+ jointType = info[2]
+ if (jointType == p.JOINT_PRISMATIC or jointType == p.JOINT_REVOLUTE):
+ paramIds.append(
+ p.addUserDebugParameter(jointName.decode("utf-8"), -4, 4,
+ (js[0] - jointOffsets[j]) / jointDirections[j]))
p.setRealTimeSimulation(1)
@@ -100,5 +107,8 @@ while (1): c = paramIds[i]
targetPos = p.readUserDebugParameter(c)
maxForce = p.readUserDebugParameter(maxForceId)
- p.setJointMotorControl2(quadruped,jointIds[i],p.POSITION_CONTROL,jointDirections[i]*targetPos+jointOffsets[i], force=maxForce)
-
+ p.setJointMotorControl2(quadruped,
+ jointIds[i],
+ p.POSITION_CONTROL,
+ jointDirections[i] * targetPos + jointOffsets[i],
+ force=maxForce)
diff --git a/examples/pybullet/gym/pybullet_envs/examples/minitaur_gym_env_example.py b/examples/pybullet/gym/pybullet_envs/examples/minitaur_gym_env_example.py index 8057b2dc5..06337da40 100644 --- a/examples/pybullet/gym/pybullet_envs/examples/minitaur_gym_env_example.py +++ b/examples/pybullet/gym/pybullet_envs/examples/minitaur_gym_env_example.py @@ -5,7 +5,7 @@ import os import inspect currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe()))) parentdir = os.path.dirname(os.path.dirname(currentdir)) -os.sys.path.insert(0,parentdir) +os.sys.path.insert(0, parentdir) import math import numpy as np @@ -13,19 +13,19 @@ from pybullet_envs.bullet import minitaur_gym_env import argparse from pybullet_envs.bullet import minitaur_env_randomizer + def ResetPoseExample(): """An example that the minitaur stands still using the reset pose.""" steps = 1000 randomizer = (minitaur_env_randomizer.MinitaurEnvRandomizer()) - environment = minitaur_gym_env.MinitaurBulletEnv( - render=True, - leg_model_enabled=False, - motor_velocity_limit=np.inf, - pd_control_enabled=True, - accurate_motor_model_enabled=True, - motor_overheat_protection=True, - env_randomizer = randomizer, - hard_reset=False) + environment = minitaur_gym_env.MinitaurBulletEnv(render=True, + leg_model_enabled=False, + motor_velocity_limit=np.inf, + pd_control_enabled=True, + accurate_motor_model_enabled=True, + motor_overheat_protection=True, + env_randomizer=randomizer, + hard_reset=False) action = [math.pi / 2] * 8 for _ in range(steps): _, _, done, _ = environment.step(action) @@ -33,6 +33,7 @@ def ResetPoseExample(): break environment.reset() + def MotorOverheatExample(): """An example of minitaur motor overheat protection is triggered. @@ -40,15 +41,14 @@ def MotorOverheatExample(): torques. The overheat protection will be triggered in ~1 sec. """ - environment = minitaur_gym_env.MinitaurBulletEnv( - render=True, - leg_model_enabled=False, - motor_velocity_limit=np.inf, - motor_overheat_protection=True, - accurate_motor_model_enabled=True, - motor_kp=1.20, - motor_kd=0.00, - on_rack=False) + environment = minitaur_gym_env.MinitaurBulletEnv(render=True, + leg_model_enabled=False, + motor_velocity_limit=np.inf, + motor_overheat_protection=True, + accurate_motor_model_enabled=True, + motor_kp=1.20, + motor_kd=0.00, + on_rack=False) action = [.0] * 8 for i in range(8): @@ -68,6 +68,7 @@ def MotorOverheatExample(): actions_and_observations.append(current_row) environment.reset() + def SineStandExample(): """An example of minitaur standing and squatting on the floor. @@ -75,15 +76,14 @@ def SineStandExample(): periodically in both simulation and experiment. We compare the measured motor trajectories, torques and gains. """ - environment = minitaur_gym_env.MinitaurBulletEnv( - render=True, - leg_model_enabled=False, - motor_velocity_limit=np.inf, - motor_overheat_protection=True, - accurate_motor_model_enabled=True, - motor_kp=1.20, - motor_kd=0.02, - on_rack=False) + environment = minitaur_gym_env.MinitaurBulletEnv(render=True, + leg_model_enabled=False, + motor_velocity_limit=np.inf, + motor_overheat_protection=True, + accurate_motor_model_enabled=True, + motor_kp=1.20, + motor_kd=0.02, + on_rack=False) steps = 1000 amplitude = 0.5 speed = 3 @@ -102,20 +102,19 @@ def SineStandExample(): observation, _, _, _ = environment.step(action) current_row.extend(observation.tolist()) actions_and_observations.append(current_row) - + environment.reset() def SinePolicyExample(): """An example of minitaur walking with a sine gait.""" randomizer = (minitaur_env_randomizer.MinitaurEnvRandomizer()) - environment = minitaur_gym_env.MinitaurBulletEnv( - render=True, - motor_velocity_limit=np.inf, - pd_control_enabled=True, - hard_reset=False, - env_randomizer = randomizer, - on_rack=False) + environment = minitaur_gym_env.MinitaurBulletEnv(render=True, + motor_velocity_limit=np.inf, + pd_control_enabled=True, + hard_reset=False, + env_randomizer=randomizer, + on_rack=False) sum_reward = 0 steps = 20000 amplitude_1_bound = 0.1 @@ -150,21 +149,23 @@ def SinePolicyExample(): def main(): - parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter) - parser.add_argument('--env', help='environment ID (0==sine, 1==stand, 2=reset, 3=overheat)',type=int, default=0) - args = parser.parse_args() - print("--env=" + str(args.env)) - - if (args.env == 0): - SinePolicyExample() - if (args.env == 1): - SineStandExample() - if (args.env == 2): - ResetPoseExample() - if (args.env == 3): - MotorOverheatExample() - -if __name__ == '__main__': - main() + parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter) + parser.add_argument('--env', + help='environment ID (0==sine, 1==stand, 2=reset, 3=overheat)', + type=int, + default=0) + args = parser.parse_args() + print("--env=" + str(args.env)) + + if (args.env == 0): + SinePolicyExample() + if (args.env == 1): + SineStandExample() + if (args.env == 2): + ResetPoseExample() + if (args.env == 3): + MotorOverheatExample() +if __name__ == '__main__': + main() diff --git a/examples/pybullet/gym/pybullet_envs/examples/racecarGymEnvTest.py b/examples/pybullet/gym/pybullet_envs/examples/racecarGymEnvTest.py index d640951b0..3e4488e30 100644 --- a/examples/pybullet/gym/pybullet_envs/examples/racecarGymEnvTest.py +++ b/examples/pybullet/gym/pybullet_envs/examples/racecarGymEnvTest.py @@ -2,43 +2,45 @@ import os, inspect currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe()))) parentdir = os.path.dirname(os.path.dirname(currentdir)) -os.sys.path.insert(0,parentdir) +os.sys.path.insert(0, parentdir) from pybullet_envs.bullet.racecarGymEnv import RacecarGymEnv isDiscrete = False + def main(): - - environment = RacecarGymEnv(renders=True, isDiscrete=isDiscrete) - environment.reset() - - targetVelocitySlider = environment._p.addUserDebugParameter("wheelVelocity",-1,1,0) - steeringSlider = environment._p.addUserDebugParameter("steering",-1,1,0) - - while (True): - targetVelocity = environment._p.readUserDebugParameter(targetVelocitySlider) - steeringAngle = environment._p.readUserDebugParameter(steeringSlider) - if (isDiscrete): - discreteAction = 0 - if (targetVelocity<-0.33): - discreteAction=0 - else: - if (targetVelocity>0.33): - discreteAction=6 - else: - discreteAction=3 - if (steeringAngle>-0.17): - if (steeringAngle>0.17): - discreteAction=discreteAction+2 - else: - discreteAction=discreteAction+1 - action=discreteAction - else: - action=[targetVelocity,steeringAngle] - state, reward, done, info = environment.step(action) - obs = environment.getExtendedObservation() - print("obs") - print(obs) - -if __name__=="__main__": - main() + + environment = RacecarGymEnv(renders=True, isDiscrete=isDiscrete) + environment.reset() + + targetVelocitySlider = environment._p.addUserDebugParameter("wheelVelocity", -1, 1, 0) + steeringSlider = environment._p.addUserDebugParameter("steering", -1, 1, 0) + + while (True): + targetVelocity = environment._p.readUserDebugParameter(targetVelocitySlider) + steeringAngle = environment._p.readUserDebugParameter(steeringSlider) + if (isDiscrete): + discreteAction = 0 + if (targetVelocity < -0.33): + discreteAction = 0 + else: + if (targetVelocity > 0.33): + discreteAction = 6 + else: + discreteAction = 3 + if (steeringAngle > -0.17): + if (steeringAngle > 0.17): + discreteAction = discreteAction + 2 + else: + discreteAction = discreteAction + 1 + action = discreteAction + else: + action = [targetVelocity, steeringAngle] + state, reward, done, info = environment.step(action) + obs = environment.getExtendedObservation() + print("obs") + print(obs) + + +if __name__ == "__main__": + main() diff --git a/examples/pybullet/gym/pybullet_envs/examples/racecarZEDGymEnvTest.py b/examples/pybullet/gym/pybullet_envs/examples/racecarZEDGymEnvTest.py index cee459e70..4d529c476 100644 --- a/examples/pybullet/gym/pybullet_envs/examples/racecarZEDGymEnvTest.py +++ b/examples/pybullet/gym/pybullet_envs/examples/racecarZEDGymEnvTest.py @@ -2,43 +2,45 @@ import os, inspect currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe()))) parentdir = os.path.dirname(os.path.dirname(currentdir)) -os.sys.path.insert(0,parentdir) +os.sys.path.insert(0, parentdir) isDiscrete = False from pybullet_envs.bullet.racecarZEDGymEnv import RacecarZEDGymEnv + def main(): - - environment = RacecarZEDGymEnv(renders=True, isDiscrete=isDiscrete) - - targetVelocitySlider = environment._p.addUserDebugParameter("wheelVelocity",-1,1,0) - steeringSlider = environment._p.addUserDebugParameter("steering",-1,1,0) - - while (True): - targetVelocity = environment._p.readUserDebugParameter(targetVelocitySlider) - steeringAngle = environment._p.readUserDebugParameter(steeringSlider) - if (isDiscrete): - discreteAction = 0 - if (targetVelocity<-0.33): - discreteAction=0 - else: - if (targetVelocity>0.33): - discreteAction=6 - else: - discreteAction=3 - if (steeringAngle>-0.17): - if (steeringAngle>0.17): - discreteAction=discreteAction+2 - else: - discreteAction=discreteAction+1 - action=discreteAction - else: - action=[targetVelocity,steeringAngle] - - state, reward, done, info = environment.step(action) - obs = environment.getExtendedObservation() - #print("obs") - #print(obs) - -if __name__=="__main__": - main() + + environment = RacecarZEDGymEnv(renders=True, isDiscrete=isDiscrete) + + targetVelocitySlider = environment._p.addUserDebugParameter("wheelVelocity", -1, 1, 0) + steeringSlider = environment._p.addUserDebugParameter("steering", -1, 1, 0) + + while (True): + targetVelocity = environment._p.readUserDebugParameter(targetVelocitySlider) + steeringAngle = environment._p.readUserDebugParameter(steeringSlider) + if (isDiscrete): + discreteAction = 0 + if (targetVelocity < -0.33): + discreteAction = 0 + else: + if (targetVelocity > 0.33): + discreteAction = 6 + else: + discreteAction = 3 + if (steeringAngle > -0.17): + if (steeringAngle > 0.17): + discreteAction = discreteAction + 2 + else: + discreteAction = discreteAction + 1 + action = discreteAction + else: + action = [targetVelocity, steeringAngle] + + state, reward, done, info = environment.step(action) + obs = environment.getExtendedObservation() + #print("obs") + #print(obs) + + +if __name__ == "__main__": + main() diff --git a/examples/pybullet/gym/pybullet_envs/examples/runServer.py b/examples/pybullet/gym/pybullet_envs/examples/runServer.py index bcd95f812..054fe66a4 100644 --- a/examples/pybullet/gym/pybullet_envs/examples/runServer.py +++ b/examples/pybullet/gym/pybullet_envs/examples/runServer.py @@ -3,7 +3,7 @@ import os import inspect currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe()))) parentdir = os.path.dirname(os.path.dirname(currentdir)) -os.sys.path.insert(0,parentdir) +os.sys.path.insert(0, parentdir) import pybullet_data import pybullet as p @@ -12,8 +12,7 @@ import time p.connect(p.GUI_SERVER) p.setAdditionalSearchPath(pybullet_data.getDataPath()) -while(1): - #this is a no-op command, to allow GUI updates on Mac OSX (main thread) - p.setPhysicsEngineParameter() - time.sleep(0.01) - +while (1): + #this is a no-op command, to allow GUI updates on Mac OSX (main thread) + p.setPhysicsEngineParameter() + time.sleep(0.01) diff --git a/examples/pybullet/gym/pybullet_envs/examples/testBike.py b/examples/pybullet/gym/pybullet_envs/examples/testBike.py index e36922fdc..62475c60d 100644 --- a/examples/pybullet/gym/pybullet_envs/examples/testBike.py +++ b/examples/pybullet/gym/pybullet_envs/examples/testBike.py @@ -2,7 +2,7 @@ import os import inspect currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe()))) parentdir = os.path.dirname(os.path.dirname(currentdir)) -os.sys.path.insert(0,parentdir) +os.sys.path.insert(0, parentdir) import pybullet as p import math @@ -12,31 +12,31 @@ import pybullet_data p.connect(p.GUI) #p.loadURDF("wheel.urdf",[0,0,3]) p.setAdditionalSearchPath(pybullet_data.getDataPath()) -plane=p.loadURDF("plane100.urdf",[0,0,0]) -timestep = 1./240. - -bike=-1 -for i in range (1): - - bike=p.loadURDF("bicycle/bike.urdf",[0,0+3*i,1.5], [0,0,0,1], useFixedBase=False) - p.setJointMotorControl2(bike,0,p.VELOCITY_CONTROL,targetVelocity=0,force=0.05) - #p.setJointMotorControl2(bike,1,p.VELOCITY_CONTROL,targetVelocity=5, force=1000) - p.setJointMotorControl2(bike,1,p.VELOCITY_CONTROL,targetVelocity=5, force=0) - p.setJointMotorControl2(bike,2,p.VELOCITY_CONTROL,targetVelocity=15, force=20) - - p.changeDynamics(plane,-1, mass=0,lateralFriction=1, linearDamping=0, angularDamping=0) - p.changeDynamics(bike,1,lateralFriction=1,linearDamping=0, angularDamping=0) - p.changeDynamics(bike,2,lateralFriction=1,linearDamping=0, angularDamping=0) - #p.resetJointState(bike,1,0,100) - #p.resetJointState(bike,2,0,100) - #p.resetBaseVelocity(bike,[0,0,0],[0,0,0]) +plane = p.loadURDF("plane100.urdf", [0, 0, 0]) +timestep = 1. / 240. + +bike = -1 +for i in range(1): + + bike = p.loadURDF("bicycle/bike.urdf", [0, 0 + 3 * i, 1.5], [0, 0, 0, 1], useFixedBase=False) + p.setJointMotorControl2(bike, 0, p.VELOCITY_CONTROL, targetVelocity=0, force=0.05) + #p.setJointMotorControl2(bike,1,p.VELOCITY_CONTROL,targetVelocity=5, force=1000) + p.setJointMotorControl2(bike, 1, p.VELOCITY_CONTROL, targetVelocity=5, force=0) + p.setJointMotorControl2(bike, 2, p.VELOCITY_CONTROL, targetVelocity=15, force=20) + + p.changeDynamics(plane, -1, mass=0, lateralFriction=1, linearDamping=0, angularDamping=0) + p.changeDynamics(bike, 1, lateralFriction=1, linearDamping=0, angularDamping=0) + p.changeDynamics(bike, 2, lateralFriction=1, linearDamping=0, angularDamping=0) + #p.resetJointState(bike,1,0,100) + #p.resetJointState(bike,2,0,100) + #p.resetBaseVelocity(bike,[0,0,0],[0,0,0]) #p.setPhysicsEngineParameter(numSubSteps=0) #bike=p.loadURDF("frame.urdf",useFixedBase=True) #bike = p.loadURDF("handlebar.urdf", useFixedBase=True) #p.loadURDF("handlebar.urdf",[0,2,1]) #coord = p.loadURDF("handlebar.urdf", [0.7700000000000005, 0, 0.22000000000000006],useFixedBase=True)# p.loadURDF("coordinateframe.urdf",[-2,0,1],useFixedBase=True) #coord = p.loadURDF("coordinateframe.urdf",[-2,0,1],useFixedBase=True) -p.setGravity(0,0,-10) +p.setGravity(0, 0, -10) p.setRealTimeSimulation(0) #coordPos = [0,0,0] #coordOrnEuler = [0,0,0] @@ -44,100 +44,96 @@ p.setRealTimeSimulation(0) #coordPos= [0.7000000000000004, 0, 0.22000000000000006] #coordOrnEuler= [0, -0.2617993877991496, 0] -coordPos= [0.07, 0, -0.6900000000000004] -coordOrnEuler= [0, 0, 0] +coordPos = [0.07, 0, -0.6900000000000004] +coordOrnEuler = [0, 0, 0] -coordPos2= [0, 0, 0 ] -coordOrnEuler2= [0, 0, 0] +coordPos2 = [0, 0, 0] +coordOrnEuler2 = [0, 0, 0] -invPos,invOrn=p.invertTransform(coordPos,p.getQuaternionFromEuler(coordOrnEuler)) -mPos,mOrn = p.multiplyTransforms(invPos,invOrn, coordPos2,p.getQuaternionFromEuler(coordOrnEuler2)) +invPos, invOrn = p.invertTransform(coordPos, p.getQuaternionFromEuler(coordOrnEuler)) +mPos, mOrn = p.multiplyTransforms(invPos, invOrn, coordPos2, + p.getQuaternionFromEuler(coordOrnEuler2)) eul = p.getEulerFromQuaternion(mOrn) -print("rpy=\"",eul[0],eul[1],eul[2],"\" xyz=\"", mPos[0],mPos[1], mPos[2]) +print("rpy=\"", eul[0], eul[1], eul[2], "\" xyz=\"", mPos[0], mPos[1], mPos[2]) - -shift=0 +shift = 0 gui = 1 - -frame=0 -states=[] +frame = 0 +states = [] states.append(p.saveState()) #observations=[] #observations.append(obs) - + running = True reverting = False -p.getCameraImage(320,200)#,renderer=p.ER_BULLET_HARDWARE_OPENGL ) - -while (1): - - updateCam = 0 - keys = p.getKeyboardEvents() - for k,v in keys.items(): - if (reverting or (k == p.B3G_LEFT_ARROW and (v&p.KEY_WAS_TRIGGERED))): - reverting=True - stateIndex = len(states)-1 - #print("prestateIndex=",stateIndex) - time.sleep(timestep) - updateCam=1 - if (stateIndex>0): - stateIndex-=1 - states=states[:stateIndex+1] - #observations=observations[:stateIndex+1] - - - #print("states=",states) - #print("stateIndex =",stateIndex ) - p.restoreState(states[stateIndex]) - #obs=observations[stateIndex] - - - #obs, r, done, _ = env.step(a) - if (k == p.B3G_LEFT_ARROW and (v&p.KEY_WAS_RELEASED)): - reverting = False - - if (k == ord('1') and (v&p.KEY_WAS_TRIGGERED)): - gui = not gui - - - - if (k == p.B3G_RIGHT_ARROW and (v&p.KEY_WAS_RELEASED)): - running=False - - if (running or (k == p.B3G_RIGHT_ARROW and (v&p.KEY_WAS_TRIGGERED))): - running = True - - if (running): - - p.stepSimulation() - - updateCam=1 - time.sleep(timestep) - pts = p.getContactPoints() - #print("numPoints=",len(pts)) - #for point in pts: - # print("Point:bodyA=", point[1],"bodyB=",point[2],"linkA=",point[3],"linkB=",point[4],"dist=",point[8],"force=",point[9]) - - states.append(p.saveState()) - #observations.append(obs) - stateIndex = len(states) - #print("stateIndex =",stateIndex ) - frame += 1 - if (updateCam): - distance=5 - yaw = 0 - humanPos, humanOrn = p.getBasePositionAndOrientation(bike) - humanBaseVel = p.getBaseVelocity(bike) - #print("frame",frame, "humanPos=",humanPos, "humanVel=",humanBaseVel) - if (gui): - - camInfo = p.getDebugVisualizerCamera() - curTargetPos = camInfo[11] - distance=camInfo[10] - yaw = camInfo[8] - pitch=camInfo[9] - targetPos = [0.95*curTargetPos[0]+0.05*humanPos[0],0.95*curTargetPos[1]+0.05*humanPos[1],curTargetPos[2]] - p.resetDebugVisualizerCamera(distance,yaw,pitch,targetPos); - - +p.getCameraImage(320, 200) #,renderer=p.ER_BULLET_HARDWARE_OPENGL ) + +while (1): + + updateCam = 0 + keys = p.getKeyboardEvents() + for k, v in keys.items(): + if (reverting or (k == p.B3G_LEFT_ARROW and (v & p.KEY_WAS_TRIGGERED))): + reverting = True + stateIndex = len(states) - 1 + #print("prestateIndex=",stateIndex) + time.sleep(timestep) + updateCam = 1 + if (stateIndex > 0): + stateIndex -= 1 + states = states[:stateIndex + 1] + #observations=observations[:stateIndex+1] + + #print("states=",states) + #print("stateIndex =",stateIndex ) + p.restoreState(states[stateIndex]) + #obs=observations[stateIndex] + + #obs, r, done, _ = env.step(a) + if (k == p.B3G_LEFT_ARROW and (v & p.KEY_WAS_RELEASED)): + reverting = False + + if (k == ord('1') and (v & p.KEY_WAS_TRIGGERED)): + gui = not gui + + if (k == p.B3G_RIGHT_ARROW and (v & p.KEY_WAS_RELEASED)): + running = False + + if (running or (k == p.B3G_RIGHT_ARROW and (v & p.KEY_WAS_TRIGGERED))): + running = True + + if (running): + + p.stepSimulation() + + updateCam = 1 + time.sleep(timestep) + pts = p.getContactPoints() + #print("numPoints=",len(pts)) + #for point in pts: + # print("Point:bodyA=", point[1],"bodyB=",point[2],"linkA=",point[3],"linkB=",point[4],"dist=",point[8],"force=",point[9]) + + states.append(p.saveState()) + #observations.append(obs) + stateIndex = len(states) + #print("stateIndex =",stateIndex ) + frame += 1 + if (updateCam): + distance = 5 + yaw = 0 + humanPos, humanOrn = p.getBasePositionAndOrientation(bike) + humanBaseVel = p.getBaseVelocity(bike) + #print("frame",frame, "humanPos=",humanPos, "humanVel=",humanBaseVel) + if (gui): + + camInfo = p.getDebugVisualizerCamera() + curTargetPos = camInfo[11] + distance = camInfo[10] + yaw = camInfo[8] + pitch = camInfo[9] + targetPos = [ + 0.95 * curTargetPos[0] + 0.05 * humanPos[0], 0.95 * curTargetPos[1] + 0.05 * humanPos[1], + curTargetPos[2] + ] + p.resetDebugVisualizerCamera(distance, yaw, pitch, targetPos) diff --git a/examples/pybullet/gym/pybullet_envs/examples/testEnv.py b/examples/pybullet/gym/pybullet_envs/examples/testEnv.py index 61f9bd0f7..5d30d27af 100644 --- a/examples/pybullet/gym/pybullet_envs/examples/testEnv.py +++ b/examples/pybullet/gym/pybullet_envs/examples/testEnv.py @@ -2,7 +2,7 @@ import os import inspect currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe()))) parentdir = os.path.dirname(os.path.dirname(currentdir)) -os.sys.path.insert(0,parentdir) +os.sys.path.insert(0, parentdir) import pybullet_envs import gym import argparse @@ -10,48 +10,52 @@ import pybullet as p def test(args): - count = 0 - env = gym.make(args.env) - env.env.configure(args) - print("args.render=",args.render) - if (args.render==1): - env.render(mode="human") - env.reset() - if (args.resetbenchmark): - while (1): - env.reset() - print("p.getNumBodies()=",p.getNumBodies()) - print("count=",count) - count+=1 - print("action space:") - sample = env.action_space.sample() - action = sample*0.0 - print("action=") - print(action) - for i in range(args.steps): - obs,rewards,done,_ =env.step(action) - if (args.rgb): - print(env.render(mode="rgb_array")) - print("obs=") - print(obs) - print("rewards") - print (rewards) - print ("done") - print(done) + count = 0 + env = gym.make(args.env) + env.env.configure(args) + print("args.render=", args.render) + if (args.render == 1): + env.render(mode="human") + env.reset() + if (args.resetbenchmark): + while (1): + env.reset() + print("p.getNumBodies()=", p.getNumBodies()) + print("count=", count) + count += 1 + print("action space:") + sample = env.action_space.sample() + action = sample * 0.0 + print("action=") + print(action) + for i in range(args.steps): + obs, rewards, done, _ = env.step(action) + if (args.rgb): + print(env.render(mode="rgb_array")) + print("obs=") + print(obs) + print("rewards") + print(rewards) + print("done") + print(done) def main(): - import argparse - parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter) - parser.add_argument('--env', help='environment ID', default='AntBulletEnv-v0') - parser.add_argument('--seed', help='RNG seed', type=int, default=0) - parser.add_argument('--render', help='OpenGL Visualizer', type=int, default=0) - parser.add_argument('--rgb',help='rgb_array gym rendering',type=int, default=0) - parser.add_argument('--resetbenchmark',help='Repeat reset to show reset performance',type=int, default=0) - parser.add_argument('--steps', help='Number of steps', type=int, default=1) - - args = parser.parse_args() - test(args) + import argparse + parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter) + parser.add_argument('--env', help='environment ID', default='AntBulletEnv-v0') + parser.add_argument('--seed', help='RNG seed', type=int, default=0) + parser.add_argument('--render', help='OpenGL Visualizer', type=int, default=0) + parser.add_argument('--rgb', help='rgb_array gym rendering', type=int, default=0) + parser.add_argument('--resetbenchmark', + help='Repeat reset to show reset performance', + type=int, + default=0) + parser.add_argument('--steps', help='Number of steps', type=int, default=1) + + args = parser.parse_args() + test(args) + if __name__ == '__main__': - main() + main() diff --git a/examples/pybullet/gym/pybullet_envs/examples/testMJCF.py b/examples/pybullet/gym/pybullet_envs/examples/testMJCF.py index ebdef3b38..76a889c6a 100644 --- a/examples/pybullet/gym/pybullet_envs/examples/testMJCF.py +++ b/examples/pybullet/gym/pybullet_envs/examples/testMJCF.py @@ -2,27 +2,29 @@ import os import inspect currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe()))) parentdir = os.path.dirname(os.path.dirname(currentdir)) -os.sys.path.insert(0,parentdir) +os.sys.path.insert(0, parentdir) import pybullet as p import pybullet_data import time -def test(args): - p.connect(p.GUI) - p.setAdditionalSearchPath(pybullet_data.getDataPath()) - fileName = os.path.join("mjcf", args.mjcf) - print("fileName") - print(fileName) - p.loadMJCF(fileName) - while (1): - p.stepSimulation() - p.getCameraImage(320,240) - time.sleep(0.01) - + +def test(args): + p.connect(p.GUI) + p.setAdditionalSearchPath(pybullet_data.getDataPath()) + fileName = os.path.join("mjcf", args.mjcf) + print("fileName") + print(fileName) + p.loadMJCF(fileName) + while (1): + p.stepSimulation() + p.getCameraImage(320, 240) + time.sleep(0.01) + + if __name__ == '__main__': - import argparse - parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter) - parser.add_argument('--mjcf', help='MJCF filename', default="humanoid.xml") - args = parser.parse_args() - test(args)
\ No newline at end of file + import argparse + parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter) + parser.add_argument('--mjcf', help='MJCF filename', default="humanoid.xml") + args = parser.parse_args() + test(args) diff --git a/examples/pybullet/gym/pybullet_envs/gym_locomotion_envs.py b/examples/pybullet/gym/pybullet_envs/gym_locomotion_envs.py index 1ece4b4e5..d601c130b 100644 --- a/examples/pybullet/gym/pybullet_envs/gym_locomotion_envs.py +++ b/examples/pybullet/gym/pybullet_envs/gym_locomotion_envs.py @@ -6,175 +6,192 @@ from robot_locomotors import Hopper, Walker2D, HalfCheetah, Ant, Humanoid, Human class WalkerBaseBulletEnv(MJCFBaseBulletEnv): - def __init__(self, robot, render=False): - # print("WalkerBase::__init__ start") - MJCFBaseBulletEnv.__init__(self, robot, render) - - self.camera_x = 0 - self.walk_target_x = 1e3 # kilometer away - self.walk_target_y = 0 - self.stateId=-1 - - - def create_single_player_scene(self, bullet_client): - self.stadium_scene = SinglePlayerStadiumScene(bullet_client, gravity=9.8, timestep=0.0165/4, frame_skip=4) - return self.stadium_scene - - def reset(self): - if (self.stateId>=0): - #print("restoreState self.stateId:",self.stateId) - self._p.restoreState(self.stateId) - - r = MJCFBaseBulletEnv.reset(self) - self._p.configureDebugVisualizer(pybullet.COV_ENABLE_RENDERING,0) - - self.parts, self.jdict, self.ordered_joints, self.robot_body = self.robot.addToScene(self._p, - self.stadium_scene.ground_plane_mjcf) - self.ground_ids = set([(self.parts[f].bodies[self.parts[f].bodyIndex], self.parts[f].bodyPartIndex) for f in - self.foot_ground_object_names]) - self._p.configureDebugVisualizer(pybullet.COV_ENABLE_RENDERING,1) - if (self.stateId<0): - self.stateId=self._p.saveState() - #print("saving state self.stateId:",self.stateId) - - - return r - - def _isDone(self): - return self._alive < 0 - - def move_robot(self, init_x, init_y, init_z): - "Used by multiplayer stadium to move sideways, to another running lane." - self.cpp_robot.query_position() - pose = self.cpp_robot.root_part.pose() - pose.move_xyz(init_x, init_y, init_z) # Works because robot loads around (0,0,0), and some robots have z != 0 that is left intact - self.cpp_robot.set_pose(pose) - - electricity_cost = -2.0 # cost for using motors -- this parameter should be carefully tuned against reward for making progress, other values less improtant - stall_torque_cost = -0.1 # cost for running electric current through a motor even at zero rotational speed, small - foot_collision_cost = -1.0 # touches another leg, or other objects, that cost makes robot avoid smashing feet into itself - foot_ground_object_names = set(["floor"]) # to distinguish ground and other objects - joints_at_limit_cost = -0.1 # discourage stuck joints - - def step(self, a): - if not self.scene.multiplayer: # if multiplayer, action first applied to all robots, then global step() called, then _step() for all robots with the same actions - self.robot.apply_action(a) - self.scene.global_step() - - state = self.robot.calc_state() # also calculates self.joints_at_limit - - self._alive = float(self.robot.alive_bonus(state[0]+self.robot.initial_z, self.robot.body_rpy[1])) # state[0] is body height above ground, body_rpy[1] is pitch - done = self._isDone() - if not np.isfinite(state).all(): - print("~INF~", state) - done = True - - potential_old = self.potential - self.potential = self.robot.calc_potential() - progress = float(self.potential - potential_old) - - feet_collision_cost = 0.0 - for i,f in enumerate(self.robot.feet): # TODO: Maybe calculating feet contacts could be done within the robot code - contact_ids = set((x[2], x[4]) for x in f.contact_list()) - #print("CONTACT OF '%d' WITH %d" % (contact_ids, ",".join(contact_names)) ) - if (self.ground_ids & contact_ids): - #see Issue 63: https://github.com/openai/roboschool/issues/63 - #feet_collision_cost += self.foot_collision_cost - self.robot.feet_contact[i] = 1.0 - else: - self.robot.feet_contact[i] = 0.0 - - - electricity_cost = self.electricity_cost * float(np.abs(a*self.robot.joint_speeds).mean()) # let's assume we have DC motor with controller, and reverse current braking - electricity_cost += self.stall_torque_cost * float(np.square(a).mean()) - - joints_at_limit_cost = float(self.joints_at_limit_cost * self.robot.joints_at_limit) - debugmode=0 - if(debugmode): - print("alive=") - print(self._alive) - print("progress") - print(progress) - print("electricity_cost") - print(electricity_cost) - print("joints_at_limit_cost") - print(joints_at_limit_cost) - print("feet_collision_cost") - print(feet_collision_cost) - - self.rewards = [ - self._alive, - progress, - electricity_cost, - joints_at_limit_cost, - feet_collision_cost - ] - if (debugmode): - print("rewards=") - print(self.rewards) - print("sum rewards") - print(sum(self.rewards)) - self.HUD(state, a, done) - self.reward += sum(self.rewards) - - return state, sum(self.rewards), bool(done), {} - - def camera_adjust(self): - x, y, z = self.body_xyz - self.camera_x = 0.98*self.camera_x + (1-0.98)*x - self.camera.move_and_look_at(self.camera_x, y-2.0, 1.4, x, y, 1.0) + + def __init__(self, robot, render=False): + # print("WalkerBase::__init__ start") + MJCFBaseBulletEnv.__init__(self, robot, render) + + self.camera_x = 0 + self.walk_target_x = 1e3 # kilometer away + self.walk_target_y = 0 + self.stateId = -1 + + def create_single_player_scene(self, bullet_client): + self.stadium_scene = SinglePlayerStadiumScene(bullet_client, + gravity=9.8, + timestep=0.0165 / 4, + frame_skip=4) + return self.stadium_scene + + def reset(self): + if (self.stateId >= 0): + #print("restoreState self.stateId:",self.stateId) + self._p.restoreState(self.stateId) + + r = MJCFBaseBulletEnv.reset(self) + self._p.configureDebugVisualizer(pybullet.COV_ENABLE_RENDERING, 0) + + self.parts, self.jdict, self.ordered_joints, self.robot_body = self.robot.addToScene( + self._p, self.stadium_scene.ground_plane_mjcf) + self.ground_ids = set([(self.parts[f].bodies[self.parts[f].bodyIndex], + self.parts[f].bodyPartIndex) for f in self.foot_ground_object_names]) + self._p.configureDebugVisualizer(pybullet.COV_ENABLE_RENDERING, 1) + if (self.stateId < 0): + self.stateId = self._p.saveState() + #print("saving state self.stateId:",self.stateId) + + return r + + def _isDone(self): + return self._alive < 0 + + def move_robot(self, init_x, init_y, init_z): + "Used by multiplayer stadium to move sideways, to another running lane." + self.cpp_robot.query_position() + pose = self.cpp_robot.root_part.pose() + pose.move_xyz( + init_x, init_y, init_z + ) # Works because robot loads around (0,0,0), and some robots have z != 0 that is left intact + self.cpp_robot.set_pose(pose) + + electricity_cost = -2.0 # cost for using motors -- this parameter should be carefully tuned against reward for making progress, other values less improtant + stall_torque_cost = -0.1 # cost for running electric current through a motor even at zero rotational speed, small + foot_collision_cost = -1.0 # touches another leg, or other objects, that cost makes robot avoid smashing feet into itself + foot_ground_object_names = set(["floor"]) # to distinguish ground and other objects + joints_at_limit_cost = -0.1 # discourage stuck joints + + def step(self, a): + if not self.scene.multiplayer: # if multiplayer, action first applied to all robots, then global step() called, then _step() for all robots with the same actions + self.robot.apply_action(a) + self.scene.global_step() + + state = self.robot.calc_state() # also calculates self.joints_at_limit + + self._alive = float( + self.robot.alive_bonus( + state[0] + self.robot.initial_z, + self.robot.body_rpy[1])) # state[0] is body height above ground, body_rpy[1] is pitch + done = self._isDone() + if not np.isfinite(state).all(): + print("~INF~", state) + done = True + + potential_old = self.potential + self.potential = self.robot.calc_potential() + progress = float(self.potential - potential_old) + + feet_collision_cost = 0.0 + for i, f in enumerate( + self.robot.feet + ): # TODO: Maybe calculating feet contacts could be done within the robot code + contact_ids = set((x[2], x[4]) for x in f.contact_list()) + #print("CONTACT OF '%d' WITH %d" % (contact_ids, ",".join(contact_names)) ) + if (self.ground_ids & contact_ids): + #see Issue 63: https://github.com/openai/roboschool/issues/63 + #feet_collision_cost += self.foot_collision_cost + self.robot.feet_contact[i] = 1.0 + else: + self.robot.feet_contact[i] = 0.0 + + electricity_cost = self.electricity_cost * float(np.abs(a * self.robot.joint_speeds).mean( + )) # let's assume we have DC motor with controller, and reverse current braking + electricity_cost += self.stall_torque_cost * float(np.square(a).mean()) + + joints_at_limit_cost = float(self.joints_at_limit_cost * self.robot.joints_at_limit) + debugmode = 0 + if (debugmode): + print("alive=") + print(self._alive) + print("progress") + print(progress) + print("electricity_cost") + print(electricity_cost) + print("joints_at_limit_cost") + print(joints_at_limit_cost) + print("feet_collision_cost") + print(feet_collision_cost) + + self.rewards = [ + self._alive, progress, electricity_cost, joints_at_limit_cost, feet_collision_cost + ] + if (debugmode): + print("rewards=") + print(self.rewards) + print("sum rewards") + print(sum(self.rewards)) + self.HUD(state, a, done) + self.reward += sum(self.rewards) + + return state, sum(self.rewards), bool(done), {} + + def camera_adjust(self): + x, y, z = self.body_xyz + self.camera_x = 0.98 * self.camera_x + (1 - 0.98) * x + self.camera.move_and_look_at(self.camera_x, y - 2.0, 1.4, x, y, 1.0) + class HopperBulletEnv(WalkerBaseBulletEnv): - def __init__(self, render=False): - self.robot = Hopper() - WalkerBaseBulletEnv.__init__(self, self.robot, render) + + def __init__(self, render=False): + self.robot = Hopper() + WalkerBaseBulletEnv.__init__(self, self.robot, render) + class Walker2DBulletEnv(WalkerBaseBulletEnv): - def __init__(self, render=False): - self.robot = Walker2D() - WalkerBaseBulletEnv.__init__(self, self.robot, render) + + def __init__(self, render=False): + self.robot = Walker2D() + WalkerBaseBulletEnv.__init__(self, self.robot, render) + class HalfCheetahBulletEnv(WalkerBaseBulletEnv): - def __init__(self, render=False): - self.robot = HalfCheetah() - WalkerBaseBulletEnv.__init__(self, self.robot, render) - def _isDone(self): - return False + def __init__(self, render=False): + self.robot = HalfCheetah() + WalkerBaseBulletEnv.__init__(self, self.robot, render) + + def _isDone(self): + return False + class AntBulletEnv(WalkerBaseBulletEnv): - def __init__(self, render=False): - self.robot = Ant() - WalkerBaseBulletEnv.__init__(self, self.robot, render) + + def __init__(self, render=False): + self.robot = Ant() + WalkerBaseBulletEnv.__init__(self, self.robot, render) + class HumanoidBulletEnv(WalkerBaseBulletEnv): - def __init__(self, robot=Humanoid(), render=False): - self.robot = robot - WalkerBaseBulletEnv.__init__(self, self.robot, render) - self.electricity_cost = 4.25*WalkerBaseBulletEnv.electricity_cost - self.stall_torque_cost = 4.25*WalkerBaseBulletEnv.stall_torque_cost + + def __init__(self, robot=Humanoid(), render=False): + self.robot = robot + WalkerBaseBulletEnv.__init__(self, self.robot, render) + self.electricity_cost = 4.25 * WalkerBaseBulletEnv.electricity_cost + self.stall_torque_cost = 4.25 * WalkerBaseBulletEnv.stall_torque_cost + class HumanoidFlagrunBulletEnv(HumanoidBulletEnv): - random_yaw = True + random_yaw = True + + def __init__(self, render=False): + self.robot = HumanoidFlagrun() + HumanoidBulletEnv.__init__(self, self.robot, render) - def __init__(self, render=False): - self.robot = HumanoidFlagrun() - HumanoidBulletEnv.__init__(self, self.robot, render) + def create_single_player_scene(self, bullet_client): + s = HumanoidBulletEnv.create_single_player_scene(self, bullet_client) + s.zero_at_running_strip_start_line = False + return s - def create_single_player_scene(self, bullet_client): - s = HumanoidBulletEnv.create_single_player_scene(self, bullet_client) - s.zero_at_running_strip_start_line = False - return s class HumanoidFlagrunHarderBulletEnv(HumanoidBulletEnv): - random_lean = True # can fall on start + random_lean = True # can fall on start - def __init__(self, render=False): - self.robot = HumanoidFlagrunHarder() - self.electricity_cost /= 4 # don't care that much about electricity, just stand up! - HumanoidBulletEnv.__init__(self, self.robot, render) + def __init__(self, render=False): + self.robot = HumanoidFlagrunHarder() + self.electricity_cost /= 4 # don't care that much about electricity, just stand up! + HumanoidBulletEnv.__init__(self, self.robot, render) - def create_single_player_scene(self, bullet_client): - s = HumanoidBulletEnv.create_single_player_scene(self, bullet_client) - s.zero_at_running_strip_start_line = False - return s + def create_single_player_scene(self, bullet_client): + s = HumanoidBulletEnv.create_single_player_scene(self, bullet_client) + s.zero_at_running_strip_start_line = False + return s diff --git a/examples/pybullet/gym/pybullet_envs/gym_manipulator_envs.py b/examples/pybullet/gym/pybullet_envs/gym_manipulator_envs.py index 8ac4f8ad2..9fef7e615 100644 --- a/examples/pybullet/gym/pybullet_envs/gym_manipulator_envs.py +++ b/examples/pybullet/gym/pybullet_envs/gym_manipulator_envs.py @@ -5,229 +5,250 @@ from robot_manipulators import Reacher, Pusher, Striker, Thrower class ReacherBulletEnv(MJCFBaseBulletEnv): - def __init__(self, render=False): - self.robot = Reacher() - MJCFBaseBulletEnv.__init__(self, self.robot, render) - def create_single_player_scene(self, bullet_client): - return SingleRobotEmptyScene(bullet_client, gravity=0.0, timestep=0.0165, frame_skip=1) + def __init__(self, render=False): + self.robot = Reacher() + MJCFBaseBulletEnv.__init__(self, self.robot, render) - def step(self, a): - assert (not self.scene.multiplayer) - self.robot.apply_action(a) - self.scene.global_step() + def create_single_player_scene(self, bullet_client): + return SingleRobotEmptyScene(bullet_client, gravity=0.0, timestep=0.0165, frame_skip=1) - state = self.robot.calc_state() # sets self.to_target_vec + def step(self, a): + assert (not self.scene.multiplayer) + self.robot.apply_action(a) + self.scene.global_step() - potential_old = self.potential - self.potential = self.robot.calc_potential() + state = self.robot.calc_state() # sets self.to_target_vec - electricity_cost = ( - -0.10 * (np.abs(a[0] * self.robot.theta_dot) + np.abs(a[1] * self.robot.gamma_dot)) # work torque*angular_velocity - - 0.01 * (np.abs(a[0]) + np.abs(a[1])) # stall torque require some energy - ) - stuck_joint_cost = -0.1 if np.abs(np.abs(self.robot.gamma) - 1) < 0.01 else 0.0 - self.rewards = [float(self.potential - potential_old), float(electricity_cost), float(stuck_joint_cost)] - self.HUD(state, a, False) - return state, sum(self.rewards), False, {} + potential_old = self.potential + self.potential = self.robot.calc_potential() - def camera_adjust(self): - x, y, z = self.robot.fingertip.pose().xyz() - x *= 0.5 - y *= 0.5 - self.camera.move_and_look_at(0.3, 0.3, 0.3, x, y, z) + electricity_cost = ( + -0.10 * (np.abs(a[0] * self.robot.theta_dot) + np.abs(a[1] * self.robot.gamma_dot) + ) # work torque*angular_velocity + - 0.01 * (np.abs(a[0]) + np.abs(a[1])) # stall torque require some energy + ) + stuck_joint_cost = -0.1 if np.abs(np.abs(self.robot.gamma) - 1) < 0.01 else 0.0 + self.rewards = [ + float(self.potential - potential_old), + float(electricity_cost), + float(stuck_joint_cost) + ] + self.HUD(state, a, False) + return state, sum(self.rewards), False, {} + def camera_adjust(self): + x, y, z = self.robot.fingertip.pose().xyz() + x *= 0.5 + y *= 0.5 + self.camera.move_and_look_at(0.3, 0.3, 0.3, x, y, z) -class PusherBulletEnv(MJCFBaseBulletEnv): - def __init__(self, render=False): - self.robot = Pusher() - MJCFBaseBulletEnv.__init__(self, self.robot, render) - - def create_single_player_scene(self, bullet_client): - return SingleRobotEmptyScene(bullet_client, gravity=9.81, timestep=0.0020, frame_skip=5) - - def step(self, a): - self.robot.apply_action(a) - self.scene.global_step() - - state = self.robot.calc_state() # sets self.to_target_vec - - potential_old = self.potential - self.potential = self.robot.calc_potential() - - joint_vel = np.array([ - self.robot.shoulder_pan_joint.get_velocity(), - self.robot.shoulder_lift_joint.get_velocity(), - self.robot.upper_arm_roll_joint.get_velocity(), - self.robot.elbow_flex_joint.get_velocity(), - self.robot.upper_arm_roll_joint.get_velocity(), - self.robot.wrist_flex_joint.get_velocity(), - self.robot.wrist_roll_joint.get_velocity() - ]) - - action_product = np.matmul(np.abs(a), np.abs(joint_vel)) - action_sum = np.sum(a) - - electricity_cost = ( - -0.10 * action_product # work torque*angular_velocity - - 0.01 * action_sum # stall torque require some energy - ) - - stuck_joint_cost = 0 - for j in self.robot.ordered_joints: - if np.abs(j.current_relative_position()[0]) - 1 < 0.01: - stuck_joint_cost += -0.1 - self.rewards = [float(self.potential - potential_old), float(electricity_cost), float(stuck_joint_cost)] - self.HUD(state, a, False) - return state, sum(self.rewards), False, {} - - def calc_potential(self): - return -100 * np.linalg.norm(self.to_target_vec) - - def camera_adjust(self): - x, y, z = self.robot.fingertip.pose().xyz() - x *= 0.5 - y *= 0.5 - self.camera.move_and_look_at(0.3, 0.3, 0.3, x, y, z) - - -class StrikerBulletEnv(MJCFBaseBulletEnv): - def __init__(self, render=False): - self.robot = Striker() - MJCFBaseBulletEnv.__init__(self, self.robot, render) - self._striked = False - self._min_strike_dist = np.inf - self.strike_threshold = 0.1 - - def create_single_player_scene(self, bullet_client): - return SingleRobotEmptyScene(bullet_client, gravity=9.81, timestep=0.0020, frame_skip=5) +class PusherBulletEnv(MJCFBaseBulletEnv): - def step(self, a): - self.robot.apply_action(a) - self.scene.global_step() + def __init__(self, render=False): + self.robot = Pusher() + MJCFBaseBulletEnv.__init__(self, self.robot, render) - state = self.robot.calc_state() # sets self.to_target_vec + def create_single_player_scene(self, bullet_client): + return SingleRobotEmptyScene(bullet_client, gravity=9.81, timestep=0.0020, frame_skip=5) - potential_old = self.potential - self.potential = self.robot.calc_potential() + def step(self, a): + self.robot.apply_action(a) + self.scene.global_step() - joint_vel = np.array([ - self.robot.shoulder_pan_joint.get_velocity(), - self.robot.shoulder_lift_joint.get_velocity(), - self.robot.upper_arm_roll_joint.get_velocity(), - self.robot.elbow_flex_joint.get_velocity(), - self.robot.upper_arm_roll_joint.get_velocity(), - self.robot.wrist_flex_joint.get_velocity(), - self.robot.wrist_roll_joint.get_velocity() - ]) + state = self.robot.calc_state() # sets self.to_target_vec - action_product = np.matmul(np.abs(a), np.abs(joint_vel)) - action_sum = np.sum(a) + potential_old = self.potential + self.potential = self.robot.calc_potential() - electricity_cost = ( - -0.10 * action_product # work torque*angular_velocity - - 0.01 * action_sum # stall torque require some energy - ) + joint_vel = np.array([ + self.robot.shoulder_pan_joint.get_velocity(), + self.robot.shoulder_lift_joint.get_velocity(), + self.robot.upper_arm_roll_joint.get_velocity(), + self.robot.elbow_flex_joint.get_velocity(), + self.robot.upper_arm_roll_joint.get_velocity(), + self.robot.wrist_flex_joint.get_velocity(), + self.robot.wrist_roll_joint.get_velocity() + ]) - stuck_joint_cost = 0 - for j in self.robot.ordered_joints: - if np.abs(j.current_relative_position()[0]) - 1 < 0.01: - stuck_joint_cost += -0.1 + action_product = np.matmul(np.abs(a), np.abs(joint_vel)) + action_sum = np.sum(a) - dist_object_finger = self.robot.object.pose().xyz() - self.robot.fingertip.pose().xyz() - reward_dist_vec = self.robot.object.pose().xyz() - self.robot.target.pose().xyz() # TODO: Should the object and target really belong to the robot? Maybe split this off + electricity_cost = ( + -0.10 * action_product # work torque*angular_velocity + - 0.01 * action_sum # stall torque require some energy + ) - self._min_strike_dist = min(self._min_strike_dist, np.linalg.norm(reward_dist_vec)) + stuck_joint_cost = 0 + for j in self.robot.ordered_joints: + if np.abs(j.current_relative_position()[0]) - 1 < 0.01: + stuck_joint_cost += -0.1 - if np.linalg.norm(dist_object_finger) < self.strike_threshold: - self._striked = True - self._strike_pos = self.robot.fingertip.pose().xyz() + self.rewards = [ + float(self.potential - potential_old), + float(electricity_cost), + float(stuck_joint_cost) + ] + self.HUD(state, a, False) + return state, sum(self.rewards), False, {} - if self._striked: - reward_near_vec = self.robot.object.pose().xyz() - self._strike_pos - else: - reward_near_vec = self.robot.object.pose().xyz() - self.robot.fingertip.pose().xyz() + def calc_potential(self): + return -100 * np.linalg.norm(self.to_target_vec) - reward_near = - np.linalg.norm(reward_near_vec) + def camera_adjust(self): + x, y, z = self.robot.fingertip.pose().xyz() + x *= 0.5 + y *= 0.5 + self.camera.move_and_look_at(0.3, 0.3, 0.3, x, y, z) - reward_dist = - np.linalg.norm(self._min_strike_dist) - reward_ctrl = - np.square(a).sum() - self.rewards = [float(self.potential - potential_old), float(electricity_cost), float(stuck_joint_cost), - 3 * reward_dist, 0.1 * reward_ctrl, 0.5 * reward_near] - self.HUD(state, a, False) - return state, sum(self.rewards), False, {} - def calc_potential(self): - return -100 * np.linalg.norm(self.to_target_vec) +class StrikerBulletEnv(MJCFBaseBulletEnv): - def camera_adjust(self): - x, y, z = self.robot.fingertip.pose().xyz() - x *= 0.5 - y *= 0.5 - self.camera.move_and_look_at(0.3, 0.3, 0.3, x, y, z) + def __init__(self, render=False): + self.robot = Striker() + MJCFBaseBulletEnv.__init__(self, self.robot, render) + self._striked = False + self._min_strike_dist = np.inf + self.strike_threshold = 0.1 + + def create_single_player_scene(self, bullet_client): + return SingleRobotEmptyScene(bullet_client, gravity=9.81, timestep=0.0020, frame_skip=5) + + def step(self, a): + self.robot.apply_action(a) + self.scene.global_step() + + state = self.robot.calc_state() # sets self.to_target_vec + + potential_old = self.potential + self.potential = self.robot.calc_potential() + + joint_vel = np.array([ + self.robot.shoulder_pan_joint.get_velocity(), + self.robot.shoulder_lift_joint.get_velocity(), + self.robot.upper_arm_roll_joint.get_velocity(), + self.robot.elbow_flex_joint.get_velocity(), + self.robot.upper_arm_roll_joint.get_velocity(), + self.robot.wrist_flex_joint.get_velocity(), + self.robot.wrist_roll_joint.get_velocity() + ]) + + action_product = np.matmul(np.abs(a), np.abs(joint_vel)) + action_sum = np.sum(a) + + electricity_cost = ( + -0.10 * action_product # work torque*angular_velocity + - 0.01 * action_sum # stall torque require some energy + ) + + stuck_joint_cost = 0 + for j in self.robot.ordered_joints: + if np.abs(j.current_relative_position()[0]) - 1 < 0.01: + stuck_joint_cost += -0.1 + + dist_object_finger = self.robot.object.pose().xyz() - self.robot.fingertip.pose().xyz() + reward_dist_vec = self.robot.object.pose().xyz() - self.robot.target.pose().xyz( + ) # TODO: Should the object and target really belong to the robot? Maybe split this off + + self._min_strike_dist = min(self._min_strike_dist, np.linalg.norm(reward_dist_vec)) + + if np.linalg.norm(dist_object_finger) < self.strike_threshold: + self._striked = True + self._strike_pos = self.robot.fingertip.pose().xyz() + + if self._striked: + reward_near_vec = self.robot.object.pose().xyz() - self._strike_pos + else: + reward_near_vec = self.robot.object.pose().xyz() - self.robot.fingertip.pose().xyz() + + reward_near = -np.linalg.norm(reward_near_vec) + + reward_dist = -np.linalg.norm(self._min_strike_dist) + reward_ctrl = -np.square(a).sum() + self.rewards = [ + float(self.potential - potential_old), + float(electricity_cost), + float(stuck_joint_cost), 3 * reward_dist, 0.1 * reward_ctrl, 0.5 * reward_near + ] + self.HUD(state, a, False) + return state, sum(self.rewards), False, {} + + def calc_potential(self): + return -100 * np.linalg.norm(self.to_target_vec) + + def camera_adjust(self): + x, y, z = self.robot.fingertip.pose().xyz() + x *= 0.5 + y *= 0.5 + self.camera.move_and_look_at(0.3, 0.3, 0.3, x, y, z) class ThrowerBulletEnv(MJCFBaseBulletEnv): - def __init__(self, render=False): - self.robot = Thrower() - MJCFBaseBulletEnv.__init__(self, self.robot, render) - - def create_single_player_scene(self, bullet_client): - return SingleRobotEmptyScene(bullet_client, gravity=0.0, timestep=0.0020, frame_skip=5) - - def step(self, a): - self.robot.apply_action(a) - self.scene.global_step() - state = self.robot.calc_state() # sets self.to_target_vec - - potential_old = self.potential - self.potential = self.robot.calc_potential() - - joint_vel = np.array([ - self.robot.shoulder_pan_joint.get_velocity(), - self.robot.shoulder_lift_joint.get_velocity(), - self.robot.upper_arm_roll_joint.get_velocity(), - self.robot.elbow_flex_joint.get_velocity(), - self.robot.upper_arm_roll_joint.get_velocity(), - self.robot.wrist_flex_joint.get_velocity(), - self.robot.wrist_roll_joint.get_velocity() - ]) - - action_product = np.matmul(np.abs(a), np.abs(joint_vel)) - action_sum = np.sum(a) - - electricity_cost = ( - -0.10 * action_product # work torque*angular_velocity - - 0.01 * action_sum # stall torque require some energy - ) - - stuck_joint_cost = 0 - for j in self.robot.ordered_joints: - if np.abs(j.current_relative_position()[0]) - 1 < 0.01: - stuck_joint_cost += -0.1 - - object_xy = self.robot.object.pose().xyz()[:2] - target_xy = self.robot.target.pose().xyz()[:2] - - if not self.robot._object_hit_ground and self.robot.object.pose().xyz()[2] < -0.25: # TODO: Should the object and target really belong to the robot? Maybe split this off - self.robot._object_hit_ground = True - self.robot._object_hit_location = self.robot.object.pose().xyz() - - if self.robot._object_hit_ground: - object_hit_xy = self.robot._object_hit_location[:2] - reward_dist = -np.linalg.norm(object_hit_xy - target_xy) - else: - reward_dist = -np.linalg.norm(object_xy - target_xy) - reward_ctrl = - np.square(a).sum() - - self.rewards = [float(self.potential - potential_old), float(electricity_cost), float(stuck_joint_cost), - reward_dist, 0.002 * reward_ctrl] - self.HUD(state, a, False) - return state, sum(self.rewards), False, {} - - def camera_adjust(self): - x, y, z = self.robot.fingertip.pose().xyz() - x *= 0.5 - y *= 0.5 - self.camera.move_and_look_at(0.3, 0.3, 0.3, x, y, z) + + def __init__(self, render=False): + self.robot = Thrower() + MJCFBaseBulletEnv.__init__(self, self.robot, render) + + def create_single_player_scene(self, bullet_client): + return SingleRobotEmptyScene(bullet_client, gravity=0.0, timestep=0.0020, frame_skip=5) + + def step(self, a): + self.robot.apply_action(a) + self.scene.global_step() + state = self.robot.calc_state() # sets self.to_target_vec + + potential_old = self.potential + self.potential = self.robot.calc_potential() + + joint_vel = np.array([ + self.robot.shoulder_pan_joint.get_velocity(), + self.robot.shoulder_lift_joint.get_velocity(), + self.robot.upper_arm_roll_joint.get_velocity(), + self.robot.elbow_flex_joint.get_velocity(), + self.robot.upper_arm_roll_joint.get_velocity(), + self.robot.wrist_flex_joint.get_velocity(), + self.robot.wrist_roll_joint.get_velocity() + ]) + + action_product = np.matmul(np.abs(a), np.abs(joint_vel)) + action_sum = np.sum(a) + + electricity_cost = ( + -0.10 * action_product # work torque*angular_velocity + - 0.01 * action_sum # stall torque require some energy + ) + + stuck_joint_cost = 0 + for j in self.robot.ordered_joints: + if np.abs(j.current_relative_position()[0]) - 1 < 0.01: + stuck_joint_cost += -0.1 + + object_xy = self.robot.object.pose().xyz()[:2] + target_xy = self.robot.target.pose().xyz()[:2] + + if not self.robot._object_hit_ground and self.robot.object.pose().xyz( + )[2] < -0.25: # TODO: Should the object and target really belong to the robot? Maybe split this off + self.robot._object_hit_ground = True + self.robot._object_hit_location = self.robot.object.pose().xyz() + + if self.robot._object_hit_ground: + object_hit_xy = self.robot._object_hit_location[:2] + reward_dist = -np.linalg.norm(object_hit_xy - target_xy) + else: + reward_dist = -np.linalg.norm(object_xy - target_xy) + reward_ctrl = -np.square(a).sum() + + self.rewards = [ + float(self.potential - potential_old), + float(electricity_cost), + float(stuck_joint_cost), reward_dist, 0.002 * reward_ctrl + ] + self.HUD(state, a, False) + return state, sum(self.rewards), False, {} + + def camera_adjust(self): + x, y, z = self.robot.fingertip.pose().xyz() + x *= 0.5 + y *= 0.5 + self.camera.move_and_look_at(0.3, 0.3, 0.3, x, y, z) diff --git a/examples/pybullet/gym/pybullet_envs/gym_pendulum_envs.py b/examples/pybullet/gym/pybullet_envs/gym_pendulum_envs.py index 07924c453..310874de8 100644 --- a/examples/pybullet/gym/pybullet_envs/gym_pendulum_envs.py +++ b/examples/pybullet/gym/pybullet_envs/gym_pendulum_envs.py @@ -3,83 +3,90 @@ from .env_bases import MJCFBaseBulletEnv from robot_pendula import InvertedPendulum, InvertedPendulumSwingup, InvertedDoublePendulum import gym, gym.spaces, gym.utils, gym.utils.seeding import numpy as np -import pybullet +import pybullet import os, sys + class InvertedPendulumBulletEnv(MJCFBaseBulletEnv): - def __init__(self): - self.robot = InvertedPendulum() - MJCFBaseBulletEnv.__init__(self, self.robot) - self.stateId=-1 - - def create_single_player_scene(self, bullet_client): - return SingleRobotEmptyScene(bullet_client, gravity=9.8, timestep=0.0165, frame_skip=1) - - def reset(self): - if (self.stateId>=0): - #print("InvertedPendulumBulletEnv reset p.restoreState(",self.stateId,")") - self._p.restoreState(self.stateId) - r = MJCFBaseBulletEnv.reset(self) - if (self.stateId<0): - self.stateId = self._p.saveState() - #print("InvertedPendulumBulletEnv reset self.stateId=",self.stateId) - return r - - def step(self, a): - self.robot.apply_action(a) - self.scene.global_step() - state = self.robot.calc_state() # sets self.pos_x self.pos_y - vel_penalty = 0 - if self.robot.swingup: - reward = np.cos(self.robot.theta) - done = False - else: - reward = 1.0 - done = np.abs(self.robot.theta) > .2 - self.rewards = [float(reward)] - self.HUD(state, a, done) - return state, sum(self.rewards), done, {} - - def camera_adjust(self): - self.camera.move_and_look_at(0,1.2,1.0, 0,0,0.5) + + def __init__(self): + self.robot = InvertedPendulum() + MJCFBaseBulletEnv.__init__(self, self.robot) + self.stateId = -1 + + def create_single_player_scene(self, bullet_client): + return SingleRobotEmptyScene(bullet_client, gravity=9.8, timestep=0.0165, frame_skip=1) + + def reset(self): + if (self.stateId >= 0): + #print("InvertedPendulumBulletEnv reset p.restoreState(",self.stateId,")") + self._p.restoreState(self.stateId) + r = MJCFBaseBulletEnv.reset(self) + if (self.stateId < 0): + self.stateId = self._p.saveState() + #print("InvertedPendulumBulletEnv reset self.stateId=",self.stateId) + return r + + def step(self, a): + self.robot.apply_action(a) + self.scene.global_step() + state = self.robot.calc_state() # sets self.pos_x self.pos_y + vel_penalty = 0 + if self.robot.swingup: + reward = np.cos(self.robot.theta) + done = False + else: + reward = 1.0 + done = np.abs(self.robot.theta) > .2 + self.rewards = [float(reward)] + self.HUD(state, a, done) + return state, sum(self.rewards), done, {} + + def camera_adjust(self): + self.camera.move_and_look_at(0, 1.2, 1.0, 0, 0, 0.5) + class InvertedPendulumSwingupBulletEnv(InvertedPendulumBulletEnv): - def __init__(self): - self.robot = InvertedPendulumSwingup() - MJCFBaseBulletEnv.__init__(self, self.robot) - self.stateId=-1 + + def __init__(self): + self.robot = InvertedPendulumSwingup() + MJCFBaseBulletEnv.__init__(self, self.robot) + self.stateId = -1 + class InvertedDoublePendulumBulletEnv(MJCFBaseBulletEnv): - def __init__(self): - self.robot = InvertedDoublePendulum() - MJCFBaseBulletEnv.__init__(self, self.robot) - self.stateId = -1 - def create_single_player_scene(self, bullet_client): - return SingleRobotEmptyScene(bullet_client, gravity=9.8, timestep=0.0165, frame_skip=1) - - def reset(self): - if (self.stateId>=0): - self._p.restoreState(self.stateId) - r = MJCFBaseBulletEnv.reset(self) - if (self.stateId<0): - self.stateId = self._p.saveState() - return r - - def step(self, a): - self.robot.apply_action(a) - self.scene.global_step() - state = self.robot.calc_state() # sets self.pos_x self.pos_y - # upright position: 0.6 (one pole) + 0.6 (second pole) * 0.5 (middle of second pole) = 0.9 - # using <site> tag in original xml, upright position is 0.6 + 0.6 = 1.2, difference +0.3 - dist_penalty = 0.01 * self.robot.pos_x ** 2 + (self.robot.pos_y + 0.3 - 2) ** 2 - # v1, v2 = self.model.data.qvel[1:3] TODO when this fixed https://github.com/bulletphysics/bullet3/issues/1040 - #vel_penalty = 1e-3 * v1**2 + 5e-3 * v2**2 - vel_penalty = 0 - alive_bonus = 10 - done = self.robot.pos_y + 0.3 <= 1 - self.rewards = [float(alive_bonus), float(-dist_penalty), float(-vel_penalty)] - self.HUD(state, a, done) - return state, sum(self.rewards), done, {} - - def camera_adjust(self): - self.camera.move_and_look_at(0,1.2,1.2, 0,0,0.5) + + def __init__(self): + self.robot = InvertedDoublePendulum() + MJCFBaseBulletEnv.__init__(self, self.robot) + self.stateId = -1 + + def create_single_player_scene(self, bullet_client): + return SingleRobotEmptyScene(bullet_client, gravity=9.8, timestep=0.0165, frame_skip=1) + + def reset(self): + if (self.stateId >= 0): + self._p.restoreState(self.stateId) + r = MJCFBaseBulletEnv.reset(self) + if (self.stateId < 0): + self.stateId = self._p.saveState() + return r + + def step(self, a): + self.robot.apply_action(a) + self.scene.global_step() + state = self.robot.calc_state() # sets self.pos_x self.pos_y + # upright position: 0.6 (one pole) + 0.6 (second pole) * 0.5 (middle of second pole) = 0.9 + # using <site> tag in original xml, upright position is 0.6 + 0.6 = 1.2, difference +0.3 + dist_penalty = 0.01 * self.robot.pos_x**2 + (self.robot.pos_y + 0.3 - 2)**2 + # v1, v2 = self.model.data.qvel[1:3] TODO when this fixed https://github.com/bulletphysics/bullet3/issues/1040 + #vel_penalty = 1e-3 * v1**2 + 5e-3 * v2**2 + vel_penalty = 0 + alive_bonus = 10 + done = self.robot.pos_y + 0.3 <= 1 + self.rewards = [float(alive_bonus), float(-dist_penalty), float(-vel_penalty)] + self.HUD(state, a, done) + return state, sum(self.rewards), done, {} + + def camera_adjust(self): + self.camera.move_and_look_at(0, 1.2, 1.2, 0, 0, 0.5) diff --git a/examples/pybullet/gym/pybullet_envs/kerasrl_utils.py b/examples/pybullet/gym/pybullet_envs/kerasrl_utils.py index 1ccedce5c..8045fff6c 100644 --- a/examples/pybullet/gym/pybullet_envs/kerasrl_utils.py +++ b/examples/pybullet/gym/pybullet_envs/kerasrl_utils.py @@ -1,28 +1,32 @@ - import re from gym import error import glob - # checkpoints/KerasDDPG-InvertedPendulum-v0-20170701190920_actor.h5 +# checkpoints/KerasDDPG-InvertedPendulum-v0-20170701190920_actor.h5 weight_save_re = re.compile(r'^(?:\w+\/)+?(\w+-v\d+)-(\w+-v\d+)-(\d+)(?:_\w+)?\.(\w+)$') + def get_fields(weight_save_name): - match = weight_save_re.search(weight_save_name) - if not match: - raise error.Error('Attempted to read a malformed weight save: {}. (Currently all weight saves must be of the form {}.)'.format(id,weight_save_re.pattern)) - return match.group(1), match.group(2), int(match.group(3)) + match = weight_save_re.search(weight_save_name) + if not match: + raise error.Error( + 'Attempted to read a malformed weight save: {}. (Currently all weight saves must be of the form {}.)' + .format(id, weight_save_re.pattern)) + return match.group(1), match.group(2), int(match.group(3)) + def get_latest_save(file_folder, agent_name, env_name, version_number): - """ + """ Returns the properties of the latest weight save. The information can be used to generate the loading path :return: """ - path = "%s%s"% (file_folder, "*.h5") - file_list = glob.glob(path) - latest_file_properties = [] - file_properties = [] - for f in file_list: - file_properties = get_fields(f) - if file_properties[0] == agent_name and file_properties[1] == env_name and (latest_file_properties == [] or file_properties[2] > latest_file_properties[2]): - latest_file_properties = file_properties + path = "%s%s" % (file_folder, "*.h5") + file_list = glob.glob(path) + latest_file_properties = [] + file_properties = [] + for f in file_list: + file_properties = get_fields(f) + if file_properties[0] == agent_name and file_properties[1] == env_name and ( + latest_file_properties == [] or file_properties[2] > latest_file_properties[2]): + latest_file_properties = file_properties - return latest_file_properties + return latest_file_properties diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/__init__.py b/examples/pybullet/gym/pybullet_envs/minitaur/__init__.py index b28b04f64..8b1378917 100644 --- a/examples/pybullet/gym/pybullet_envs/minitaur/__init__.py +++ b/examples/pybullet/gym/pybullet_envs/minitaur/__init__.py @@ -1,3 +1 @@ - - diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/actuatornet/__init__.py b/examples/pybullet/gym/pybullet_envs/minitaur/actuatornet/__init__.py index 139597f9c..8b1378917 100644 --- a/examples/pybullet/gym/pybullet_envs/minitaur/actuatornet/__init__.py +++ b/examples/pybullet/gym/pybullet_envs/minitaur/actuatornet/__init__.py @@ -1,2 +1 @@ - diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/actuatornet/actuatornet_keras.py b/examples/pybullet/gym/pybullet_envs/minitaur/actuatornet/actuatornet_keras.py index 016fee5ea..57b27068f 100644 --- a/examples/pybullet/gym/pybullet_envs/minitaur/actuatornet/actuatornet_keras.py +++ b/examples/pybullet/gym/pybullet_envs/minitaur/actuatornet/actuatornet_keras.py @@ -1,4 +1,3 @@ - from __future__ import absolute_import from __future__ import division from __future__ import print_function @@ -22,9 +21,7 @@ def main(unused_argv): # fix random seed for reproducibility numpy.random.seed(7) # load pima indians dataset - dataset = numpy.loadtxt( - FLAGS.input_filename, - delimiter=",") + dataset = numpy.loadtxt(FLAGS.input_filename, delimiter=",") # split into input (X) and output (Y) variables x = dataset[:, 0:3] y = dataset[:, 3] @@ -38,28 +35,27 @@ def main(unused_argv): model.add(Dense(1, activation="linear")) # Compile model (use adam or sgd) - model.compile( - loss="mean_squared_error", - optimizer="adam", - metrics=["mean_squared_error"]) + model.compile(loss="mean_squared_error", optimizer="adam", metrics=["mean_squared_error"]) # checkpoint filepath = "/tmp/keras/weights-improvement-{epoch:02d}-{val_loss:.2f}.hdf5" - checkpoint = ModelCheckpoint( - filepath, monitor="val_loss", verbose=1, save_best_only=True, mode="min") + checkpoint = ModelCheckpoint(filepath, + monitor="val_loss", + verbose=1, + save_best_only=True, + mode="min") callbacks_list = [checkpoint] # Fit the model # model.fit(X, Y, epochs=150, batch_size=10) # model.fit(X, Y, epochs=150, batch_size=10, callbacks=callbacks_list) - model.fit( - x, - y, - validation_split=0.34, - epochs=4500, - batch_size=1024, - callbacks=callbacks_list, - verbose=0) + model.fit(x, + y, + validation_split=0.34, + epochs=4500, + batch_size=1024, + callbacks=callbacks_list, + verbose=0) # evaluate the model scores = model.evaluate(x, y) diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/actuatornet/actuatornet_keras_lstm.py b/examples/pybullet/gym/pybullet_envs/minitaur/actuatornet/actuatornet_keras_lstm.py index f3a45a1dd..35f7a16a1 100644 --- a/examples/pybullet/gym/pybullet_envs/minitaur/actuatornet/actuatornet_keras_lstm.py +++ b/examples/pybullet/gym/pybullet_envs/minitaur/actuatornet/actuatornet_keras_lstm.py @@ -75,8 +75,8 @@ reframed = series_to_supervised(scaled, lag_steps, 1) print("reframed before drop=", reframed) # drop columns we don't want to predict -reframed.drop(reframed.columns[[3,7,11,15,19]], axis=1, inplace=True) -print("after drop=",reframed.head()) +reframed.drop(reframed.columns[[3, 7, 11, 15, 19]], axis=1, inplace=True) +print("after drop=", reframed.head()) #dummy = scaler.inverse_transform(reframed) #print(dummy) @@ -104,17 +104,17 @@ test = values[n_train_hours:, :] train_X, train_y = train[:, :-1], train[:, -1] test_X, test_y = test[:, :-1], test[:, -1] -print("train_X.shape[1]=",train_X.shape[1]) - +print("train_X.shape[1]=", train_X.shape[1]) # design network -useLSTM=True +useLSTM = True if useLSTM: # reshape input to be 3D [samples, timesteps, features] - train_X = train_X.reshape((train_X.shape[0], lag_steps+1, int(train_X.shape[1]/(lag_steps+1)))) - test_X = test_X.reshape((test_X.shape[0], lag_steps+1, int(test_X.shape[1]/(lag_steps+1)))) + train_X = train_X.reshape( + (train_X.shape[0], lag_steps + 1, int(train_X.shape[1] / (lag_steps + 1)))) + test_X = test_X.reshape((test_X.shape[0], lag_steps + 1, int(test_X.shape[1] / (lag_steps + 1)))) model = Sequential() - model.add(LSTM(40, input_shape=(train_X.shape[1], train_X.shape[2]))) + model.add(LSTM(40, input_shape=(train_X.shape[1], train_X.shape[2]))) model.add(Dropout(0.05)) model.add(Dense(8, activation='sigmoid')) model.add(Dense(8, activation='sigmoid')) @@ -128,39 +128,37 @@ else: model.add(Dense(1, activation="linear")) #model.compile(loss='mae', optimizer='adam') -model.compile( - loss='mean_squared_error', optimizer='adam', metrics=['mean_squared_error']) +model.compile(loss='mean_squared_error', optimizer='adam', metrics=['mean_squared_error']) # checkpoint filepath = '/tmp/keras/weights-improvement-{epoch:02d}-{val_loss:.2f}.hdf5' -checkpoint = ModelCheckpoint( - filepath, monitor='val_loss', verbose=1, save_best_only=True, mode='min') +checkpoint = ModelCheckpoint(filepath, + monitor='val_loss', + verbose=1, + save_best_only=True, + mode='min') callbacks_list = [checkpoint] # fit network -history = model.fit( - train_X, - train_y, - epochs=1500, - batch_size=32, - callbacks=callbacks_list, - validation_data=(test_X, test_y), - verbose=2, - shuffle=False) +history = model.fit(train_X, + train_y, + epochs=1500, + batch_size=32, + callbacks=callbacks_list, + validation_data=(test_X, test_y), + verbose=2, + shuffle=False) # plot history - -data = np.array([[[1.513535008329887299,3.234624992847829894e-01,1.731481043119239782,1.741165415165205399, -1.534267104753672228e+00,1.071354965017878635e+00,1.712386127673626302e+00]]]) - +data = np.array([[[ + 1.513535008329887299, 3.234624992847829894e-01, 1.731481043119239782, 1.741165415165205399, + 1.534267104753672228e+00, 1.071354965017878635e+00, 1.712386127673626302e+00 +]]]) #prediction = model.predict(data) #print("prediction=",prediction) - pyplot.plot(history.history['loss'], label='train') pyplot.plot(history.history['val_loss'], label='test') pyplot.legend() pyplot.show() - - diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/actuatornet/minitaur_raibert_controller_example.py b/examples/pybullet/gym/pybullet_envs/minitaur/actuatornet/minitaur_raibert_controller_example.py index 0be299a75..40e6b4799 100644 --- a/examples/pybullet/gym/pybullet_envs/minitaur/actuatornet/minitaur_raibert_controller_example.py +++ b/examples/pybullet/gym/pybullet_envs/minitaur/actuatornet/minitaur_raibert_controller_example.py @@ -15,9 +15,8 @@ FLAGS = tf.app.flags.FLAGS flags.DEFINE_float("motor_kp", 1.0, "The position gain of the motor.") flags.DEFINE_float("motor_kd", 0.015, "The speed gain of the motor.") -flags.DEFINE_float( - "control_latency", 0.006, "The latency between sensor measurement and action" - " execution the robot.") +flags.DEFINE_float("control_latency", 0.006, "The latency between sensor measurement and action" + " execution the robot.") flags.DEFINE_string("log_path", ".", "The directory to write the log file.") @@ -31,37 +30,34 @@ def speed(t): def main(argv): - del argv - env = minitaur_gym_env.MinitaurGymEnv( - urdf_version=minitaur_gym_env.RAINBOW_DASH_V0_URDF_VERSION, - control_time_step=0.006, - action_repeat=6, - pd_latency=0.0, - control_latency=FLAGS.control_latency, - motor_kp=FLAGS.motor_kp, - motor_kd=FLAGS.motor_kd, - remove_default_joint_damping=True, - leg_model_enabled=False, - render=True, - on_rack=False, - accurate_motor_model_enabled=True, - log_path=FLAGS.log_path) - env.reset() + del argv + env = minitaur_gym_env.MinitaurGymEnv(urdf_version=minitaur_gym_env.RAINBOW_DASH_V0_URDF_VERSION, + control_time_step=0.006, + action_repeat=6, + pd_latency=0.0, + control_latency=FLAGS.control_latency, + motor_kp=FLAGS.motor_kp, + motor_kd=FLAGS.motor_kd, + remove_default_joint_damping=True, + leg_model_enabled=False, + render=True, + on_rack=False, + accurate_motor_model_enabled=True, + log_path=FLAGS.log_path) + env.reset() + + controller = minitaur_raibert_controller.MinitaurRaibertTrottingController(env.minitaur) + + tstart = env.minitaur.GetTimeSinceReset() + for _ in range(1000): + t = env.minitaur.GetTimeSinceReset() - tstart + controller.behavior_parameters = (minitaur_raibert_controller.BehaviorParameters( + desired_forward_speed=speed(t))) + controller.update(t) + env.step(controller.get_action()) + + #env.close() - controller = minitaur_raibert_controller.MinitaurRaibertTrottingController( - env.minitaur) - - tstart = env.minitaur.GetTimeSinceReset() - for _ in range(1000): - t = env.minitaur.GetTimeSinceReset() - tstart - controller.behavior_parameters = ( - minitaur_raibert_controller.BehaviorParameters( - desired_forward_speed=speed(t))) - controller.update(t) - env.step(controller.get_action()) - - #env.close() if __name__ == "__main__": tf.app.run(main) - diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/actuatornet/proto2csv.py b/examples/pybullet/gym/pybullet_envs/minitaur/actuatornet/proto2csv.py index 893c831e0..2e04313e8 100644 --- a/examples/pybullet/gym/pybullet_envs/minitaur/actuatornet/proto2csv.py +++ b/examples/pybullet/gym/pybullet_envs/minitaur/actuatornet/proto2csv.py @@ -35,12 +35,11 @@ def main(argv): #print("motorState.velocity=",motorState.velocity) #print("motorState.action=",motorState.action) #print("motorState.torque=",motorState.torque) - recs.append([motorState.angle,motorState.velocity,motorState.action,motorState.torque]) - + recs.append([motorState.angle, motorState.velocity, motorState.action, motorState.torque]) + a = numpy.array(recs) numpy.savetxt(FLAGS.csv_file, a, delimiter=",") + if __name__ == "__main__": tf.app.run(main) - - diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/agents/__init__.py b/examples/pybullet/gym/pybullet_envs/minitaur/agents/__init__.py index 8d1c8b69c..8b1378917 100644 --- a/examples/pybullet/gym/pybullet_envs/minitaur/agents/__init__.py +++ b/examples/pybullet/gym/pybullet_envs/minitaur/agents/__init__.py @@ -1 +1 @@ - + diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/agents/ppo/__init__.py b/examples/pybullet/gym/pybullet_envs/minitaur/agents/ppo/__init__.py index 26a87baf9..6f4c222dd 100644 --- a/examples/pybullet/gym/pybullet_envs/minitaur/agents/ppo/__init__.py +++ b/examples/pybullet/gym/pybullet_envs/minitaur/agents/ppo/__init__.py @@ -11,7 +11,6 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. - """Proximal Policy Optimization algorithm.""" from __future__ import absolute_import diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/agents/ppo/algorithm.py b/examples/pybullet/gym/pybullet_envs/minitaur/agents/ppo/algorithm.py index 81b18b7b4..032eac760 100644 --- a/examples/pybullet/gym/pybullet_envs/minitaur/agents/ppo/algorithm.py +++ b/examples/pybullet/gym/pybullet_envs/minitaur/agents/ppo/algorithm.py @@ -11,7 +11,6 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. - """Proximal Policy Optimization algorithm. Based on John Schulman's implementation in Python and Theano: @@ -30,9 +29,7 @@ from . import memory from . import normalize from . import utility - -_NetworkOutput = collections.namedtuple( - 'NetworkOutput', 'policy, mean, logstd, value, state') +_NetworkOutput = collections.namedtuple('NetworkOutput', 'policy, mean, logstd, value, state') class PPOAlgorithm(object): @@ -53,44 +50,46 @@ class PPOAlgorithm(object): self._is_training = is_training self._should_log = should_log self._config = config - self._observ_filter = normalize.StreamingNormalize( - self._batch_env.observ[0], center=True, scale=True, clip=5, - name='normalize_observ') - self._reward_filter = normalize.StreamingNormalize( - self._batch_env.reward[0], center=False, scale=True, clip=10, - name='normalize_reward') + self._observ_filter = normalize.StreamingNormalize(self._batch_env.observ[0], + center=True, + scale=True, + clip=5, + name='normalize_observ') + self._reward_filter = normalize.StreamingNormalize(self._batch_env.reward[0], + center=False, + scale=True, + clip=10, + name='normalize_reward') # Memory stores tuple of observ, action, mean, logstd, reward. - template = ( - self._batch_env.observ[0], self._batch_env.action[0], - self._batch_env.action[0], self._batch_env.action[0], - self._batch_env.reward[0]) - self._memory = memory.EpisodeMemory( - template, config.update_every, config.max_length, 'memory') + template = (self._batch_env.observ[0], self._batch_env.action[0], self._batch_env.action[0], + self._batch_env.action[0], self._batch_env.reward[0]) + self._memory = memory.EpisodeMemory(template, config.update_every, config.max_length, 'memory') self._memory_index = tf.Variable(0, False) use_gpu = self._config.use_gpu and utility.available_gpus() with tf.device('/gpu:0' if use_gpu else '/cpu:0'): # Create network variables for later calls to reuse. - self._network( - tf.zeros_like(self._batch_env.observ)[:, None], - tf.ones(len(self._batch_env)), reuse=None) + self._network(tf.zeros_like(self._batch_env.observ)[:, None], + tf.ones(len(self._batch_env)), + reuse=None) cell = self._config.network(self._batch_env.action.shape[1].value) with tf.variable_scope('ppo_temporary'): - self._episodes = memory.EpisodeMemory( - template, len(batch_env), config.max_length, 'episodes') - self._last_state = utility.create_nested_vars( - cell.zero_state(len(batch_env), tf.float32)) - self._last_action = tf.Variable( - tf.zeros_like(self._batch_env.action), False, name='last_action') - self._last_mean = tf.Variable( - tf.zeros_like(self._batch_env.action), False, name='last_mean') - self._last_logstd = tf.Variable( - tf.zeros_like(self._batch_env.action), False, name='last_logstd') - self._penalty = tf.Variable( - self._config.kl_init_penalty, False, dtype=tf.float32) - self._policy_optimizer = self._config.policy_optimizer( - self._config.policy_lr, name='policy_optimizer') - self._value_optimizer = self._config.value_optimizer( - self._config.value_lr, name='value_optimizer') + self._episodes = memory.EpisodeMemory(template, len(batch_env), config.max_length, + 'episodes') + self._last_state = utility.create_nested_vars(cell.zero_state(len(batch_env), tf.float32)) + self._last_action = tf.Variable(tf.zeros_like(self._batch_env.action), + False, + name='last_action') + self._last_mean = tf.Variable(tf.zeros_like(self._batch_env.action), + False, + name='last_mean') + self._last_logstd = tf.Variable(tf.zeros_like(self._batch_env.action), + False, + name='last_logstd') + self._penalty = tf.Variable(self._config.kl_init_penalty, False, dtype=tf.float32) + self._policy_optimizer = self._config.policy_optimizer(self._config.policy_lr, + name='policy_optimizer') + self._value_optimizer = self._config.value_optimizer(self._config.value_lr, + name='value_optimizer') def begin_episode(self, agent_indices): """Reset the recurrent states and stored episode. @@ -118,23 +117,24 @@ class PPOAlgorithm(object): """ with tf.name_scope('perform/'): observ = self._observ_filter.transform(observ) - network = self._network( - observ[:, None], tf.ones(observ.shape[0]), self._last_state) - action = tf.cond( - self._is_training, network.policy.sample, lambda: network.mean) + network = self._network(observ[:, None], tf.ones(observ.shape[0]), self._last_state) + action = tf.cond(self._is_training, network.policy.sample, lambda: network.mean) logprob = network.policy.log_prob(action)[:, 0] # pylint: disable=g-long-lambda - summary = tf.cond(self._should_log, lambda: tf.summary.merge([ - tf.summary.histogram('mean', network.mean[:, 0]), - tf.summary.histogram('std', tf.exp(network.logstd[:, 0])), - tf.summary.histogram('action', action[:, 0]), - tf.summary.histogram('logprob', logprob)]), str) + summary = tf.cond( + self._should_log, lambda: tf.summary.merge([ + tf.summary.histogram('mean', network.mean[:, 0]), + tf.summary.histogram('std', tf.exp(network.logstd[:, 0])), + tf.summary.histogram('action', action[:, 0]), + tf.summary.histogram('logprob', logprob) + ]), str) # Remember current policy to append to memory in the experience callback. with tf.control_dependencies([ utility.assign_nested_vars(self._last_state, network.state), self._last_action.assign(action[:, 0]), self._last_mean.assign(network.mean[:, 0]), - self._last_logstd.assign(network.logstd[:, 0])]): + self._last_logstd.assign(network.logstd[:, 0]) + ]): return tf.check_numerics(action[:, 0], 'action'), tf.identity(summary) def experience(self, observ, action, reward, unused_done, unused_nextob): @@ -155,15 +155,14 @@ class PPOAlgorithm(object): Summary tensor. """ with tf.name_scope('experience/'): - return tf.cond( - self._is_training, - lambda: self._define_experience(observ, action, reward), str) + return tf.cond(self._is_training, lambda: self._define_experience(observ, action, reward), + str) def _define_experience(self, observ, action, reward): """Implement the branch of experience() entered during training.""" - update_filters = tf.summary.merge([ - self._observ_filter.update(observ), - self._reward_filter.update(reward)]) + update_filters = tf.summary.merge( + [self._observ_filter.update(observ), + self._reward_filter.update(reward)]) with tf.control_dependencies([update_filters]): if self._config.train_on_agent_action: # NOTE: Doesn't seem to change much. @@ -174,14 +173,16 @@ class PPOAlgorithm(object): norm_observ = self._observ_filter.transform(observ) norm_reward = tf.reduce_mean(self._reward_filter.transform(reward)) # pylint: disable=g-long-lambda - summary = tf.cond(self._should_log, lambda: tf.summary.merge([ - update_filters, - self._observ_filter.summary(), - self._reward_filter.summary(), - tf.summary.scalar('memory_size', self._memory_index), - tf.summary.histogram('normalized_observ', norm_observ), - tf.summary.histogram('action', self._last_action), - tf.summary.scalar('normalized_reward', norm_reward)]), str) + summary = tf.cond( + self._should_log, lambda: tf.summary.merge([ + update_filters, + self._observ_filter.summary(), + self._reward_filter.summary(), + tf.summary.scalar('memory_size', self._memory_index), + tf.summary.histogram('normalized_observ', norm_observ), + tf.summary.histogram('action', self._last_action), + tf.summary.scalar('normalized_reward', norm_reward) + ]), str) return summary def end_episode(self, agent_indices): @@ -199,20 +200,16 @@ class PPOAlgorithm(object): Summary tensor. """ with tf.name_scope('end_episode/'): - return tf.cond( - self._is_training, - lambda: self._define_end_episode(agent_indices), str) + return tf.cond(self._is_training, lambda: self._define_end_episode(agent_indices), str) def _define_end_episode(self, agent_indices): """Implement the branch of end_episode() entered during training.""" episodes, length = self._episodes.data(agent_indices) space_left = self._config.update_every - self._memory_index - use_episodes = tf.range(tf.minimum( - tf.shape(agent_indices)[0], space_left)) + use_episodes = tf.range(tf.minimum(tf.shape(agent_indices)[0], space_left)) episodes = [tf.gather(elem, use_episodes) for elem in episodes] - append = self._memory.replace( - episodes, tf.gather(length, use_episodes), - use_episodes + self._memory_index) + append = self._memory.replace(episodes, tf.gather(length, use_episodes), + use_episodes + self._memory_index) with tf.control_dependencies([append]): inc_index = self._memory_index.assign_add(tf.shape(use_episodes)[0]) with tf.control_dependencies([inc_index]): @@ -229,8 +226,7 @@ class PPOAlgorithm(object): Summary tensor. """ with tf.name_scope('training'): - assert_full = tf.assert_equal( - self._memory_index, self._config.update_every) + assert_full = tf.assert_equal(self._memory_index, self._config.update_every) with tf.control_dependencies([assert_full]): data = self._memory.data() (observ, action, old_mean, old_logstd, reward), length = data @@ -238,21 +234,17 @@ class PPOAlgorithm(object): length = tf.identity(length) observ = self._observ_filter.transform(observ) reward = self._reward_filter.transform(reward) - policy_summary = self._update_policy( - observ, action, old_mean, old_logstd, reward, length) + policy_summary = self._update_policy(observ, action, old_mean, old_logstd, reward, length) with tf.control_dependencies([policy_summary]): value_summary = self._update_value(observ, reward, length) with tf.control_dependencies([value_summary]): - penalty_summary = self._adjust_penalty( - observ, old_mean, old_logstd, length) + penalty_summary = self._adjust_penalty(observ, old_mean, old_logstd, length) with tf.control_dependencies([penalty_summary]): - clear_memory = tf.group( - self._memory.clear(), self._memory_index.assign(0)) + clear_memory = tf.group(self._memory.clear(), self._memory_index.assign(0)) with tf.control_dependencies([clear_memory]): - weight_summary = utility.variable_summaries( - tf.trainable_variables(), self._config.weight_summaries) - return tf.summary.merge([ - policy_summary, value_summary, penalty_summary, weight_summary]) + weight_summary = utility.variable_summaries(tf.trainable_variables(), + self._config.weight_summaries) + return tf.summary.merge([policy_summary, value_summary, penalty_summary, weight_summary]) def _update_value(self, observ, reward, length): """Perform multiple update steps of the value baseline. @@ -269,10 +261,9 @@ class PPOAlgorithm(object): Summary tensor. """ with tf.name_scope('update_value'): - loss, summary = tf.scan( - lambda _1, _2: self._update_value_step(observ, reward, length), - tf.range(self._config.update_epochs_value), - [0., ''], parallel_iterations=1) + loss, summary = tf.scan(lambda _1, _2: self._update_value_step(observ, reward, length), + tf.range(self._config.update_epochs_value), [0., ''], + parallel_iterations=1) print_loss = tf.Print(0, [tf.reduce_mean(loss)], 'value loss: ') with tf.control_dependencies([loss, print_loss]): return summary[self._config.update_epochs_value // 2] @@ -289,15 +280,13 @@ class PPOAlgorithm(object): Tuple of loss tensor and summary tensor. """ loss, summary = self._value_loss(observ, reward, length) - gradients, variables = ( - zip(*self._value_optimizer.compute_gradients(loss))) - optimize = self._value_optimizer.apply_gradients( - zip(gradients, variables)) + gradients, variables = (zip(*self._value_optimizer.compute_gradients(loss))) + optimize = self._value_optimizer.apply_gradients(zip(gradients, variables)) summary = tf.summary.merge([ summary, tf.summary.scalar('gradient_norm', tf.global_norm(gradients)), - utility.gradient_summaries( - zip(gradients, variables), dict(value=r'.*'))]) + utility.gradient_summaries(zip(gradients, variables), dict(value=r'.*')) + ]) with tf.control_dependencies([optimize]): return [tf.identity(loss), tf.identity(summary)] @@ -317,18 +306,17 @@ class PPOAlgorithm(object): """ with tf.name_scope('value_loss'): value = self._network(observ, length).value - return_ = utility.discounted_return( - reward, length, self._config.discount) + return_ = utility.discounted_return(reward, length, self._config.discount) advantage = return_ - value - value_loss = 0.5 * self._mask(advantage ** 2, length) + value_loss = 0.5 * self._mask(advantage**2, length) summary = tf.summary.merge([ tf.summary.histogram('value_loss', value_loss), - tf.summary.scalar('avg_value_loss', tf.reduce_mean(value_loss))]) + tf.summary.scalar('avg_value_loss', tf.reduce_mean(value_loss)) + ]) value_loss = tf.reduce_mean(value_loss) return tf.check_numerics(value_loss, 'value_loss'), summary - def _update_policy( - self, observ, action, old_mean, old_logstd, reward, length): + def _update_policy(self, observ, action, old_mean, old_logstd, reward, length): """Perform multiple update steps of the policy. The advantage is computed once at the beginning and shared across @@ -347,35 +335,28 @@ class PPOAlgorithm(object): Summary tensor. """ with tf.name_scope('update_policy'): - return_ = utility.discounted_return( - reward, length, self._config.discount) + return_ = utility.discounted_return(reward, length, self._config.discount) value = self._network(observ, length).value if self._config.gae_lambda: - advantage = utility.lambda_return( - reward, value, length, self._config.discount, - self._config.gae_lambda) + advantage = utility.lambda_return(reward, value, length, self._config.discount, + self._config.gae_lambda) else: advantage = return_ - value mean, variance = tf.nn.moments(advantage, axes=[0, 1], keep_dims=True) advantage = (advantage - mean) / (tf.sqrt(variance) + 1e-8) advantage = tf.Print( - advantage, [tf.reduce_mean(return_), tf.reduce_mean(value)], - 'return and value: ') - advantage = tf.Print( - advantage, [tf.reduce_mean(advantage)], - 'normalized advantage: ') + advantage, [tf.reduce_mean(return_), tf.reduce_mean(value)], 'return and value: ') + advantage = tf.Print(advantage, [tf.reduce_mean(advantage)], 'normalized advantage: ') # pylint: disable=g-long-lambda - loss, summary = tf.scan( - lambda _1, _2: self._update_policy_step( - observ, action, old_mean, old_logstd, advantage, length), - tf.range(self._config.update_epochs_policy), - [0., ''], parallel_iterations=1) + loss, summary = tf.scan(lambda _1, _2: self._update_policy_step( + observ, action, old_mean, old_logstd, advantage, length), + tf.range(self._config.update_epochs_policy), [0., ''], + parallel_iterations=1) print_loss = tf.Print(0, [tf.reduce_mean(loss)], 'policy loss: ') with tf.control_dependencies([loss, print_loss]): return summary[self._config.update_epochs_policy // 2] - def _update_policy_step( - self, observ, action, old_mean, old_logstd, advantage, length): + def _update_policy_step(self, observ, action, old_mean, old_logstd, advantage, length): """Compute the current policy loss and perform a gradient update step. Args: @@ -390,23 +371,19 @@ class PPOAlgorithm(object): Tuple of loss tensor and summary tensor. """ network = self._network(observ, length) - loss, summary = self._policy_loss( - network.mean, network.logstd, old_mean, old_logstd, action, - advantage, length) - gradients, variables = ( - zip(*self._policy_optimizer.compute_gradients(loss))) - optimize = self._policy_optimizer.apply_gradients( - zip(gradients, variables)) + loss, summary = self._policy_loss(network.mean, network.logstd, old_mean, old_logstd, action, + advantage, length) + gradients, variables = (zip(*self._policy_optimizer.compute_gradients(loss))) + optimize = self._policy_optimizer.apply_gradients(zip(gradients, variables)) summary = tf.summary.merge([ summary, tf.summary.scalar('gradient_norm', tf.global_norm(gradients)), - utility.gradient_summaries( - zip(gradients, variables), dict(policy=r'.*'))]) + utility.gradient_summaries(zip(gradients, variables), dict(policy=r'.*')) + ]) with tf.control_dependencies([optimize]): return [tf.identity(loss), tf.identity(summary)] - def _policy_loss( - self, mean, logstd, old_mean, old_logstd, action, advantage, length): + def _policy_loss(self, mean, logstd, old_mean, old_logstd, action, advantage, length): """Compute the policy loss composed of multiple components. 1. The policy gradient loss is importance sampled from the data-collecting @@ -430,24 +407,20 @@ class PPOAlgorithm(object): """ with tf.name_scope('policy_loss'): entropy = utility.diag_normal_entropy(mean, logstd) - kl = tf.reduce_mean(self._mask(utility.diag_normal_kl( - old_mean, old_logstd, mean, logstd), length), 1) + kl = tf.reduce_mean( + self._mask(utility.diag_normal_kl(old_mean, old_logstd, mean, logstd), length), 1) policy_gradient = tf.exp( utility.diag_normal_logpdf(mean, logstd, action) - utility.diag_normal_logpdf(old_mean, old_logstd, action)) - surrogate_loss = -tf.reduce_mean(self._mask( - policy_gradient * tf.stop_gradient(advantage), length), 1) + surrogate_loss = -tf.reduce_mean( + self._mask(policy_gradient * tf.stop_gradient(advantage), length), 1) kl_penalty = self._penalty * kl cutoff_threshold = self._config.kl_target * self._config.kl_cutoff_factor - cutoff_count = tf.reduce_sum( - tf.cast(kl > cutoff_threshold, tf.int32)) - with tf.control_dependencies([tf.cond( - cutoff_count > 0, - lambda: tf.Print(0, [cutoff_count], 'kl cutoff! '), int)]): - kl_cutoff = ( - self._config.kl_cutoff_coef * - tf.cast(kl > cutoff_threshold, tf.float32) * - (kl - cutoff_threshold) ** 2) + cutoff_count = tf.reduce_sum(tf.cast(kl > cutoff_threshold, tf.int32)) + with tf.control_dependencies( + [tf.cond(cutoff_count > 0, lambda: tf.Print(0, [cutoff_count], 'kl cutoff! '), int)]): + kl_cutoff = (self._config.kl_cutoff_coef * tf.cast(kl > cutoff_threshold, tf.float32) * + (kl - cutoff_threshold)**2) policy_loss = surrogate_loss + kl_penalty + kl_cutoff summary = tf.summary.merge([ tf.summary.histogram('entropy', entropy), @@ -459,7 +432,8 @@ class PPOAlgorithm(object): tf.summary.histogram('policy_loss', policy_loss), tf.summary.scalar('avg_surr_loss', tf.reduce_mean(surrogate_loss)), tf.summary.scalar('avg_kl_penalty', tf.reduce_mean(kl_penalty)), - tf.summary.scalar('avg_policy_loss', tf.reduce_mean(policy_loss))]) + tf.summary.scalar('avg_policy_loss', tf.reduce_mean(policy_loss)) + ]) policy_loss = tf.reduce_mean(policy_loss, 0) return tf.check_numerics(policy_loss, 'policy_loss'), summary @@ -481,30 +455,30 @@ class PPOAlgorithm(object): """ with tf.name_scope('adjust_penalty'): network = self._network(observ, length) - assert_change = tf.assert_equal( - tf.reduce_all(tf.equal(network.mean, old_mean)), False, - message='policy should change') + assert_change = tf.assert_equal(tf.reduce_all(tf.equal(network.mean, old_mean)), + False, + message='policy should change') print_penalty = tf.Print(0, [self._penalty], 'current penalty: ') with tf.control_dependencies([assert_change, print_penalty]): - kl_change = tf.reduce_mean(self._mask(utility.diag_normal_kl( - old_mean, old_logstd, network.mean, network.logstd), length)) + kl_change = tf.reduce_mean( + self._mask(utility.diag_normal_kl(old_mean, old_logstd, network.mean, network.logstd), + length)) kl_change = tf.Print(kl_change, [kl_change], 'kl change: ') maybe_increase = tf.cond( kl_change > 1.3 * self._config.kl_target, # pylint: disable=g-long-lambda - lambda: tf.Print(self._penalty.assign( - self._penalty * 1.5), [0], 'increase penalty '), + lambda: tf.Print(self._penalty.assign(self._penalty * 1.5), [0], 'increase penalty '), float) maybe_decrease = tf.cond( kl_change < 0.7 * self._config.kl_target, # pylint: disable=g-long-lambda - lambda: tf.Print(self._penalty.assign( - self._penalty / 1.5), [0], 'decrease penalty '), + lambda: tf.Print(self._penalty.assign(self._penalty / 1.5), [0], 'decrease penalty '), float) with tf.control_dependencies([maybe_increase, maybe_decrease]): return tf.summary.merge([ tf.summary.scalar('kl_change', kl_change), - tf.summary.scalar('penalty', self._penalty)]) + tf.summary.scalar('penalty', self._penalty) + ]) def _mask(self, tensor, length): """Set padding elements of a batch of sequences to zero. @@ -548,11 +522,14 @@ class PPOAlgorithm(object): with tf.device('/gpu:0' if use_gpu else '/cpu:0'): observ = tf.check_numerics(observ, 'observ') cell = self._config.network(self._batch_env.action.shape[1].value) - (mean, logstd, value), state = tf.nn.dynamic_rnn( - cell, observ, length, state, tf.float32, swap_memory=True) + (mean, logstd, value), state = tf.nn.dynamic_rnn(cell, + observ, + length, + state, + tf.float32, + swap_memory=True) mean = tf.check_numerics(mean, 'mean') logstd = tf.check_numerics(logstd, 'logstd') value = tf.check_numerics(value, 'value') - policy = tf.contrib.distributions.MultivariateNormalDiag( - mean, tf.exp(logstd)) + policy = tf.contrib.distributions.MultivariateNormalDiag(mean, tf.exp(logstd)) return _NetworkOutput(policy, mean, logstd, value, state) diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/agents/ppo/memory.py b/examples/pybullet/gym/pybullet_envs/minitaur/agents/ppo/memory.py index 81e0f73d7..37a7c7f53 100644 --- a/examples/pybullet/gym/pybullet_envs/minitaur/agents/ppo/memory.py +++ b/examples/pybullet/gym/pybullet_envs/minitaur/agents/ppo/memory.py @@ -11,7 +11,6 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. - """Memory that stores episodes.""" from __future__ import absolute_import @@ -43,10 +42,9 @@ class EpisodeMemory(object): self._scope = scope self._length = tf.Variable(tf.zeros(capacity, tf.int32), False) self._buffers = [ - tf.Variable(tf.zeros( - [capacity, max_length] + elem.shape.as_list(), - elem.dtype), False) - for elem in template] + tf.Variable(tf.zeros([capacity, max_length] + elem.shape.as_list(), elem.dtype), False) + for elem in template + ] def length(self, rows=None): """Tensor holding the current length of episodes. @@ -72,13 +70,11 @@ class EpisodeMemory(object): """ rows = tf.range(self._capacity) if rows is None else rows assert rows.shape.ndims == 1 - assert_capacity = tf.assert_less( - rows, self._capacity, - message='capacity exceeded') + assert_capacity = tf.assert_less(rows, self._capacity, message='capacity exceeded') with tf.control_dependencies([assert_capacity]): - assert_max_length = tf.assert_less( - tf.gather(self._length, rows), self._max_length, - message='max length exceeded') + assert_max_length = tf.assert_less(tf.gather(self._length, rows), + self._max_length, + message='max length exceeded') append_ops = [] with tf.control_dependencies([assert_max_length]): for buffer_, elements in zip(self._buffers, transitions): @@ -86,8 +82,7 @@ class EpisodeMemory(object): indices = tf.stack([rows, timestep], 1) append_ops.append(tf.scatter_nd_update(buffer_, indices, elements)) with tf.control_dependencies(append_ops): - episode_mask = tf.reduce_sum(tf.one_hot( - rows, self._capacity, dtype=tf.int32), 0) + episode_mask = tf.reduce_sum(tf.one_hot(rows, self._capacity, dtype=tf.int32), 0) return self._length.assign_add(episode_mask) def replace(self, episodes, length, rows=None): @@ -103,11 +98,11 @@ class EpisodeMemory(object): """ rows = tf.range(self._capacity) if rows is None else rows assert rows.shape.ndims == 1 - assert_capacity = tf.assert_less( - rows, self._capacity, message='capacity exceeded') + assert_capacity = tf.assert_less(rows, self._capacity, message='capacity exceeded') with tf.control_dependencies([assert_capacity]): - assert_max_length = tf.assert_less_equal( - length, self._max_length, message='max length exceeded') + assert_max_length = tf.assert_less_equal(length, + self._max_length, + message='max length exceeded') replace_ops = [] with tf.control_dependencies([assert_max_length]): for buffer_, elements in zip(self._buffers, episodes): diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/agents/ppo/normalize.py b/examples/pybullet/gym/pybullet_envs/minitaur/agents/ppo/normalize.py index 6c4170519..4de38fd25 100644 --- a/examples/pybullet/gym/pybullet_envs/minitaur/agents/ppo/normalize.py +++ b/examples/pybullet/gym/pybullet_envs/minitaur/agents/ppo/normalize.py @@ -11,7 +11,6 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. - """Normalize tensors based on streaming estimates of mean and variance.""" from __future__ import absolute_import @@ -24,8 +23,7 @@ import tensorflow as tf class StreamingNormalize(object): """Normalize tensors based on streaming estimates of mean and variance.""" - def __init__( - self, template, center=True, scale=True, clip=10, name='normalize'): + def __init__(self, template, center=True, scale=True, clip=10, name='normalize'): """Normalize tensors based on streaming estimates of mean and variance. Centering the value, scaling it by the standard deviation, and clipping @@ -69,8 +67,7 @@ class StreamingNormalize(object): if self._scale: # We cannot scale before seeing at least two samples. value /= tf.cond( - self._count > 1, lambda: self._std() + 1e-8, - lambda: tf.ones_like(self._var_sum))[None] + self._count > 1, lambda: self._std() + 1e-8, lambda: tf.ones_like(self._var_sum))[None] if self._clip: value = tf.clip_by_value(value, -self._clip, self._clip) # Remove batch dimension if necessary. @@ -97,8 +94,7 @@ class StreamingNormalize(object): mean_delta = tf.reduce_sum(value - self._mean[None, ...], 0) new_mean = self._mean + mean_delta / step new_mean = tf.cond(self._count > 1, lambda: new_mean, lambda: value[0]) - var_delta = ( - value - self._mean[None, ...]) * (value - new_mean[None, ...]) + var_delta = (value - self._mean[None, ...]) * (value - new_mean[None, ...]) new_var_sum = self._var_sum + tf.reduce_sum(var_delta, 0) with tf.control_dependencies([new_mean, new_var_sum]): update = self._mean.assign(new_mean), self._var_sum.assign(new_var_sum) @@ -116,10 +112,8 @@ class StreamingNormalize(object): Operation. """ with tf.name_scope(self._name + '/reset'): - return tf.group( - self._count.assign(0), - self._mean.assign(tf.zeros_like(self._mean)), - self._var_sum.assign(tf.zeros_like(self._var_sum))) + return tf.group(self._count.assign(0), self._mean.assign(tf.zeros_like(self._mean)), + self._var_sum.assign(tf.zeros_like(self._var_sum))) def summary(self): """Summary string of mean and standard deviation. @@ -128,10 +122,8 @@ class StreamingNormalize(object): Summary tensor. """ with tf.name_scope(self._name + '/summary'): - mean_summary = tf.cond( - self._count > 0, lambda: self._summary('mean', self._mean), str) - std_summary = tf.cond( - self._count > 1, lambda: self._summary('stddev', self._std()), str) + mean_summary = tf.cond(self._count > 0, lambda: self._summary('mean', self._mean), str) + std_summary = tf.cond(self._count > 1, lambda: self._summary('stddev', self._std()), str) return tf.summary.merge([mean_summary, std_summary]) def _std(self): @@ -143,10 +135,8 @@ class StreamingNormalize(object): Returns: Tensor of current variance. """ - variance = tf.cond( - self._count > 1, - lambda: self._var_sum / tf.cast(self._count - 1, tf.float32), - lambda: tf.ones_like(self._var_sum) * float('nan')) + variance = tf.cond(self._count > 1, lambda: self._var_sum / tf.cast( + self._count - 1, tf.float32), lambda: tf.ones_like(self._var_sum) * float('nan')) # The epsilon corrects for small negative variance values caused by # the algorithm. It was empirically chosen to work with all environments # tested. diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/agents/ppo/utility.py b/examples/pybullet/gym/pybullet_envs/minitaur/agents/ppo/utility.py index 79645b16b..9f6360404 100644 --- a/examples/pybullet/gym/pybullet_envs/minitaur/agents/ppo/utility.py +++ b/examples/pybullet/gym/pybullet_envs/minitaur/agents/ppo/utility.py @@ -11,7 +11,6 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. - """Utilities for the PPO algorithm.""" from __future__ import absolute_import @@ -51,8 +50,7 @@ def reinit_nested_vars(variables, indices=None): Operation. """ if isinstance(variables, (tuple, list)): - return tf.group(*[ - reinit_nested_vars(variable, indices) for variable in variables]) + return tf.group(*[reinit_nested_vars(variable, indices) for variable in variables]) if indices is None: return variables.assign(tf.zeros_like(variables)) else: @@ -71,9 +69,8 @@ def assign_nested_vars(variables, tensors): Operation. """ if isinstance(variables, (tuple, list)): - return tf.group(*[ - assign_nested_vars(variable, tensor) - for variable, tensor in zip(variables, tensors)]) + return tf.group( + *[assign_nested_vars(variable, tensor) for variable, tensor in zip(variables, tensors)]) return variables.assign(tensors) @@ -81,10 +78,11 @@ def discounted_return(reward, length, discount): """Discounted Monte-Carlo returns.""" timestep = tf.range(reward.shape[1].value) mask = tf.cast(timestep[None, :] < length[:, None], tf.float32) - return_ = tf.reverse(tf.transpose(tf.scan( - lambda agg, cur: cur + discount * agg, - tf.transpose(tf.reverse(mask * reward, [1]), [1, 0]), - tf.zeros_like(reward[:, -1]), 1, False), [1, 0]), [1]) + return_ = tf.reverse( + tf.transpose( + tf.scan(lambda agg, cur: cur + discount * agg, + tf.transpose(tf.reverse(mask * reward, [1]), [1, 0]), + tf.zeros_like(reward[:, -1]), 1, False), [1, 0]), [1]) return tf.check_numerics(tf.stop_gradient(return_), 'return') @@ -95,9 +93,8 @@ def fixed_step_return(reward, value, length, discount, window): return_ = tf.zeros_like(reward) for _ in range(window): return_ += reward - reward = discount * tf.concat( - [reward[:, 1:], tf.zeros_like(reward[:, -1:])], 1) - return_ += discount ** window * tf.concat( + reward = discount * tf.concat([reward[:, 1:], tf.zeros_like(reward[:, -1:])], 1) + return_ += discount**window * tf.concat( [value[:, window:], tf.zeros_like(value[:, -window:]), 1]) return tf.check_numerics(tf.stop_gradient(mask * return_), 'return') @@ -109,10 +106,11 @@ def lambda_return(reward, value, length, discount, lambda_): sequence = mask * reward + discount * value * (1 - lambda_) discount = mask * discount * lambda_ sequence = tf.stack([sequence, discount], 2) - return_ = tf.reverse(tf.transpose(tf.scan( - lambda agg, cur: cur[0] + cur[1] * agg, - tf.transpose(tf.reverse(sequence, [1]), [1, 2, 0]), - tf.zeros_like(value[:, -1]), 1, False), [1, 0]), [1]) + return_ = tf.reverse( + tf.transpose( + tf.scan(lambda agg, cur: cur[0] + cur[1] * agg, + tf.transpose(tf.reverse(sequence, [1]), [1, 2, 0]), tf.zeros_like(value[:, -1]), + 1, False), [1, 0]), [1]) return tf.check_numerics(tf.stop_gradient(return_), 'return') @@ -122,27 +120,26 @@ def lambda_advantage(reward, value, length, discount): mask = tf.cast(timestep[None, :] < length[:, None], tf.float32) next_value = tf.concat([value[:, 1:], tf.zeros_like(value[:, -1:])], 1) delta = reward + discount * next_value - value - advantage = tf.reverse(tf.transpose(tf.scan( - lambda agg, cur: cur + discount * agg, - tf.transpose(tf.reverse(mask * delta, [1]), [1, 0]), - tf.zeros_like(delta[:, -1]), 1, False), [1, 0]), [1]) + advantage = tf.reverse( + tf.transpose( + tf.scan(lambda agg, cur: cur + discount * agg, + tf.transpose(tf.reverse(mask * delta, [1]), [1, 0]), tf.zeros_like(delta[:, -1]), + 1, False), [1, 0]), [1]) return tf.check_numerics(tf.stop_gradient(advantage), 'advantage') def diag_normal_kl(mean0, logstd0, mean1, logstd1): """Epirical KL divergence of two normals with diagonal covariance.""" logstd0_2, logstd1_2 = 2 * logstd0, 2 * logstd1 - return 0.5 * ( - tf.reduce_sum(tf.exp(logstd0_2 - logstd1_2), -1) + - tf.reduce_sum((mean1 - mean0) ** 2 / tf.exp(logstd1_2), -1) + - tf.reduce_sum(logstd1_2, -1) - tf.reduce_sum(logstd0_2, -1) - - mean0.shape[-1].value) + return 0.5 * (tf.reduce_sum(tf.exp(logstd0_2 - logstd1_2), -1) + tf.reduce_sum( + (mean1 - mean0)**2 / tf.exp(logstd1_2), -1) + tf.reduce_sum(logstd1_2, -1) - + tf.reduce_sum(logstd0_2, -1) - mean0.shape[-1].value) def diag_normal_logpdf(mean, logstd, loc): """Log density of a normal with diagonal covariance.""" constant = -0.5 * (math.log(2 * math.pi) + logstd) - value = -0.5 * ((loc - mean) / tf.exp(logstd)) ** 2 + value = -0.5 * ((loc - mean) / tf.exp(logstd))**2 return tf.reduce_sum(constant + value, -1) diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/agents/scripts/__init__.py b/examples/pybullet/gym/pybullet_envs/minitaur/agents/scripts/__init__.py index 05ee82406..a3980d0f3 100644 --- a/examples/pybullet/gym/pybullet_envs/minitaur/agents/scripts/__init__.py +++ b/examples/pybullet/gym/pybullet_envs/minitaur/agents/scripts/__init__.py @@ -11,7 +11,6 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. - """Executable scripts for reinforcement learning.""" from __future__ import absolute_import diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/agents/scripts/configs.py b/examples/pybullet/gym/pybullet_envs/minitaur/agents/scripts/configs.py index 669f61827..1ea65948b 100644 --- a/examples/pybullet/gym/pybullet_envs/minitaur/agents/scripts/configs.py +++ b/examples/pybullet/gym/pybullet_envs/minitaur/agents/scripts/configs.py @@ -11,7 +11,6 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. - """Example configurations using the PPO algorithm.""" from __future__ import absolute_import @@ -33,10 +32,7 @@ def default(): use_gpu = False # Network network = networks.ForwardGaussianPolicy - weight_summaries = dict( - all=r'.*', - policy=r'.*/policy/.*', - value=r'.*/value/.*') + weight_summaries = dict(all=r'.*', policy=r'.*/policy/.*', value=r'.*/value/.*') policy_layers = 200, 100 value_layers = 200, 100 init_mean_factor = 0.05 diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/agents/scripts/networks.py b/examples/pybullet/gym/pybullet_envs/minitaur/agents/scripts/networks.py index fe46e6b8f..d2c22bd0f 100644 --- a/examples/pybullet/gym/pybullet_envs/minitaur/agents/scripts/networks.py +++ b/examples/pybullet/gym/pybullet_envs/minitaur/agents/scripts/networks.py @@ -11,7 +11,6 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. - """Networks for the PPO algorithm defined as recurrent cells.""" from __future__ import absolute_import @@ -20,11 +19,10 @@ from __future__ import print_function import tensorflow as tf - -_MEAN_WEIGHTS_INITIALIZER = tf.contrib.layers.variance_scaling_initializer( - factor=0.1) +_MEAN_WEIGHTS_INITIALIZER = tf.contrib.layers.variance_scaling_initializer(factor=0.1) _LOGSTD_INITIALIZER = tf.random_normal_initializer(-1, 1e-10) + class LinearGaussianPolicy(tf.contrib.rnn.RNNCell): """Indepent linear network with a tanh at the end for policy and feedforward network for the value. @@ -56,15 +54,12 @@ class LinearGaussianPolicy(tf.contrib.rnn.RNNCell): def __call__(self, observation, state): with tf.variable_scope('policy'): x = tf.contrib.layers.flatten(observation) - mean = tf.contrib.layers.fully_connected( - x, - self._action_size, - tf.tanh, - weights_initializer=self._mean_weights_initializer) - logstd = tf.get_variable('logstd', mean.shape[1:], tf.float32, - self._logstd_initializer) - logstd = tf.tile(logstd[None, ...], - [tf.shape(mean)[0]] + [1] * logstd.shape.ndims) + mean = tf.contrib.layers.fully_connected(x, + self._action_size, + tf.tanh, + weights_initializer=self._mean_weights_initializer) + logstd = tf.get_variable('logstd', mean.shape[1:], tf.float32, self._logstd_initializer) + logstd = tf.tile(logstd[None, ...], [tf.shape(mean)[0]] + [1] * logstd.shape.ndims) with tf.variable_scope('value'): x = tf.contrib.layers.flatten(observation) for size in self._value_layers: @@ -80,10 +75,12 @@ class ForwardGaussianPolicy(tf.contrib.rnn.RNNCell): is learned as independent parameter vector. """ - def __init__( - self, policy_layers, value_layers, action_size, - mean_weights_initializer=_MEAN_WEIGHTS_INITIALIZER, - logstd_initializer=_LOGSTD_INITIALIZER): + def __init__(self, + policy_layers, + value_layers, + action_size, + mean_weights_initializer=_MEAN_WEIGHTS_INITIALIZER, + logstd_initializer=_LOGSTD_INITIALIZER): self._policy_layers = policy_layers self._value_layers = value_layers self._action_size = action_size @@ -104,13 +101,12 @@ class ForwardGaussianPolicy(tf.contrib.rnn.RNNCell): x = tf.contrib.layers.flatten(observation) for size in self._policy_layers: x = tf.contrib.layers.fully_connected(x, size, tf.nn.relu) - mean = tf.contrib.layers.fully_connected( - x, self._action_size, tf.tanh, - weights_initializer=self._mean_weights_initializer) - logstd = tf.get_variable( - 'logstd', mean.shape[1:], tf.float32, self._logstd_initializer) - logstd = tf.tile( - logstd[None, ...], [tf.shape(mean)[0]] + [1] * logstd.shape.ndims) + mean = tf.contrib.layers.fully_connected(x, + self._action_size, + tf.tanh, + weights_initializer=self._mean_weights_initializer) + logstd = tf.get_variable('logstd', mean.shape[1:], tf.float32, self._logstd_initializer) + logstd = tf.tile(logstd[None, ...], [tf.shape(mean)[0]] + [1] * logstd.shape.ndims) with tf.variable_scope('value'): x = tf.contrib.layers.flatten(observation) for size in self._value_layers: @@ -127,10 +123,12 @@ class RecurrentGaussianPolicy(tf.contrib.rnn.RNNCell): and uses a GRU cell. """ - def __init__( - self, policy_layers, value_layers, action_size, - mean_weights_initializer=_MEAN_WEIGHTS_INITIALIZER, - logstd_initializer=_LOGSTD_INITIALIZER): + def __init__(self, + policy_layers, + value_layers, + action_size, + mean_weights_initializer=_MEAN_WEIGHTS_INITIALIZER, + logstd_initializer=_LOGSTD_INITIALIZER): self._policy_layers = policy_layers self._value_layers = value_layers self._action_size = action_size @@ -152,13 +150,12 @@ class RecurrentGaussianPolicy(tf.contrib.rnn.RNNCell): for size in self._policy_layers[:-1]: x = tf.contrib.layers.fully_connected(x, size, tf.nn.relu) x, state = self._cell(x, state) - mean = tf.contrib.layers.fully_connected( - x, self._action_size, tf.tanh, - weights_initializer=self._mean_weights_initializer) - logstd = tf.get_variable( - 'logstd', mean.shape[1:], tf.float32, self._logstd_initializer) - logstd = tf.tile( - logstd[None, ...], [tf.shape(mean)[0]] + [1] * logstd.shape.ndims) + mean = tf.contrib.layers.fully_connected(x, + self._action_size, + tf.tanh, + weights_initializer=self._mean_weights_initializer) + logstd = tf.get_variable('logstd', mean.shape[1:], tf.float32, self._logstd_initializer) + logstd = tf.tile(logstd[None, ...], [tf.shape(mean)[0]] + [1] * logstd.shape.ndims) with tf.variable_scope('value'): x = tf.contrib.layers.flatten(observation) for size in self._value_layers: diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/agents/scripts/train.py b/examples/pybullet/gym/pybullet_envs/minitaur/agents/scripts/train.py index e7048c2d9..99495b362 100644 --- a/examples/pybullet/gym/pybullet_envs/minitaur/agents/scripts/train.py +++ b/examples/pybullet/gym/pybullet_envs/minitaur/agents/scripts/train.py @@ -11,7 +11,6 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. - r"""Script to train a batch reinforcement learning algorithm. Command line: @@ -68,21 +67,25 @@ def _define_loop(graph, logdir, train_steps, eval_steps): Returns: Loop object. """ - loop = tools.Loop( - logdir, graph.step, graph.should_log, graph.do_report, - graph.force_reset) - loop.add_phase( - 'train', graph.done, graph.score, graph.summary, train_steps, - report_every=None, - log_every=train_steps // 2, - checkpoint_every=None, - feed={graph.is_training: True}) - loop.add_phase( - 'eval', graph.done, graph.score, graph.summary, eval_steps, - report_every=eval_steps, - log_every=eval_steps // 2, - checkpoint_every=10 * eval_steps, - feed={graph.is_training: False}) + loop = tools.Loop(logdir, graph.step, graph.should_log, graph.do_report, graph.force_reset) + loop.add_phase('train', + graph.done, + graph.score, + graph.summary, + train_steps, + report_every=None, + log_every=train_steps // 2, + checkpoint_every=None, + feed={graph.is_training: True}) + loop.add_phase('eval', + graph.done, + graph.score, + graph.summary, + eval_steps, + report_every=eval_steps, + log_every=eval_steps // 2, + checkpoint_every=10 * eval_steps, + feed={graph.is_training: False}) return loop @@ -101,25 +104,19 @@ def train(config, env_processes): """ tf.reset_default_graph() with config.unlocked: - config.network = functools.partial( - utility.define_network, config.network, config) + config.network = functools.partial(utility.define_network, config.network, config) config.policy_optimizer = getattr(tf.train, config.policy_optimizer) config.value_optimizer = getattr(tf.train, config.value_optimizer) if config.update_every % config.num_agents: tf.logging.warn('Number of agents should divide episodes per update.') with tf.device('/cpu:0'): - batch_env = utility.define_batch_env( - lambda: _create_environment(config), - config.num_agents, env_processes) - graph = utility.define_simulation_graph( - batch_env, config.algorithm, config) - loop = _define_loop( - graph, config.logdir, - config.update_every * config.max_length, - config.eval_episodes * config.max_length) - total_steps = int( - config.steps / config.update_every * - (config.update_every + config.eval_episodes)) + batch_env = utility.define_batch_env(lambda: _create_environment(config), config.num_agents, + env_processes) + graph = utility.define_simulation_graph(batch_env, config.algorithm, config) + loop = _define_loop(graph, config.logdir, config.update_every * config.max_length, + config.eval_episodes * config.max_length) + total_steps = int(config.steps / config.update_every * + (config.update_every + config.eval_episodes)) # Exclude episode related variables since the Python state of environments is # not checkpointed and thus new episodes start after resuming. saver = utility.define_saver(exclude=(r'.*_temporary/.*',)) @@ -137,8 +134,8 @@ def main(_): utility.set_up_logging() if not FLAGS.config: raise KeyError('You must specify a configuration.') - logdir = FLAGS.logdir and os.path.expanduser(os.path.join( - FLAGS.logdir, '{}-{}'.format(FLAGS.timestamp, FLAGS.config))) + logdir = FLAGS.logdir and os.path.expanduser( + os.path.join(FLAGS.logdir, '{}-{}'.format(FLAGS.timestamp, FLAGS.config))) try: config = utility.load_config(logdir) except IOError: @@ -150,16 +147,11 @@ def main(_): if __name__ == '__main__': FLAGS = tf.app.flags.FLAGS - tf.app.flags.DEFINE_string( - 'logdir', None, - 'Base directory to store logs.') - tf.app.flags.DEFINE_string( - 'timestamp', datetime.datetime.now().strftime('%Y%m%dT%H%M%S'), - 'Sub directory to store logs.') - tf.app.flags.DEFINE_string( - 'config', None, - 'Configuration to execute.') - tf.app.flags.DEFINE_boolean( - 'env_processes', True, - 'Step environments in separate processes to circumvent the GIL.') + tf.app.flags.DEFINE_string('logdir', None, 'Base directory to store logs.') + tf.app.flags.DEFINE_string('timestamp', + datetime.datetime.now().strftime('%Y%m%dT%H%M%S'), + 'Sub directory to store logs.') + tf.app.flags.DEFINE_string('config', None, 'Configuration to execute.') + tf.app.flags.DEFINE_boolean('env_processes', True, + 'Step environments in separate processes to circumvent the GIL.') tf.app.run() diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/agents/scripts/train_ppo_test.py b/examples/pybullet/gym/pybullet_envs/minitaur/agents/scripts/train_ppo_test.py index df5095825..930143c39 100644 --- a/examples/pybullet/gym/pybullet_envs/minitaur/agents/scripts/train_ppo_test.py +++ b/examples/pybullet/gym/pybullet_envs/minitaur/agents/scripts/train_ppo_test.py @@ -11,7 +11,6 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. - """Tests for the PPO algorithm usage example.""" from __future__ import absolute_import @@ -29,7 +28,6 @@ from google3.robotics.reinforcement_learning.agents.scripts import configs from google3.robotics.reinforcement_learning.agents.scripts import networks from google3.robotics.reinforcement_learning.agents.scripts import train - FLAGS = tf.app.flags.FLAGS @@ -65,9 +63,11 @@ class PPOTest(tf.test.TestCase): for network, observ_shape in itertools.product(nets, observ_shapes): config = self._define_config() with config.unlocked: - config.env = functools.partial( - tools.MockEnvironment, observ_shape, action_shape=(3,), - min_duration=15, max_duration=15) + config.env = functools.partial(tools.MockEnvironment, + observ_shape, + action_shape=(3,), + min_duration=15, + max_duration=15) config.max_length = 20 config.steps = 100 config.network = network @@ -77,9 +77,11 @@ class PPOTest(tf.test.TestCase): def test_no_crash_variable_duration(self): config = self._define_config() with config.unlocked: - config.env = functools.partial( - tools.MockEnvironment, observ_shape=(2, 3), action_shape=(3,), - min_duration=5, max_duration=25) + config.env = functools.partial(tools.MockEnvironment, + observ_shape=(2, 3), + action_shape=(3,), + min_duration=5, + max_duration=25) config.max_length = 25 config.steps = 200 config.network = networks.RecurrentGaussianPolicy diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/agents/scripts/utility.py b/examples/pybullet/gym/pybullet_envs/minitaur/agents/scripts/utility.py index 1342977c7..22bc50126 100644 --- a/examples/pybullet/gym/pybullet_envs/minitaur/agents/scripts/utility.py +++ b/examples/pybullet/gym/pybullet_envs/minitaur/agents/scripts/utility.py @@ -11,7 +11,6 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. - """Utilities for using reinforcement learning algorithms.""" from __future__ import absolute_import @@ -46,8 +45,7 @@ def define_simulation_graph(batch_env, algo_cls, config): do_report = tf.placeholder(tf.bool, name='do_report') force_reset = tf.placeholder(tf.bool, name='force_reset') algo = algo_cls(batch_env, step, is_training, should_log, config) - done, score, summary = tools.simulate( - batch_env, algo, should_log, force_reset) + done, score, summary = tools.simulate(batch_env, algo, should_log, force_reset) message = 'Graph contains {} trainable variables.' tf.logging.info(message.format(tools.count_weights())) # pylint: enable=unused-variable @@ -67,9 +65,7 @@ def define_batch_env(constructor, num_agents, env_processes): """ with tf.variable_scope('environments'): if env_processes: - envs = [ - tools.wrappers.ExternalProcess(constructor) - for _ in range(num_agents)] + envs = [tools.wrappers.ExternalProcess(constructor) for _ in range(num_agents)] else: envs = [constructor() for _ in range(num_agents)] batch_env = tools.BatchEnv(envs, blocking=not env_processes) @@ -108,15 +104,14 @@ def define_network(constructor, config, action_size): Returns: Created recurrent cell object. """ - mean_weights_initializer = ( - tf.contrib.layers.variance_scaling_initializer( - factor=config.init_mean_factor)) - logstd_initializer = tf.random_normal_initializer( - config.init_logstd, 1e-10) - network = constructor( - config.policy_layers, config.value_layers, action_size, - mean_weights_initializer=mean_weights_initializer, - logstd_initializer=logstd_initializer) + mean_weights_initializer = (tf.contrib.layers.variance_scaling_initializer( + factor=config.init_mean_factor)) + logstd_initializer = tf.random_normal_initializer(config.init_logstd, 1e-10) + network = constructor(config.policy_layers, + config.value_layers, + action_size, + mean_weights_initializer=mean_weights_initializer, + logstd_initializer=logstd_initializer) return network @@ -134,9 +129,7 @@ def initialize_variables(sess, saver, logdir, checkpoint=None, resume=None): ValueError: If resume expected but no log directory specified. RuntimeError: If no resume expected but a checkpoint was found. """ - sess.run(tf.group( - tf.local_variables_initializer(), - tf.global_variables_initializer())) + sess.run(tf.group(tf.local_variables_initializer(), tf.global_variables_initializer())) if resume and not (logdir or checkpoint): raise ValueError('Need to specify logdir to resume a checkpoint.') if logdir: @@ -175,9 +168,8 @@ def save_config(config, logdir=None): with tf.gfile.GFile(config_path, 'w') as file_: yaml.dump(config, file_, default_flow_style=False) else: - message = ( - 'Start a new run without storing summaries and checkpoints since no ' - 'logging directory was specified.') + message = ('Start a new run without storing summaries and checkpoints since no ' + 'logging directory was specified.') tf.logging.info(message) return config @@ -196,9 +188,8 @@ def load_config(logdir): """ config_path = logdir and os.path.join(logdir, 'config.yaml') if not config_path or not tf.gfile.Exists(config_path): - message = ( - 'Cannot resume an existing run since the logging directory does not ' - 'contain a configuration file.') + message = ('Cannot resume an existing run since the logging directory does not ' + 'contain a configuration file.') raise IOError(message) with tf.gfile.FastGFile(config_path, 'r') as file_: config = yaml.load(file_) diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/agents/scripts/visualize.py b/examples/pybullet/gym/pybullet_envs/minitaur/agents/scripts/visualize.py index 3a5f62e4b..cc68c0d42 100644 --- a/examples/pybullet/gym/pybullet_envs/minitaur/agents/scripts/visualize.py +++ b/examples/pybullet/gym/pybullet_envs/minitaur/agents/scripts/visualize.py @@ -11,7 +11,6 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. - r"""Script to render videos of the Proximal Policy Gradient algorithm. Command line: @@ -54,6 +53,8 @@ def _create_environment(config, outdir): setattr(env, 'spec', getattr(env, 'spec', None)) if config.max_length: env = tools.wrappers.LimitDuration(env, config.max_length) + + # env = gym.wrappers.Monitor( # env, outdir, lambda unused_episode_number: True) env = tools.wrappers.RangeNormalize(env) @@ -72,20 +73,20 @@ def _define_loop(graph, eval_steps): Returns: Loop object. """ - loop = tools.Loop( - None, graph.step, graph.should_log, graph.do_report, graph.force_reset) - loop.add_phase( - 'eval', graph.done, graph.score, graph.summary, eval_steps, - report_every=eval_steps, - log_every=None, - checkpoint_every=None, - feed={graph.is_training: False}) + loop = tools.Loop(None, graph.step, graph.should_log, graph.do_report, graph.force_reset) + loop.add_phase('eval', + graph.done, + graph.score, + graph.summary, + eval_steps, + report_every=eval_steps, + log_every=None, + checkpoint_every=None, + feed={graph.is_training: False}) return loop -def visualize( - logdir, outdir, num_agents, num_episodes, checkpoint=None, - env_processes=True): +def visualize(logdir, outdir, num_agents, num_episodes, checkpoint=None, env_processes=True): """Recover checkpoint and render videos from it. Args: @@ -98,25 +99,20 @@ def visualize( """ config = utility.load_config(logdir) with config.unlocked: - config.network = functools.partial( - utility.define_network, config.network, config) + config.network = functools.partial(utility.define_network, config.network, config) config.policy_optimizer = getattr(tf.train, config.policy_optimizer) config.value_optimizer = getattr(tf.train, config.value_optimizer) with tf.device('/cpu:0'): - batch_env = utility.define_batch_env( - lambda: _create_environment(config, outdir), - num_agents, env_processes) - graph = utility.define_simulation_graph( - batch_env, config.algorithm, config) + batch_env = utility.define_batch_env(lambda: _create_environment(config, outdir), num_agents, + env_processes) + graph = utility.define_simulation_graph(batch_env, config.algorithm, config) total_steps = num_episodes * config.max_length loop = _define_loop(graph, total_steps) - saver = utility.define_saver( - exclude=(r'.*_temporary/.*', r'global_step')) + saver = utility.define_saver(exclude=(r'.*_temporary/.*', r'global_step')) sess_config = tf.ConfigProto(allow_soft_placement=True) sess_config.gpu_options.allow_growth = True with tf.Session(config=sess_config) as sess: - utility.initialize_variables( - sess, saver, config.logdir, checkpoint, resume=True) + utility.initialize_variables(sess, saver, config.logdir, checkpoint, resume=True) for unused_score in loop.run(sess, saver, total_steps): pass batch_env.close() @@ -129,29 +125,18 @@ def main(_): raise KeyError('You must specify logging and outdirs directories.') FLAGS.logdir = os.path.expanduser(FLAGS.logdir) FLAGS.outdir = os.path.expanduser(FLAGS.outdir) - visualize( - FLAGS.logdir, FLAGS.outdir, FLAGS.num_agents, FLAGS.num_episodes, - FLAGS.checkpoint, FLAGS.env_processes) + visualize(FLAGS.logdir, FLAGS.outdir, FLAGS.num_agents, FLAGS.num_episodes, FLAGS.checkpoint, + FLAGS.env_processes) if __name__ == '__main__': FLAGS = tf.app.flags.FLAGS - tf.app.flags.DEFINE_string( - 'logdir', None, - 'Directory to the checkpoint of a training run.') - tf.app.flags.DEFINE_string( - 'outdir', None, - 'Local directory for storing the monitoring outdir.') - tf.app.flags.DEFINE_string( - 'checkpoint', None, - 'Checkpoint name to load; defaults to most recent.') - tf.app.flags.DEFINE_integer( - 'num_agents', 1, - 'How many environments to step in parallel.') - tf.app.flags.DEFINE_integer( - 'num_episodes', 5, - 'Minimum number of episodes to render.') - tf.app.flags.DEFINE_boolean( - 'env_processes', True, - 'Step environments in separate processes to circumvent the GIL.') + tf.app.flags.DEFINE_string('logdir', None, 'Directory to the checkpoint of a training run.') + tf.app.flags.DEFINE_string('outdir', None, 'Local directory for storing the monitoring outdir.') + tf.app.flags.DEFINE_string('checkpoint', None, + 'Checkpoint name to load; defaults to most recent.') + tf.app.flags.DEFINE_integer('num_agents', 1, 'How many environments to step in parallel.') + tf.app.flags.DEFINE_integer('num_episodes', 5, 'Minimum number of episodes to render.') + tf.app.flags.DEFINE_boolean('env_processes', True, + 'Step environments in separate processes to circumvent the GIL.') tf.app.run() diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/__init__.py b/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/__init__.py index d624c8243..f2f435c3e 100644 --- a/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/__init__.py +++ b/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/__init__.py @@ -11,7 +11,6 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. - """Tools for reinforcement learning.""" from __future__ import absolute_import diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/attr_dict.py b/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/attr_dict.py index 1707486d8..fbb08290f 100644 --- a/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/attr_dict.py +++ b/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/attr_dict.py @@ -11,7 +11,6 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. - """Wrap a dictionary to access keys as attributes.""" from __future__ import absolute_import diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/attr_dict_test.py b/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/attr_dict_test.py index f5b2e3ce4..7ab1f415f 100644 --- a/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/attr_dict_test.py +++ b/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/attr_dict_test.py @@ -11,7 +11,6 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. - """Tests for the attribute dictionary.""" from __future__ import absolute_import diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/batch_env.py b/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/batch_env.py index d103d0133..a2971bc4d 100644 --- a/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/batch_env.py +++ b/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/batch_env.py @@ -11,7 +11,6 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. - """Combine multiple environments to step them in batch.""" from __future__ import absolute_import @@ -84,13 +83,9 @@ class BatchEnv(object): message = 'Invalid action at index {}: {}' raise ValueError(message.format(index, action)) if self._blocking: - transitions = [ - env.step(action) - for env, action in zip(self._envs, actions)] + transitions = [env.step(action) for env, action in zip(self._envs, actions)] else: - transitions = [ - env.step(action, blocking=False) - for env, action in zip(self._envs, actions)] + transitions = [env.step(action, blocking=False) for env, action in zip(self._envs, actions)] transitions = [transition() for transition in transitions] observs, rewards, dones, infos = zip(*transitions) observ = np.stack(observs) diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/count_weights.py b/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/count_weights.py index dd0d870f6..279df82a0 100644 --- a/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/count_weights.py +++ b/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/count_weights.py @@ -11,7 +11,6 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. - """Count learnable parameters.""" from __future__ import absolute_import diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/count_weights_test.py b/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/count_weights_test.py index 930aa8623..a42c60f57 100644 --- a/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/count_weights_test.py +++ b/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/count_weights_test.py @@ -11,7 +11,6 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. - """Tests for the weight counting utility.""" from __future__ import absolute_import diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/in_graph_batch_env.py b/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/in_graph_batch_env.py index 2765d4821..6cd0a2fdf 100644 --- a/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/in_graph_batch_env.py +++ b/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/in_graph_batch_env.py @@ -11,7 +11,6 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. - """Batch of environments inside the TensorFlow graph.""" from __future__ import absolute_import @@ -42,18 +41,18 @@ class InGraphBatchEnv(object): action_shape = self._parse_shape(self._batch_env.action_space) action_dtype = self._parse_dtype(self._batch_env.action_space) with tf.variable_scope('env_temporary'): - self._observ = tf.Variable( - tf.zeros((len(self._batch_env),) + observ_shape, observ_dtype), - name='observ', trainable=False) - self._action = tf.Variable( - tf.zeros((len(self._batch_env),) + action_shape, action_dtype), - name='action', trainable=False) - self._reward = tf.Variable( - tf.zeros((len(self._batch_env),), tf.float32), - name='reward', trainable=False) - self._done = tf.Variable( - tf.cast(tf.ones((len(self._batch_env),)), tf.bool), - name='done', trainable=False) + self._observ = tf.Variable(tf.zeros((len(self._batch_env),) + observ_shape, observ_dtype), + name='observ', + trainable=False) + self._action = tf.Variable(tf.zeros((len(self._batch_env),) + action_shape, action_dtype), + name='action', + trainable=False) + self._reward = tf.Variable(tf.zeros((len(self._batch_env),), tf.float32), + name='reward', + trainable=False) + self._done = tf.Variable(tf.cast(tf.ones((len(self._batch_env),)), tf.bool), + name='done', + trainable=False) def __getattr__(self, name): """Forward unimplemented attributes to one of the original environments. @@ -89,16 +88,13 @@ class InGraphBatchEnv(object): if action.dtype in (tf.float16, tf.float32, tf.float64): action = tf.check_numerics(action, 'action') observ_dtype = self._parse_dtype(self._batch_env.observation_space) - observ, reward, done = tf.py_func( - lambda a: self._batch_env.step(a)[:3], [action], - [observ_dtype, tf.float32, tf.bool], name='step') + observ, reward, done = tf.py_func(lambda a: self._batch_env.step(a)[:3], [action], + [observ_dtype, tf.float32, tf.bool], + name='step') observ = tf.check_numerics(observ, 'observ') reward = tf.check_numerics(reward, 'reward') - return tf.group( - self._observ.assign(observ), - self._action.assign(action), - self._reward.assign(reward), - self._done.assign(done)) + return tf.group(self._observ.assign(observ), self._action.assign(action), + self._reward.assign(reward), self._done.assign(done)) def reset(self, indices=None): """Reset the batch of environments. @@ -112,15 +108,15 @@ class InGraphBatchEnv(object): if indices is None: indices = tf.range(len(self._batch_env)) observ_dtype = self._parse_dtype(self._batch_env.observation_space) - observ = tf.py_func( - self._batch_env.reset, [indices], observ_dtype, name='reset') + observ = tf.py_func(self._batch_env.reset, [indices], observ_dtype, name='reset') observ = tf.check_numerics(observ, 'observ') reward = tf.zeros_like(indices, tf.float32) done = tf.zeros_like(indices, tf.bool) with tf.control_dependencies([ tf.scatter_update(self._observ, indices, observ), tf.scatter_update(self._reward, indices, reward), - tf.scatter_update(self._done, indices, done)]): + tf.scatter_update(self._done, indices, done) + ]): return tf.identity(observ) @property diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/in_graph_env.py b/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/in_graph_env.py index 33ff31d07..6a71f4516 100644 --- a/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/in_graph_env.py +++ b/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/in_graph_env.py @@ -11,7 +11,6 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. - """Put an OpenAI Gym environment into the TensorFlow graph.""" from __future__ import absolute_import @@ -42,16 +41,15 @@ class InGraphEnv(object): action_shape = self._parse_shape(self._env.action_space) action_dtype = self._parse_dtype(self._env.action_space) with tf.name_scope('environment'): - self._observ = tf.Variable( - tf.zeros(observ_shape, observ_dtype), name='observ', trainable=False) - self._action = tf.Variable( - tf.zeros(action_shape, action_dtype), name='action', trainable=False) - self._reward = tf.Variable( - 0.0, dtype=tf.float32, name='reward', trainable=False) - self._done = tf.Variable( - True, dtype=tf.bool, name='done', trainable=False) - self._step = tf.Variable( - 0, dtype=tf.int32, name='step', trainable=False) + self._observ = tf.Variable(tf.zeros(observ_shape, observ_dtype), + name='observ', + trainable=False) + self._action = tf.Variable(tf.zeros(action_shape, action_dtype), + name='action', + trainable=False) + self._reward = tf.Variable(0.0, dtype=tf.float32, name='reward', trainable=False) + self._done = tf.Variable(True, dtype=tf.bool, name='done', trainable=False) + self._step = tf.Variable(0, dtype=tf.int32, name='step', trainable=False) def __getattr__(self, name): """Forward unimplemented attributes to the original environment. @@ -79,17 +77,14 @@ class InGraphEnv(object): if action.dtype in (tf.float16, tf.float32, tf.float64): action = tf.check_numerics(action, 'action') observ_dtype = self._parse_dtype(self._env.observation_space) - observ, reward, done = tf.py_func( - lambda a: self._env.step(a)[:3], [action], - [observ_dtype, tf.float32, tf.bool], name='step') + observ, reward, done = tf.py_func(lambda a: self._env.step(a)[:3], [action], + [observ_dtype, tf.float32, tf.bool], + name='step') observ = tf.check_numerics(observ, 'observ') reward = tf.check_numerics(reward, 'reward') - return tf.group( - self._observ.assign(observ), - self._action.assign(action), - self._reward.assign(reward), - self._done.assign(done), - self._step.assign_add(1)) + return tf.group(self._observ.assign(observ), self._action.assign(action), + self._reward.assign(reward), self._done.assign(done), + self._step.assign_add(1)) def reset(self): """Reset the environment. @@ -100,10 +95,10 @@ class InGraphEnv(object): observ_dtype = self._parse_dtype(self._env.observation_space) observ = tf.py_func(self._env.reset, [], observ_dtype, name='reset') observ = tf.check_numerics(observ, 'observ') - with tf.control_dependencies([ - self._observ.assign(observ), - self._reward.assign(0), - self._done.assign(False)]): + with tf.control_dependencies( + [self._observ.assign(observ), + self._reward.assign(0), + self._done.assign(False)]): return tf.identity(observ) @property diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/loop.py b/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/loop.py index 36f1e7faa..78a3cfd29 100644 --- a/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/loop.py +++ b/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/loop.py @@ -11,7 +11,6 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. - """Execute operations in a loop and coordinate logging and checkpoints.""" from __future__ import absolute_import @@ -25,10 +24,8 @@ import tensorflow as tf from pybullet_envs.minitaur.agents.tools import streaming_mean - _Phase = collections.namedtuple( - 'Phase', - 'name, writer, op, batch, steps, feed, report_every, log_every,' + 'Phase', 'name, writer, op, batch, steps, feed, report_every, log_every,' 'checkpoint_every') @@ -56,16 +53,22 @@ class Loop(object): reset: Tensor indicating to the model to start a new computation. """ self._logdir = logdir - self._step = ( - tf.Variable(0, False, name='global_step') if step is None else step) + self._step = (tf.Variable(0, False, name='global_step') if step is None else step) self._log = tf.placeholder(tf.bool) if log is None else log self._report = tf.placeholder(tf.bool) if report is None else report self._reset = tf.placeholder(tf.bool) if reset is None else reset self._phases = [] - def add_phase( - self, name, done, score, summary, steps, - report_every=None, log_every=None, checkpoint_every=None, feed=None): + def add_phase(self, + name, + done, + score, + summary, + steps, + report_every=None, + log_every=None, + checkpoint_every=None, + feed=None): """Add a phase to the loop protocol. If the model breaks long computation into multiple steps, the done tensor @@ -97,13 +100,12 @@ class Loop(object): if done.shape.ndims is None or score.shape.ndims is None: raise ValueError("Rank of 'done' and 'score' tensors must be known.") writer = self._logdir and tf.summary.FileWriter( - os.path.join(self._logdir, name), tf.get_default_graph(), - flush_secs=60) + os.path.join(self._logdir, name), tf.get_default_graph(), flush_secs=60) op = self._define_step(done, score, summary) batch = 1 if score.shape.ndims == 0 else score.shape[0].value - self._phases.append(_Phase( - name, writer, op, batch, int(steps), feed, report_every, - log_every, checkpoint_every)) + self._phases.append( + _Phase(name, writer, op, batch, int(steps), feed, report_every, log_every, + checkpoint_every)) def run(self, sess, saver, max_step=None): """Run the loop schedule for a specified number of steps. @@ -133,13 +135,11 @@ class Loop(object): tf.logging.info(message.format(phase.name, phase_step, global_step)) # Populate book keeping tensors. phase.feed[self._reset] = (steps_in < steps_made) - phase.feed[self._log] = ( - phase.writer and - self._is_every_steps(phase_step, phase.batch, phase.log_every)) - phase.feed[self._report] = ( - self._is_every_steps(phase_step, phase.batch, phase.report_every)) - summary, mean_score, global_step, steps_made = sess.run( - phase.op, phase.feed) + phase.feed[self._log] = (phase.writer and + self._is_every_steps(phase_step, phase.batch, phase.log_every)) + phase.feed[self._report] = (self._is_every_steps(phase_step, phase.batch, + phase.report_every)) + summary, mean_score, global_step, steps_made = sess.run(phase.op, phase.feed) if self._is_every_steps(phase_step, phase.batch, phase.checkpoint_every): self._store_checkpoint(sess, saver, global_step) if self._is_every_steps(phase_step, phase.batch, phase.report_every): @@ -207,8 +207,7 @@ class Loop(object): score_mean = streaming_mean.StreamingMean((), tf.float32) with tf.control_dependencies([done, score, summary]): done_score = tf.gather(score, tf.where(done)[:, 0]) - submit_score = tf.cond( - tf.reduce_any(done), lambda: score_mean.submit(done_score), tf.no_op) + submit_score = tf.cond(tf.reduce_any(done), lambda: score_mean.submit(done_score), tf.no_op) with tf.control_dependencies([submit_score]): mean_score = tf.cond(self._report, score_mean.clear, float) steps_made = tf.shape(score)[0] diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/loop_test.py b/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/loop_test.py index d4d03c513..3245d26b7 100644 --- a/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/loop_test.py +++ b/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/loop_test.py @@ -11,7 +11,6 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. - """Tests for the training loop.""" from __future__ import absolute_import @@ -28,8 +27,7 @@ class LoopTest(tf.test.TestCase): def test_report_every_step(self): step = tf.Variable(0, False, dtype=tf.int32, name='step') loop = tools.Loop(None, step) - loop.add_phase( - 'phase_1', done=True, score=0, summary='', steps=1, report_every=3) + loop.add_phase('phase_1', done=True, score=0, summary='', steps=1, report_every=3) # Step: 0 1 2 3 4 5 6 7 8 # Report: x x x with self.test_session() as sess: @@ -45,15 +43,33 @@ class LoopTest(tf.test.TestCase): def test_phases_feed(self): score = tf.placeholder(tf.float32, []) loop = tools.Loop(None) - loop.add_phase( - 'phase_1', done=True, score=score, summary='', steps=1, report_every=1, - log_every=None, checkpoint_every=None, feed={score: 1}) - loop.add_phase( - 'phase_2', done=True, score=score, summary='', steps=3, report_every=1, - log_every=None, checkpoint_every=None, feed={score: 2}) - loop.add_phase( - 'phase_3', done=True, score=score, summary='', steps=2, report_every=1, - log_every=None, checkpoint_every=None, feed={score: 3}) + loop.add_phase('phase_1', + done=True, + score=score, + summary='', + steps=1, + report_every=1, + log_every=None, + checkpoint_every=None, + feed={score: 1}) + loop.add_phase('phase_2', + done=True, + score=score, + summary='', + steps=3, + report_every=1, + log_every=None, + checkpoint_every=None, + feed={score: 2}) + loop.add_phase('phase_3', + done=True, + score=score, + summary='', + steps=2, + report_every=1, + log_every=None, + checkpoint_every=None, + feed={score: 3}) with self.test_session() as sess: sess.run(tf.global_variables_initializer()) scores = list(loop.run(sess, saver=None, max_step=15)) @@ -61,10 +77,8 @@ class LoopTest(tf.test.TestCase): def test_average_score_over_phases(self): loop = tools.Loop(None) - loop.add_phase( - 'phase_1', done=True, score=1, summary='', steps=1, report_every=2) - loop.add_phase( - 'phase_2', done=True, score=2, summary='', steps=2, report_every=5) + loop.add_phase('phase_1', done=True, score=1, summary='', steps=1, report_every=2) + loop.add_phase('phase_2', done=True, score=2, summary='', steps=2, report_every=5) # Score: 1 2 2 1 2 2 1 2 2 1 2 2 1 2 2 1 2 # Report 1: x x x # Report 2: x x @@ -78,8 +92,7 @@ class LoopTest(tf.test.TestCase): done = tf.equal((step + 1) % 2, 0) score = tf.cast(step, tf.float32) loop = tools.Loop(None, step) - loop.add_phase( - 'phase_1', done, score, summary='', steps=1, report_every=3) + loop.add_phase('phase_1', done, score, summary='', steps=1, report_every=3) # Score: 0 1 2 3 4 5 6 7 8 # Done: x x x x # Report: x x x @@ -91,10 +104,9 @@ class LoopTest(tf.test.TestCase): def test_not_done_batch(self): step = tf.Variable(0, False, dtype=tf.int32, name='step') done = tf.equal([step % 3, step % 4], 0) - score = tf.cast([step, step ** 2], tf.float32) + score = tf.cast([step, step**2], tf.float32) loop = tools.Loop(None, step) - loop.add_phase( - 'phase_1', done, score, summary='', steps=1, report_every=8) + loop.add_phase('phase_1', done, score, summary='', steps=1, report_every=8) # Step: 0 2 4 6 # Score 1: 0 2 4 6 # Done 1: x x diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/mock_algorithm.py b/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/mock_algorithm.py index 1bc58a906..e321afae3 100644 --- a/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/mock_algorithm.py +++ b/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/mock_algorithm.py @@ -11,7 +11,6 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. - """Mock algorithm for testing reinforcement learning code.""" from __future__ import absolute_import diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/mock_environment.py b/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/mock_environment.py index 248f515b1..9db14d56c 100644 --- a/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/mock_environment.py +++ b/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/mock_environment.py @@ -11,7 +11,6 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. - """Mock environment for testing reinforcement learning code.""" from __future__ import absolute_import diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/simulate.py b/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/simulate.py index 45b36d6b0..e794f6a9d 100644 --- a/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/simulate.py +++ b/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/simulate.py @@ -11,7 +11,6 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. - """In-graph simulation step of a vecrotized algorithm with environments.""" from __future__ import absolute_import @@ -55,7 +54,8 @@ def simulate(batch_env, algo, log=True, reset=False): reset_ops = [ batch_env.reset(agent_indices), tf.scatter_update(score, agent_indices, zero_scores), - tf.scatter_update(length, agent_indices, zero_durations)] + tf.scatter_update(length, agent_indices, zero_durations) + ] with tf.control_dependencies(reset_ops): return algo.begin_episode(agent_indices) @@ -76,9 +76,8 @@ def simulate(batch_env, algo, log=True, reset=False): add_score = score.assign_add(batch_env.reward) inc_length = length.assign_add(tf.ones(len(batch_env), tf.int32)) with tf.control_dependencies([add_score, inc_length]): - experience_summary = algo.experience( - prevob, batch_env.action, batch_env.reward, batch_env.done, - batch_env.observ) + experience_summary = algo.experience(prevob, batch_env.action, batch_env.reward, + batch_env.done, batch_env.observ) return tf.summary.merge([step_summary, experience_summary]) def _define_end_episode(agent_indices): @@ -94,8 +93,7 @@ def simulate(batch_env, algo, log=True, reset=False): """ assert agent_indices.shape.ndims == 1 submit_score = mean_score.submit(tf.gather(score, agent_indices)) - submit_length = mean_length.submit( - tf.cast(tf.gather(length, agent_indices), tf.float32)) + submit_length = mean_length.submit(tf.cast(tf.gather(length, agent_indices), tf.float32)) with tf.control_dependencies([submit_score, submit_length]): return algo.end_episode(agent_indices) @@ -105,41 +103,34 @@ def simulate(batch_env, algo, log=True, reset=False): Returns: Summary string. """ - score_summary = tf.cond( - tf.logical_and(log, tf.cast(mean_score.count, tf.bool)), - lambda: tf.summary.scalar('mean_score', mean_score.clear()), str) - length_summary = tf.cond( - tf.logical_and(log, tf.cast(mean_length.count, tf.bool)), - lambda: tf.summary.scalar('mean_length', mean_length.clear()), str) + score_summary = tf.cond(tf.logical_and(log, tf.cast( + mean_score.count, tf.bool)), lambda: tf.summary.scalar('mean_score', mean_score.clear()), + str) + length_summary = tf.cond(tf.logical_and( + log, tf.cast(mean_length.count, + tf.bool)), lambda: tf.summary.scalar('mean_length', mean_length.clear()), str) return tf.summary.merge([score_summary, length_summary]) with tf.name_scope('simulate'): log = tf.convert_to_tensor(log) reset = tf.convert_to_tensor(reset) with tf.variable_scope('simulate_temporary'): - score = tf.Variable( - tf.zeros(len(batch_env), dtype=tf.float32), False, name='score') - length = tf.Variable( - tf.zeros(len(batch_env), dtype=tf.int32), False, name='length') + score = tf.Variable(tf.zeros(len(batch_env), dtype=tf.float32), False, name='score') + length = tf.Variable(tf.zeros(len(batch_env), dtype=tf.int32), False, name='length') mean_score = streaming_mean.StreamingMean((), tf.float32) mean_length = streaming_mean.StreamingMean((), tf.float32) - agent_indices = tf.cond( - reset, - lambda: tf.range(len(batch_env)), - lambda: tf.cast(tf.where(batch_env.done)[:, 0], tf.int32)) - begin_episode = tf.cond( - tf.cast(tf.shape(agent_indices)[0], tf.bool), - lambda: _define_begin_episode(agent_indices), str) + agent_indices = tf.cond(reset, lambda: tf.range(len(batch_env)), lambda: tf.cast( + tf.where(batch_env.done)[:, 0], tf.int32)) + begin_episode = tf.cond(tf.cast(tf.shape(agent_indices)[0], + tf.bool), lambda: _define_begin_episode(agent_indices), str) with tf.control_dependencies([begin_episode]): step = _define_step() with tf.control_dependencies([step]): agent_indices = tf.cast(tf.where(batch_env.done)[:, 0], tf.int32) - end_episode = tf.cond( - tf.cast(tf.shape(agent_indices)[0], tf.bool), - lambda: _define_end_episode(agent_indices), str) + end_episode = tf.cond(tf.cast(tf.shape(agent_indices)[0], + tf.bool), lambda: _define_end_episode(agent_indices), str) with tf.control_dependencies([end_episode]): - summary = tf.summary.merge([ - _define_summaries(), begin_episode, step, end_episode]) + summary = tf.summary.merge([_define_summaries(), begin_episode, step, end_episode]) with tf.control_dependencies([summary]): done, score = tf.identity(batch_env.done), tf.identity(score) return done, score, summary diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/simulate_test.py b/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/simulate_test.py index 590010fec..42b45bc7f 100644 --- a/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/simulate_test.py +++ b/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/simulate_test.py @@ -11,7 +11,6 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. - """Tests for the simulation operation.""" from __future__ import absolute_import @@ -84,9 +83,10 @@ class SimulateTest(tf.test.TestCase): def _create_test_batch_env(self, durations): envs = [] for duration in durations: - env = tools.MockEnvironment( - observ_shape=(2, 3), action_shape=(3,), - min_duration=duration, max_duration=duration) + env = tools.MockEnvironment(observ_shape=(2, 3), + action_shape=(3,), + min_duration=duration, + max_duration=duration) env = tools.wrappers.ConvertTo32Bit(env) envs.append(env) batch_env = tools.BatchEnv(envs, blocking=True) diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/streaming_mean.py b/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/streaming_mean.py index 3f620fe37..5992eaa6b 100644 --- a/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/streaming_mean.py +++ b/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/streaming_mean.py @@ -11,7 +11,6 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. - """Compute a streaming estimation of the mean of submitted tensors.""" from __future__ import absolute_import @@ -53,9 +52,8 @@ class StreamingMean(object): # Add a batch dimension if necessary. if value.shape.ndims == self._sum.shape.ndims: value = value[None, ...] - return tf.group( - self._sum.assign_add(tf.reduce_sum(value, 0)), - self._count.assign_add(tf.shape(value)[0])) + return tf.group(self._sum.assign_add(tf.reduce_sum(value, 0)), + self._count.assign_add(tf.shape(value)[0])) def clear(self): """Return the mean estimate and reset the streaming statistics.""" diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/wrappers.py b/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/wrappers.py index 5cac88fdf..2e33f3942 100644 --- a/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/wrappers.py +++ b/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/wrappers.py @@ -11,7 +11,6 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. - """Wrappers for OpenAI Gym environments.""" from __future__ import absolute_import @@ -150,8 +149,7 @@ class FrameHistory(object): return self._select_frames() def _select_frames(self): - indices = [ - (self._step - index) % self._capacity for index in self._past_indices] + indices = [(self._step - index) % self._capacity for index in self._past_indices] observ = self._buffer[indices] if self._flatten: observ = np.reshape(observ, (-1,) + observ.shape[2:]) @@ -192,14 +190,14 @@ class RangeNormalize(object): def __init__(self, env, observ=None, action=None): self._env = env - self._should_normalize_observ = ( - observ is not False and self._is_finite(self._env.observation_space)) + self._should_normalize_observ = (observ is not False and + self._is_finite(self._env.observation_space)) if observ is True and not self._should_normalize_observ: raise ValueError('Cannot normalize infinite observation range.') if observ is None and not self._should_normalize_observ: tf.logging.info('Not normalizing infinite observation range.') - self._should_normalize_action = ( - action is not False and self._is_finite(self._env.action_space)) + self._should_normalize_action = (action is not False and + self._is_finite(self._env.action_space)) if action is True and not self._should_normalize_action: raise ValueError('Cannot normalize infinite action range.') if action is None and not self._should_normalize_action: @@ -327,8 +325,7 @@ class ExternalProcess(object): action_space: The cached action space of the environment. """ self._conn, conn = multiprocessing.Pipe() - self._process = multiprocessing.Process( - target=self._worker, args=(constructor, conn)) + self._process = multiprocessing.Process(target=self._worker, args=(constructor, conn)) atexit.register(self.close) self._process.start() self._observ_space = None diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/wrappers_test.py b/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/wrappers_test.py index dfc6fb263..fac8dd0d4 100644 --- a/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/wrappers_test.py +++ b/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/wrappers_test.py @@ -11,7 +11,6 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. - """Tests for environment wrappers.""" from __future__ import absolute_import @@ -28,18 +27,20 @@ from agents import tools class ExternalProcessTest(tf.test.TestCase): def test_close_no_hang_after_init(self): - constructor = functools.partial( - tools.MockEnvironment, - observ_shape=(2, 3), action_shape=(2,), - min_duration=2, max_duration=2) + constructor = functools.partial(tools.MockEnvironment, + observ_shape=(2, 3), + action_shape=(2,), + min_duration=2, + max_duration=2) env = tools.wrappers.ExternalProcess(constructor) env.close() def test_close_no_hang_after_step(self): - constructor = functools.partial( - tools.MockEnvironment, - observ_shape=(2, 3), action_shape=(2,), - min_duration=5, max_duration=5) + constructor = functools.partial(tools.MockEnvironment, + observ_shape=(2, 3), + action_shape=(2,), + min_duration=5, + max_duration=5) env = tools.wrappers.ExternalProcess(constructor) env.reset() env.step(env.action_space.sample()) @@ -53,8 +54,7 @@ class ExternalProcessTest(tf.test.TestCase): env.step(env.action_space.sample()) def test_reraise_exception_in_step(self): - constructor = functools.partial( - MockEnvironmentCrashInStep, crash_at_step=3) + constructor = functools.partial(MockEnvironmentCrashInStep, crash_at_step=3) env = tools.wrappers.ExternalProcess(constructor) env.reset() env.step(env.action_space.sample()) @@ -74,9 +74,10 @@ class MockEnvironmentCrashInStep(tools.MockEnvironment): """Raise an error after specified number of steps in an episode.""" def __init__(self, crash_at_step): - super(MockEnvironmentCrashInStep, self).__init__( - observ_shape=(2, 3), action_shape=(2,), - min_duration=crash_at_step + 1, max_duration=crash_at_step + 1) + super(MockEnvironmentCrashInStep, self).__init__(observ_shape=(2, 3), + action_shape=(2,), + min_duration=crash_at_step + 1, + max_duration=crash_at_step + 1) self._crash_at_step = crash_at_step def step(self, *args, **kwargs): diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs/__init__.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs/__init__.py index 37def777d..72c619547 100644 --- a/examples/pybullet/gym/pybullet_envs/minitaur/envs/__init__.py +++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs/__init__.py @@ -5,6 +5,3 @@ from pybullet_envs.minitaur.envs.minitaur_reactive_env import MinitaurReactiveEn from pybullet_envs.minitaur.envs.minitaur_stand_gym_env import MinitaurStandGymEnv from pybullet_envs.minitaur.envs.minitaur_trotting_env import MinitaurTrottingEnv from pybullet_envs.minitaur.envs.minitaur_four_leg_stand_env import MinitaurFourLegStandEnv - - - diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs/bullet_client.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs/bullet_client.py index 8fc1fcd5c..54e0bd09e 100644 --- a/examples/pybullet/gym/pybullet_envs/minitaur/envs/bullet_client.py +++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs/bullet_client.py @@ -42,9 +42,13 @@ class BulletClient(object): attribute = getattr(pybullet, name) if inspect.isbuiltin(attribute): if name not in [ - "invertTransform", "multiplyTransforms", "getMatrixFromQuaternion", - "getEulerFromQuaternion", "computeViewMatrixFromYawPitchRoll", - "computeProjectionMatrixFOV", "getQuaternionFromEuler", + "invertTransform", + "multiplyTransforms", + "getMatrixFromQuaternion", + "getEulerFromQuaternion", + "computeViewMatrixFromYawPitchRoll", + "computeProjectionMatrixFOV", + "getQuaternionFromEuler", ]: # A temporary hack for now. attribute = functools.partial(attribute, physicsClientId=self._client) return attribute diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs/env_randomizers/minitaur_alternating_legs_env_randomizer.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs/env_randomizers/minitaur_alternating_legs_env_randomizer.py index a2d1645f4..9f868ba60 100644 --- a/examples/pybullet/gym/pybullet_envs/minitaur/envs/env_randomizers/minitaur_alternating_legs_env_randomizer.py +++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs/env_randomizers/minitaur_alternating_legs_env_randomizer.py @@ -4,11 +4,11 @@ The randomization include swing_offset, extension_offset of all legs that mimics bent legs, desired_pitch from user input, battery voltage and motor damping. """ -import os, inspect +import os, inspect currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe()))) parentdir = os.path.dirname(os.path.dirname(currentdir)) parentdir = os.path.dirname(os.path.dirname(parentdir)) -os.sys.path.insert(0,parentdir) +os.sys.path.insert(0, parentdir) import numpy as np import tensorflow as tf @@ -20,8 +20,7 @@ BATTERY_VOLTAGE_RANGE = (14.8, 16.8) MOTOR_VISCOUS_DAMPING_RANGE = (0, 0.01) -class MinitaurAlternatingLegsEnvRandomizer( - env_randomizer_base.EnvRandomizerBase): +class MinitaurAlternatingLegsEnvRandomizer(env_randomizer_base.EnvRandomizerBase): """A randomizer that changes the minitaur_gym_alternating_leg_env.""" def __init__(self, @@ -34,23 +33,20 @@ class MinitaurAlternatingLegsEnvRandomizer( self.perturb_desired_pitch_bound = perturb_desired_pitch_bound def randomize_env(self, env): - perturb_magnitude = np.random.uniform( - low=-self.perturb_swing_bound, - high=self.perturb_swing_bound, - size=NUM_LEGS) + perturb_magnitude = np.random.uniform(low=-self.perturb_swing_bound, + high=self.perturb_swing_bound, + size=NUM_LEGS) env.set_swing_offset(perturb_magnitude) tf.logging.info("swing_offset: {}".format(perturb_magnitude)) - perturb_magnitude = np.random.uniform( - low=-self.perturb_extension_bound, - high=self.perturb_extension_bound, - size=NUM_LEGS) + perturb_magnitude = np.random.uniform(low=-self.perturb_extension_bound, + high=self.perturb_extension_bound, + size=NUM_LEGS) env.set_extension_offset(perturb_magnitude) tf.logging.info("extension_offset: {}".format(perturb_magnitude)) - perturb_magnitude = np.random.uniform( - low=-self.perturb_desired_pitch_bound, - high=self.perturb_desired_pitch_bound) + perturb_magnitude = np.random.uniform(low=-self.perturb_desired_pitch_bound, + high=self.perturb_desired_pitch_bound) env.set_desired_pitch(perturb_magnitude) tf.logging.info("desired_pitch: {}".format(perturb_magnitude)) diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs/env_randomizers/minitaur_env_randomizer.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs/env_randomizers/minitaur_env_randomizer.py index befdf7ea1..0fbfef36d 100644 --- a/examples/pybullet/gym/pybullet_envs/minitaur/envs/env_randomizers/minitaur_env_randomizer.py +++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs/env_randomizers/minitaur_env_randomizer.py @@ -1,11 +1,11 @@ """Randomize the minitaur_gym_env when reset() is called.""" import random -import os, inspect +import os, inspect currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe()))) parentdir = os.path.dirname(os.path.dirname(currentdir)) parentdir = os.path.dirname(os.path.dirname(parentdir)) -os.sys.path.insert(0,parentdir) +os.sys.path.insert(0, parentdir) import numpy as np from pybullet_envs.minitaur.envs import env_randomizer_base @@ -52,24 +52,20 @@ class MinitaurEnvRandomizer(env_randomizer_base.EnvRandomizerBase): minitaur.SetBaseMasses(randomized_base_mass) leg_masses = minitaur.GetLegMassesFromURDF() - leg_masses_lower_bound = np.array(leg_masses) * ( - 1.0 + self._minitaur_leg_mass_err_range[0]) - leg_masses_upper_bound = np.array(leg_masses) * ( - 1.0 + self._minitaur_leg_mass_err_range[1]) + leg_masses_lower_bound = np.array(leg_masses) * (1.0 + self._minitaur_leg_mass_err_range[0]) + leg_masses_upper_bound = np.array(leg_masses) * (1.0 + self._minitaur_leg_mass_err_range[1]) randomized_leg_masses = [ np.random.uniform(leg_masses_lower_bound[i], leg_masses_upper_bound[i]) for i in xrange(len(leg_masses)) ] minitaur.SetLegMasses(randomized_leg_masses) - randomized_battery_voltage = random.uniform(BATTERY_VOLTAGE_RANGE[0], - BATTERY_VOLTAGE_RANGE[1]) + randomized_battery_voltage = random.uniform(BATTERY_VOLTAGE_RANGE[0], BATTERY_VOLTAGE_RANGE[1]) minitaur.SetBatteryVoltage(randomized_battery_voltage) randomized_motor_damping = random.uniform(MOTOR_VISCOUS_DAMPING_RANGE[0], MOTOR_VISCOUS_DAMPING_RANGE[1]) minitaur.SetMotorViscousDamping(randomized_motor_damping) - randomized_foot_friction = random.uniform(MINITAUR_LEG_FRICTION[0], - MINITAUR_LEG_FRICTION[1]) + randomized_foot_friction = random.uniform(MINITAUR_LEG_FRICTION[0], MINITAUR_LEG_FRICTION[1]) minitaur.SetFootFriction(randomized_foot_friction) diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs/env_randomizers/minitaur_env_randomizer_config.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs/env_randomizers/minitaur_env_randomizer_config.py index 53d5ad122..85a97bab7 100644 --- a/examples/pybullet/gym/pybullet_envs/minitaur/envs/env_randomizers/minitaur_env_randomizer_config.py +++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs/env_randomizers/minitaur_env_randomizer_config.py @@ -4,7 +4,6 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function - PARAM_RANGE = { # The following ranges are in percentage. e.g. 0.8 means 80%. "mass": [0.8, 1.2], diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs/env_randomizers/minitaur_env_randomizer_from_config.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs/env_randomizers/minitaur_env_randomizer_from_config.py index 97b91609b..a550f35a0 100644 --- a/examples/pybullet/gym/pybullet_envs/minitaur/envs/env_randomizers/minitaur_env_randomizer_from_config.py +++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs/env_randomizers/minitaur_env_randomizer_from_config.py @@ -7,11 +7,11 @@ from __future__ import print_function import functools import random -import os, inspect +import os, inspect currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe()))) parentdir = os.path.dirname(os.path.dirname(currentdir)) parentdir = os.path.dirname(os.path.dirname(parentdir)) -os.sys.path.insert(0,parentdir) +os.sys.path.insert(0, parentdir) import numpy as np import tensorflow as tf @@ -32,8 +32,7 @@ class MinitaurEnvRandomizerFromConfig(env_randomizer_base.EnvRandomizerBase): except AttributeError: raise ValueError("Config {} is not found.".format(config)) self._randomization_param_dict = config() - tf.logging.info("Randomization config is: {}".format( - self._randomization_param_dict)) + tf.logging.info("Randomization config is: {}".format(self._randomization_param_dict)) def randomize_env(self, env): """Randomize various physical properties of the environment. @@ -43,35 +42,29 @@ class MinitaurEnvRandomizerFromConfig(env_randomizer_base.EnvRandomizerBase): Args: env: A minitaur gym environment. """ - self._randomization_function_dict = self._build_randomization_function_dict( - env) + self._randomization_function_dict = self._build_randomization_function_dict(env) for param_name, random_range in self._randomization_param_dict.iteritems(): - self._randomization_function_dict[param_name]( - lower_bound=random_range[0], upper_bound=random_range[1]) + self._randomization_function_dict[param_name](lower_bound=random_range[0], + upper_bound=random_range[1]) def _build_randomization_function_dict(self, env): func_dict = {} - func_dict["mass"] = functools.partial( - self._randomize_masses, minitaur=env.minitaur) - func_dict["inertia"] = functools.partial( - self._randomize_inertia, minitaur=env.minitaur) - func_dict["latency"] = functools.partial( - self._randomize_latency, minitaur=env.minitaur) - func_dict["joint friction"] = functools.partial( - self._randomize_joint_friction, minitaur=env.minitaur) - func_dict["motor friction"] = functools.partial( - self._randomize_motor_friction, minitaur=env.minitaur) - func_dict["restitution"] = functools.partial( - self._randomize_contact_restitution, minitaur=env.minitaur) - func_dict["lateral friction"] = functools.partial( - self._randomize_contact_friction, minitaur=env.minitaur) - func_dict["battery"] = functools.partial( - self._randomize_battery_level, minitaur=env.minitaur) - func_dict["motor strength"] = functools.partial( - self._randomize_motor_strength, minitaur=env.minitaur) + func_dict["mass"] = functools.partial(self._randomize_masses, minitaur=env.minitaur) + func_dict["inertia"] = functools.partial(self._randomize_inertia, minitaur=env.minitaur) + func_dict["latency"] = functools.partial(self._randomize_latency, minitaur=env.minitaur) + func_dict["joint friction"] = functools.partial(self._randomize_joint_friction, + minitaur=env.minitaur) + func_dict["motor friction"] = functools.partial(self._randomize_motor_friction, + minitaur=env.minitaur) + func_dict["restitution"] = functools.partial(self._randomize_contact_restitution, + minitaur=env.minitaur) + func_dict["lateral friction"] = functools.partial(self._randomize_contact_friction, + minitaur=env.minitaur) + func_dict["battery"] = functools.partial(self._randomize_battery_level, minitaur=env.minitaur) + func_dict["motor strength"] = functools.partial(self._randomize_motor_strength, + minitaur=env.minitaur) # Settinmg control step needs access to the environment. - func_dict["control step"] = functools.partial( - self._randomize_control_step, env=env) + func_dict["control step"] = functools.partial(self._randomize_control_step, env=env) return func_dict def _randomize_control_step(self, env, lower_bound, upper_bound): @@ -111,8 +104,8 @@ class MinitaurEnvRandomizerFromConfig(env_randomizer_base.EnvRandomizerBase): def _randomize_joint_friction(self, minitaur, lower_bound, upper_bound): num_knee_joints = minitaur.GetNumKneeJoints() - randomized_joint_frictions = np.random.uniform( - [lower_bound] * num_knee_joints, [upper_bound] * num_knee_joints) + randomized_joint_frictions = np.random.uniform([lower_bound] * num_knee_joints, + [upper_bound] * num_knee_joints) minitaur.SetJointFriction(randomized_joint_frictions) tf.logging.info("joint friction is: {}".format(randomized_joint_frictions)) @@ -137,9 +130,7 @@ class MinitaurEnvRandomizerFromConfig(env_randomizer_base.EnvRandomizerBase): tf.logging.info("battery voltage is: {}".format(randomized_battery_voltage)) def _randomize_motor_strength(self, minitaur, lower_bound, upper_bound): - randomized_motor_strength_ratios = np.random.uniform( - [lower_bound] * minitaur.num_motors, - [upper_bound] * minitaur.num_motors) + randomized_motor_strength_ratios = np.random.uniform([lower_bound] * minitaur.num_motors, + [upper_bound] * minitaur.num_motors) minitaur.SetMotorStrengthRatios(randomized_motor_strength_ratios) - tf.logging.info( - "motor strength is: {}".format(randomized_motor_strength_ratios)) + tf.logging.info("motor strength is: {}".format(randomized_motor_strength_ratios)) diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs/env_randomizers/minitaur_push_randomizer.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs/env_randomizers/minitaur_push_randomizer.py index e030c6ae4..8d9aa8e64 100644 --- a/examples/pybullet/gym/pybullet_envs/minitaur/envs/env_randomizers/minitaur_push_randomizer.py +++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs/env_randomizers/minitaur_push_randomizer.py @@ -4,11 +4,11 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -import os, inspect +import os, inspect currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe()))) parentdir = os.path.dirname(os.path.dirname(currentdir)) parentdir = os.path.dirname(os.path.dirname(parentdir)) -os.sys.path.insert(0,parentdir) +os.sys.path.insert(0, parentdir) import math import numpy as np @@ -50,12 +50,10 @@ class MinitaurPushRandomizer(env_randomizer_base.EnvRandomizerBase): self._perturbation_start_step = perturbation_start_step self._perturbation_interval_steps = perturbation_interval_steps self._perturbation_duration_steps = perturbation_duration_steps - self._horizontal_force_bound = ( - horizontal_force_bound if horizontal_force_bound else - [_HORIZONTAL_FORCE_LOWER_BOUND, _HORIZONTAL_FORCE_UPPER_BOUND]) - self._vertical_force_bound = ( - vertical_force_bound if vertical_force_bound else - [_VERTICAL_FORCE_LOWER_BOUND, _VERTICAL_FORCE_UPPER_BOUND]) + self._horizontal_force_bound = (horizontal_force_bound if horizontal_force_bound else + [_HORIZONTAL_FORCE_LOWER_BOUND, _HORIZONTAL_FORCE_UPPER_BOUND]) + self._vertical_force_bound = (vertical_force_bound if vertical_force_bound else + [_VERTICAL_FORCE_LOWER_BOUND, _VERTICAL_FORCE_UPPER_BOUND]) def randomize_env(self, env): """Randomizes the simulation environment. @@ -75,23 +73,20 @@ class MinitaurPushRandomizer(env_randomizer_base.EnvRandomizerBase): """ base_link_ids = env.minitaur.chassis_link_ids if env.env_step_counter % self._perturbation_interval_steps == 0: - self._applied_link_id = base_link_ids[np.random.randint( - 0, len(base_link_ids))] - horizontal_force_magnitude = np.random.uniform( - self._horizontal_force_bound[0], self._horizontal_force_bound[1]) + self._applied_link_id = base_link_ids[np.random.randint(0, len(base_link_ids))] + horizontal_force_magnitude = np.random.uniform(self._horizontal_force_bound[0], + self._horizontal_force_bound[1]) theta = np.random.uniform(0, 2 * math.pi) - vertical_force_magnitude = np.random.uniform( - self._vertical_force_bound[0], self._vertical_force_bound[1]) + vertical_force_magnitude = np.random.uniform(self._vertical_force_bound[0], + self._vertical_force_bound[1]) self._applied_force = horizontal_force_magnitude * np.array( - [math.cos(theta), math.sin(theta), 0]) + np.array( - [0, 0, -vertical_force_magnitude]) + [math.cos(theta), math.sin(theta), 0]) + np.array([0, 0, -vertical_force_magnitude]) if (env.env_step_counter % self._perturbation_interval_steps < self._perturbation_duration_steps) and (env.env_step_counter >= self._perturbation_start_step): - env.pybullet_client.applyExternalForce( - objectUniqueId=env.minitaur.quadruped, - linkIndex=self._applied_link_id, - forceObj=self._applied_force, - posObj=[0.0, 0.0, 0.0], - flags=env.pybullet_client.LINK_FRAME) + env.pybullet_client.applyExternalForce(objectUniqueId=env.minitaur.quadruped, + linkIndex=self._applied_link_id, + forceObj=self._applied_force, + posObj=[0.0, 0.0, 0.0], + flags=env.pybullet_client.LINK_FRAME) diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs/env_randomizers/minitaur_terrain_randomizer.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs/env_randomizers/minitaur_terrain_randomizer.py index b7e5706b8..d5f33371e 100644 --- a/examples/pybullet/gym/pybullet_envs/minitaur/envs/env_randomizers/minitaur_terrain_randomizer.py +++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs/env_randomizers/minitaur_terrain_randomizer.py @@ -4,11 +4,11 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -import os, inspect +import os, inspect currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe()))) parentdir = os.path.dirname(os.path.dirname(currentdir)) parentdir = os.path.dirname(os.path.dirname(parentdir)) -os.sys.path.insert(0,parentdir) +os.sys.path.insert(0, parentdir) import itertools import math @@ -62,8 +62,7 @@ class PoissonDisc2D(object): self._grid = [None] * self._grid_size_x * self._grid_size_y # Generate the first sample point and set it as an active site. - first_sample = np.array( - np.random.random_sample(2)) * [grid_length, grid_width] + first_sample = np.array(np.random.random_sample(2)) * [grid_length, grid_width] self._active_list = [first_sample] # Also store the sample point in the grid. @@ -114,8 +113,7 @@ class PoissonDisc2D(object): Returns: Whether the point is inside the grid. """ - return (0 <= point[0] < self._grid_length) and (0 <= point[1] < - self._grid_width) + return (0 <= point[0] < self._grid_length) and (0 <= point[1] < self._grid_width) def _is_in_range(self, index2d): """Checks if the cell ID is within the grid. @@ -127,8 +125,7 @@ class PoissonDisc2D(object): Whether the cell (2D index) is inside the grid. """ - return (0 <= index2d[0] < self._grid_size_x) and (0 <= index2d[1] < - self._grid_size_y) + return (0 <= index2d[0] < self._grid_size_x) and (0 <= index2d[1] < self._grid_size_y) def _is_close_to_existing_points(self, point): """Checks if the point is close to any already sampled (and stored) points. @@ -142,15 +139,13 @@ class PoissonDisc2D(object): """ px, py = self._point_to_index_2d(point) # Now we can check nearby cells for existing points - for neighbor_cell in itertools.product( - xrange(px - 1, px + 2), xrange(py - 1, py + 2)): + for neighbor_cell in itertools.product(xrange(px - 1, px + 2), xrange(py - 1, py + 2)): if not self._is_in_range(neighbor_cell): continue maybe_a_point = self._grid[self._index_2d_to_1d(neighbor_cell)] - if maybe_a_point is not None and np.linalg.norm( - maybe_a_point - point) < self._min_radius: + if maybe_a_point is not None and np.linalg.norm(maybe_a_point - point) < self._min_radius: return True return False @@ -168,8 +163,8 @@ class PoissonDisc2D(object): random_angle = np.random.uniform(0, 2 * math.pi) # The sampled 2D points near the active point - sample = random_radius * np.array( - [np.cos(random_angle), np.sin(random_angle)]) + active_point + sample = random_radius * np.array([np.cos(random_angle), + np.sin(random_angle)]) + active_point if not self._is_in_grid(sample): continue @@ -214,12 +209,11 @@ class TerrainType(enum.Enum): class MinitaurTerrainRandomizer(env_randomizer_base.EnvRandomizerBase): """Generates an uneven terrain in the gym env.""" - def __init__( - self, - terrain_type=TerrainType.TRIANGLE_MESH, - mesh_filename="robotics/reinforcement_learning/minitaur/envs/testdata/" - "triangle_mesh_terrain/terrain9735.obj", - mesh_scale=None): + def __init__(self, + terrain_type=TerrainType.TRIANGLE_MESH, + mesh_filename="robotics/reinforcement_learning/minitaur/envs/testdata/" + "triangle_mesh_terrain/terrain9735.obj", + mesh_scale=None): """Initializes the randomizer. Args: @@ -261,9 +255,7 @@ class MinitaurTerrainRandomizer(env_randomizer_base.EnvRandomizerBase): flags=1, meshScale=self._mesh_scale) env.ground_id = env.pybullet_client.createMultiBody( - baseMass=0, - baseCollisionShapeIndex=terrain_collision_shape_id, - basePosition=[0, 0, 0]) + baseMass=0, baseCollisionShapeIndex=terrain_collision_shape_id, basePosition=[0, 0, 0]) def _generate_convex_blocks(self, env): """Adds random convex blocks to the flat ground. @@ -277,8 +269,7 @@ class MinitaurTerrainRandomizer(env_randomizer_base.EnvRandomizerBase): """ - poisson_disc = PoissonDisc2D(_GRID_LENGTH, _GRID_WIDTH, _MIN_BLOCK_DISTANCE, - _MAX_SAMPLE_SIZE) + poisson_disc = PoissonDisc2D(_GRID_LENGTH, _GRID_WIDTH, _MIN_BLOCK_DISTANCE, _MAX_SAMPLE_SIZE) block_centers = poisson_disc.generate() @@ -289,12 +280,10 @@ class MinitaurTerrainRandomizer(env_randomizer_base.EnvRandomizerBase): # Do not place blocks near the point [0, 0], where the robot will start. if abs(shifted_center[0]) < 1.0 and abs(shifted_center[1]) < 1.0: continue - half_length = np.random.uniform(_MIN_BLOCK_LENGTH, _MAX_BLOCK_LENGTH) / ( - 2 * math.sqrt(2)) + half_length = np.random.uniform(_MIN_BLOCK_LENGTH, _MAX_BLOCK_LENGTH) / (2 * math.sqrt(2)) half_height = np.random.uniform(_MIN_BLOCK_HEIGHT, _MAX_BLOCK_HEIGHT) / 2 box_id = env.pybullet_client.createCollisionShape( - env.pybullet_client.GEOM_BOX, - halfExtents=[half_length, half_length, half_height]) + env.pybullet_client.GEOM_BOX, halfExtents=[half_length, half_length, half_height]) env.pybullet_client.createMultiBody( baseMass=0, baseCollisionShapeIndex=box_id, diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur.py index efe32837c..fc403c0d5 100644 --- a/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur.py +++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur.py @@ -2,10 +2,10 @@ """ -import os, inspect +import os, inspect currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe()))) parentdir = os.path.dirname(os.path.dirname(currentdir)) -os.sys.path.insert(0,parentdir) +os.sys.path.insert(0, parentdir) import collections import copy @@ -24,9 +24,8 @@ OVERHEAT_SHUTDOWN_TORQUE = 2.45 OVERHEAT_SHUTDOWN_TIME = 1.0 LEG_POSITION = ["front_left", "back_left", "front_right", "back_right"] MOTOR_NAMES = [ - "motor_front_leftL_joint", "motor_front_leftR_joint", - "motor_back_leftL_joint", "motor_back_leftR_joint", - "motor_front_rightL_joint", "motor_front_rightR_joint", + "motor_front_leftL_joint", "motor_front_leftR_joint", "motor_back_leftL_joint", + "motor_back_leftR_joint", "motor_front_rightL_joint", "motor_front_rightR_joint", "motor_back_rightL_joint", "motor_back_rightR_joint" ] _CHASSIS_NAME_PATTERN = re.compile(r"chassis\D*center") @@ -141,10 +140,9 @@ class Minitaur(object): if self._accurate_motor_model_enabled: self._kp = motor_kp self._kd = motor_kd - self._motor_model = motor.MotorModel( - torque_control_enabled=self._torque_control_enabled, - kp=self._kp, - kd=self._kd) + self._motor_model = motor.MotorModel(torque_control_enabled=self._torque_control_enabled, + kp=self._kp, + kd=self._kd) elif self._pd_control_enabled: self._kp = 8 self._kd = 0.3 @@ -188,17 +186,14 @@ class Minitaur(object): self._link_urdf = [] num_bodies = self._pybullet_client.getNumJoints(self.quadruped) for body_id in range(-1, num_bodies): # -1 is for the base link. - inertia = self._pybullet_client.getDynamicsInfo(self.quadruped, - body_id)[2] + inertia = self._pybullet_client.getDynamicsInfo(self.quadruped, body_id)[2] self._link_urdf.append(inertia) # We need to use id+1 to index self._link_urdf because it has the base # (index = -1) at the first element. self._base_inertia_urdf = [ self._link_urdf[chassis_id + 1] for chassis_id in self._chassis_link_ids ] - self._leg_inertia_urdf = [ - self._link_urdf[leg_id + 1] for leg_id in self._leg_link_ids - ] + self._leg_inertia_urdf = [self._link_urdf[leg_id + 1] for leg_id in self._leg_link_ids] self._leg_inertia_urdf.extend( [self._link_urdf[motor_id + 1] for motor_id in self._motor_link_ids]) @@ -239,13 +234,10 @@ class Minitaur(object): num_joints = self._pybullet_client.getNumJoints(self.quadruped) for i in range(num_joints): joint_info = self._pybullet_client.getJointInfo(self.quadruped, i) - self._pybullet_client.changeDynamics( - joint_info[0], -1, linearDamping=0, angularDamping=0) + self._pybullet_client.changeDynamics(joint_info[0], -1, linearDamping=0, angularDamping=0) def _BuildMotorIdList(self): - self._motor_id_list = [ - self._joint_name_to_id[motor_name] for motor_name in MOTOR_NAMES - ] + self._motor_id_list = [self._joint_name_to_id[motor_name] for motor_name in MOTOR_NAMES] def IsObservationValid(self): """Whether the observation is valid for the current time step. @@ -284,10 +276,10 @@ class Minitaur(object): useFixedBase=self._on_rack, flags=self._pybullet_client.URDF_USE_SELF_COLLISION) else: - self.quadruped = self._pybullet_client.loadURDF( - "%s/quadruped/minitaur.urdf" % self._urdf_root, - init_position, - useFixedBase=self._on_rack) + self.quadruped = self._pybullet_client.loadURDF("%s/quadruped/minitaur.urdf" % + self._urdf_root, + init_position, + useFixedBase=self._on_rack) self._BuildJointNameToIdDict() self._BuildUrdfIds() if self._remove_default_joint_damping: @@ -297,10 +289,9 @@ class Minitaur(object): self._RecordInertiaInfoFromURDF() self.ResetPose(add_constraint=True) else: - self._pybullet_client.resetBasePositionAndOrientation( - self.quadruped, init_position, INIT_ORIENTATION) - self._pybullet_client.resetBaseVelocity(self.quadruped, [0, 0, 0], - [0, 0, 0]) + self._pybullet_client.resetBasePositionAndOrientation(self.quadruped, init_position, + INIT_ORIENTATION) + self._pybullet_client.resetBaseVelocity(self.quadruped, [0, 0, 0], [0, 0, 0]) self.ResetPose(add_constraint=False) self._overheat_counter = np.zeros(self.num_motors) self._motor_enabled_list = [True] * self.num_motors @@ -325,25 +316,22 @@ class Minitaur(object): self.ReceiveObservation() def _SetMotorTorqueById(self, motor_id, torque): - self._pybullet_client.setJointMotorControl2( - bodyIndex=self.quadruped, - jointIndex=motor_id, - controlMode=self._pybullet_client.TORQUE_CONTROL, - force=torque) + self._pybullet_client.setJointMotorControl2(bodyIndex=self.quadruped, + jointIndex=motor_id, + controlMode=self._pybullet_client.TORQUE_CONTROL, + force=torque) def _SetDesiredMotorAngleById(self, motor_id, desired_angle): - self._pybullet_client.setJointMotorControl2( - bodyIndex=self.quadruped, - jointIndex=motor_id, - controlMode=self._pybullet_client.POSITION_CONTROL, - targetPosition=desired_angle, - positionGain=self._kp, - velocityGain=self._kd, - force=self._max_force) + self._pybullet_client.setJointMotorControl2(bodyIndex=self.quadruped, + jointIndex=motor_id, + controlMode=self._pybullet_client.POSITION_CONTROL, + targetPosition=desired_angle, + positionGain=self._kp, + velocityGain=self._kd, + force=self._max_force) def _SetDesiredMotorAngleByName(self, motor_name, desired_angle): - self._SetDesiredMotorAngleById(self._joint_name_to_id[motor_name], - desired_angle) + self._SetDesiredMotorAngleById(self._joint_name_to_id[motor_name], desired_angle) def ResetPose(self, add_constraint): """Reset the pose of the minitaur. @@ -367,59 +355,53 @@ class Minitaur(object): knee_angle = -2.1834 leg_position = LEG_POSITION[leg_id] - self._pybullet_client.resetJointState( - self.quadruped, - self._joint_name_to_id["motor_" + leg_position + "L_joint"], - self._motor_direction[2 * leg_id] * half_pi, - targetVelocity=0) - self._pybullet_client.resetJointState( - self.quadruped, - self._joint_name_to_id["knee_" + leg_position + "L_link"], - self._motor_direction[2 * leg_id] * knee_angle, - targetVelocity=0) - self._pybullet_client.resetJointState( - self.quadruped, - self._joint_name_to_id["motor_" + leg_position + "R_joint"], - self._motor_direction[2 * leg_id + 1] * half_pi, - targetVelocity=0) - self._pybullet_client.resetJointState( - self.quadruped, - self._joint_name_to_id["knee_" + leg_position + "R_link"], - self._motor_direction[2 * leg_id + 1] * knee_angle, - targetVelocity=0) + self._pybullet_client.resetJointState(self.quadruped, + self._joint_name_to_id["motor_" + leg_position + + "L_joint"], + self._motor_direction[2 * leg_id] * half_pi, + targetVelocity=0) + self._pybullet_client.resetJointState(self.quadruped, + self._joint_name_to_id["knee_" + leg_position + + "L_link"], + self._motor_direction[2 * leg_id] * knee_angle, + targetVelocity=0) + self._pybullet_client.resetJointState(self.quadruped, + self._joint_name_to_id["motor_" + leg_position + + "R_joint"], + self._motor_direction[2 * leg_id + 1] * half_pi, + targetVelocity=0) + self._pybullet_client.resetJointState(self.quadruped, + self._joint_name_to_id["knee_" + leg_position + + "R_link"], + self._motor_direction[2 * leg_id + 1] * knee_angle, + targetVelocity=0) if add_constraint: self._pybullet_client.createConstraint( - self.quadruped, - self._joint_name_to_id["knee_" + leg_position + "R_link"], - self.quadruped, - self._joint_name_to_id["knee_" + leg_position + "L_link"], - self._pybullet_client.JOINT_POINT2POINT, [0, 0, 0], - KNEE_CONSTRAINT_POINT_RIGHT, KNEE_CONSTRAINT_POINT_LEFT) + self.quadruped, self._joint_name_to_id["knee_" + leg_position + "R_link"], + self.quadruped, self._joint_name_to_id["knee_" + leg_position + "L_link"], + self._pybullet_client.JOINT_POINT2POINT, [0, 0, 0], KNEE_CONSTRAINT_POINT_RIGHT, + KNEE_CONSTRAINT_POINT_LEFT) if self._accurate_motor_model_enabled or self._pd_control_enabled: # Disable the default motor in pybullet. self._pybullet_client.setJointMotorControl2( bodyIndex=self.quadruped, - jointIndex=( - self._joint_name_to_id["motor_" + leg_position + "L_joint"]), + jointIndex=(self._joint_name_to_id["motor_" + leg_position + "L_joint"]), controlMode=self._pybullet_client.VELOCITY_CONTROL, targetVelocity=0, force=knee_friction_force) self._pybullet_client.setJointMotorControl2( bodyIndex=self.quadruped, - jointIndex=( - self._joint_name_to_id["motor_" + leg_position + "R_joint"]), + jointIndex=(self._joint_name_to_id["motor_" + leg_position + "R_joint"]), controlMode=self._pybullet_client.VELOCITY_CONTROL, targetVelocity=0, force=knee_friction_force) else: - self._SetDesiredMotorAngleByName( - "motor_" + leg_position + "L_joint", - self._motor_direction[2 * leg_id] * half_pi) - self._SetDesiredMotorAngleByName( - "motor_" + leg_position + "R_joint", - self._motor_direction[2 * leg_id + 1] * half_pi) + self._SetDesiredMotorAngleByName("motor_" + leg_position + "L_joint", + self._motor_direction[2 * leg_id] * half_pi) + self._SetDesiredMotorAngleByName("motor_" + leg_position + "R_joint", + self._motor_direction[2 * leg_id + 1] * half_pi) self._pybullet_client.setJointMotorControl2( bodyIndex=self.quadruped, @@ -440,8 +422,7 @@ class Minitaur(object): Returns: The position of minitaur's base. """ - position, _ = ( - self._pybullet_client.getBasePositionAndOrientation(self.quadruped)) + position, _ = (self._pybullet_client.getBasePositionAndOrientation(self.quadruped)) return position def GetTrueBaseRollPitchYaw(self): @@ -464,10 +445,9 @@ class Minitaur(object): """ delayed_orientation = np.array( self._control_observation[3 * self.num_motors:3 * self.num_motors + 4]) - delayed_roll_pitch_yaw = self._pybullet_client.getEulerFromQuaternion( - delayed_orientation) - roll_pitch_yaw = self._AddSensorNoise( - np.array(delayed_roll_pitch_yaw), self._observation_noise_stdev[3]) + delayed_roll_pitch_yaw = self._pybullet_client.getEulerFromQuaternion(delayed_orientation) + roll_pitch_yaw = self._AddSensorNoise(np.array(delayed_roll_pitch_yaw), + self._observation_noise_stdev[3]) return roll_pitch_yaw def GetTrueMotorAngles(self): @@ -492,9 +472,8 @@ class Minitaur(object): Returns: Motor angles polluted by noise and latency, mapped to [-pi, pi]. """ - motor_angles = self._AddSensorNoise( - np.array(self._control_observation[0:self.num_motors]), - self._observation_noise_stdev[0]) + motor_angles = self._AddSensorNoise(np.array(self._control_observation[0:self.num_motors]), + self._observation_noise_stdev[0]) return MapToMinusPiToPi(motor_angles) def GetTrueMotorVelocities(self): @@ -518,8 +497,7 @@ class Minitaur(object): Velocities of all eight motors polluted by noise and latency. """ return self._AddSensorNoise( - np.array( - self._control_observation[self.num_motors:2 * self.num_motors]), + np.array(self._control_observation[self.num_motors:2 * self.num_motors]), self._observation_noise_stdev[1]) def GetTrueMotorTorques(self): @@ -546,8 +524,7 @@ class Minitaur(object): Motor torques of all eight motors polluted by noise and latency. """ return self._AddSensorNoise( - np.array( - self._control_observation[2 * self.num_motors:3 * self.num_motors]), + np.array(self._control_observation[2 * self.num_motors:3 * self.num_motors]), self._observation_noise_stdev[2]) def GetTrueBaseOrientation(self): @@ -556,8 +533,7 @@ class Minitaur(object): Returns: The orientation of minitaur's base. """ - _, orientation = ( - self._pybullet_client.getBasePositionAndOrientation(self.quadruped)) + _, orientation = (self._pybullet_client.getBasePositionAndOrientation(self.quadruped)) return orientation def GetBaseOrientation(self): @@ -567,8 +543,7 @@ class Minitaur(object): Returns: The orientation of minitaur's base polluted by noise and latency. """ - return self._pybullet_client.getQuaternionFromEuler( - self.GetBaseRollPitchYaw()) + return self._pybullet_client.getQuaternionFromEuler(self.GetBaseRollPitchYaw()) def GetTrueBaseRollPitchYawRate(self): """Get the rate of orientation change of the minitaur's base in euler angle. @@ -588,8 +563,7 @@ class Minitaur(object): and latency. """ return self._AddSensorNoise( - np.array(self._control_observation[3 * self.num_motors + 4: - 3 * self.num_motors + 7]), + np.array(self._control_observation[3 * self.num_motors + 4:3 * self.num_motors + 7]), self._observation_noise_stdev[4]) def GetActionDimension(self): @@ -618,12 +592,9 @@ class Minitaur(object): """ if self._motor_velocity_limit < np.inf: current_motor_angle = self.GetTrueMotorAngles() - motor_commands_max = ( - current_motor_angle + self.time_step * self._motor_velocity_limit) - motor_commands_min = ( - current_motor_angle - self.time_step * self._motor_velocity_limit) - motor_commands = np.clip(motor_commands, motor_commands_min, - motor_commands_max) + motor_commands_max = (current_motor_angle + self.time_step * self._motor_velocity_limit) + motor_commands_min = (current_motor_angle - self.time_step * self._motor_velocity_limit) + motor_commands = np.clip(motor_commands, motor_commands_min, motor_commands_max) # Set the kp and kd for all the motors if not provided as an argument. if motor_kps is None: motor_kps = np.full(8, self._kp) @@ -642,8 +613,7 @@ class Minitaur(object): self._overheat_counter[i] += 1 else: self._overheat_counter[i] = 0 - if (self._overheat_counter[i] > - OVERHEAT_SHUTDOWN_TIME / self.time_step): + if (self._overheat_counter[i] > OVERHEAT_SHUTDOWN_TIME / self.time_step): self._motor_enabled_list[i] = False # The torque is already in the observation space because we use @@ -651,19 +621,17 @@ class Minitaur(object): self._observed_motor_torques = observed_torque # Transform into the motor space when applying the torque. - self._applied_motor_torque = np.multiply(actual_torque, - self._motor_direction) + self._applied_motor_torque = np.multiply(actual_torque, self._motor_direction) - for motor_id, motor_torque, motor_enabled in zip( - self._motor_id_list, self._applied_motor_torque, - self._motor_enabled_list): + for motor_id, motor_torque, motor_enabled in zip(self._motor_id_list, + self._applied_motor_torque, + self._motor_enabled_list): if motor_enabled: self._SetMotorTorqueById(motor_id, motor_torque) else: self._SetMotorTorqueById(motor_id, 0) else: - torque_commands = -1 * motor_kps * ( - q - motor_commands) - motor_kds * qdot + torque_commands = -1 * motor_kps * (q - motor_commands) - motor_kds * qdot # The torque is already in the observation space because we use # GetMotorAngles and GetMotorVelocities. @@ -673,14 +641,12 @@ class Minitaur(object): self._applied_motor_torques = np.multiply(self._observed_motor_torques, self._motor_direction) - for motor_id, motor_torque in zip(self._motor_id_list, - self._applied_motor_torques): + for motor_id, motor_torque in zip(self._motor_id_list, self._applied_motor_torques): self._SetMotorTorqueById(motor_id, motor_torque) else: - motor_commands_with_direction = np.multiply(motor_commands, - self._motor_direction) - for motor_id, motor_command_with_direction in zip( - self._motor_id_list, motor_commands_with_direction): + motor_commands_with_direction = np.multiply(motor_commands, self._motor_direction) + for motor_id, motor_command_with_direction in zip(self._motor_id_list, + motor_commands_with_direction): self._SetDesiredMotorAngleById(motor_id, motor_command_with_direction) def ConvertFromLegModel(self, actions): @@ -698,13 +664,13 @@ class Minitaur(object): quater_pi = math.pi / 4 for i in range(self.num_motors): action_idx = int(i // 2) - forward_backward_component = (-scale_for_singularity * quater_pi * ( - actions[action_idx + half_num_motors] + offset_for_singularity)) + forward_backward_component = ( + -scale_for_singularity * quater_pi * + (actions[action_idx + half_num_motors] + offset_for_singularity)) extension_component = (-1)**i * quater_pi * actions[action_idx] if i >= half_num_motors: extension_component = -extension_component - motor_angle[i] = ( - math.pi + forward_backward_component + extension_component) + motor_angle[i] = (math.pi + forward_backward_component + extension_component) return motor_angle def GetBaseMassesFromURDF(self): @@ -734,12 +700,10 @@ class Minitaur(object): the length of self._chassis_link_ids. """ if len(base_mass) != len(self._chassis_link_ids): - raise ValueError( - "The length of base_mass {} and self._chassis_link_ids {} are not " - "the same.".format(len(base_mass), len(self._chassis_link_ids))) + raise ValueError("The length of base_mass {} and self._chassis_link_ids {} are not " + "the same.".format(len(base_mass), len(self._chassis_link_ids))) for chassis_id, chassis_mass in zip(self._chassis_link_ids, base_mass): - self._pybullet_client.changeDynamics( - self.quadruped, chassis_id, mass=chassis_mass) + self._pybullet_client.changeDynamics(self.quadruped, chassis_id, mass=chassis_mass) def SetLegMasses(self, leg_masses): """Set the mass of the legs. @@ -759,12 +723,10 @@ class Minitaur(object): raise ValueError("The number of values passed to SetLegMasses are " "different than number of leg links and motors.") for leg_id, leg_mass in zip(self._leg_link_ids, leg_masses): - self._pybullet_client.changeDynamics( - self.quadruped, leg_id, mass=leg_mass) + self._pybullet_client.changeDynamics(self.quadruped, leg_id, mass=leg_mass) motor_masses = leg_masses[len(self._leg_link_ids):] for link_id, motor_mass in zip(self._motor_link_ids, motor_masses): - self._pybullet_client.changeDynamics( - self.quadruped, link_id, mass=motor_mass) + self._pybullet_client.changeDynamics(self.quadruped, link_id, mass=motor_mass) def SetBaseInertias(self, base_inertias): """Set the inertias of minitaur's base. @@ -779,17 +741,15 @@ class Minitaur(object): negative values. """ if len(base_inertias) != len(self._chassis_link_ids): - raise ValueError( - "The length of base_inertias {} and self._chassis_link_ids {} are " - "not the same.".format( - len(base_inertias), len(self._chassis_link_ids))) - for chassis_id, chassis_inertia in zip(self._chassis_link_ids, - base_inertias): + raise ValueError("The length of base_inertias {} and self._chassis_link_ids {} are " + "not the same.".format(len(base_inertias), len(self._chassis_link_ids))) + for chassis_id, chassis_inertia in zip(self._chassis_link_ids, base_inertias): for inertia_value in chassis_inertia: if (np.asarray(inertia_value) < 0).any(): raise ValueError("Values in inertia matrix should be non-negative.") - self._pybullet_client.changeDynamics( - self.quadruped, chassis_id, localInertiaDiagonal=chassis_inertia) + self._pybullet_client.changeDynamics(self.quadruped, + chassis_id, + localInertiaDiagonal=chassis_inertia) def SetLegInertias(self, leg_inertias): """Set the inertias of the legs. @@ -813,16 +773,18 @@ class Minitaur(object): for inertia_value in leg_inertias: if (np.asarray(inertia_value) < 0).any(): raise ValueError("Values in inertia matrix should be non-negative.") - self._pybullet_client.changeDynamics( - self.quadruped, leg_id, localInertiaDiagonal=leg_inertia) + self._pybullet_client.changeDynamics(self.quadruped, + leg_id, + localInertiaDiagonal=leg_inertia) motor_inertias = leg_inertias[len(self._leg_link_ids):] for link_id, motor_inertia in zip(self._motor_link_ids, motor_inertias): for inertia_value in motor_inertias: if (np.asarray(inertia_value) < 0).any(): raise ValueError("Values in inertia matrix should be non-negative.") - self._pybullet_client.changeDynamics( - self.quadruped, link_id, localInertiaDiagonal=motor_inertia) + self._pybullet_client.changeDynamics(self.quadruped, + link_id, + localInertiaDiagonal=motor_inertia) def SetFootFriction(self, foot_friction): """Set the lateral friction of the feet. @@ -832,8 +794,7 @@ class Minitaur(object): shared by all four feet. """ for link_id in self._foot_link_ids: - self._pybullet_client.changeDynamics( - self.quadruped, link_id, lateralFriction=foot_friction) + self._pybullet_client.changeDynamics(self.quadruped, link_id, lateralFriction=foot_friction) # TODO(b/73748980): Add more API's to set other contact parameters. def SetFootRestitution(self, foot_restitution): @@ -844,8 +805,7 @@ class Minitaur(object): This value is shared by all four feet. """ for link_id in self._foot_link_ids: - self._pybullet_client.changeDynamics( - self.quadruped, link_id, restitution=foot_restitution) + self._pybullet_client.changeDynamics(self.quadruped, link_id, restitution=foot_restitution) def SetJointFriction(self, joint_frictions): for knee_joint_id, friction in zip(self._foot_link_ids, joint_frictions): @@ -901,9 +861,8 @@ class Minitaur(object): return self._observation_history[-1] remaining_latency = latency - n_steps_ago * self.time_step blend_alpha = remaining_latency / self.time_step - observation = ( - (1.0 - blend_alpha) * np.array(self._observation_history[n_steps_ago]) - + blend_alpha * np.array(self._observation_history[n_steps_ago + 1])) + observation = ((1.0 - blend_alpha) * np.array(self._observation_history[n_steps_ago]) + + blend_alpha * np.array(self._observation_history[n_steps_ago + 1])) return observation def _GetPDObservation(self): @@ -913,15 +872,13 @@ class Minitaur(object): return (np.array(q), np.array(qdot)) def _GetControlObservation(self): - control_delayed_observation = self._GetDelayedObservation( - self._control_latency) + control_delayed_observation = self._GetDelayedObservation(self._control_latency) return control_delayed_observation def _AddSensorNoise(self, sensor_values, noise_stdev): if noise_stdev <= 0: return sensor_values - observation = sensor_values + np.random.normal( - scale=noise_stdev, size=sensor_values.shape) + observation = sensor_values + np.random.normal(scale=noise_stdev, size=sensor_values.shape) return observation def SetControlLatency(self, latency): diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_alternating_legs_env.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_alternating_legs_env.py index 9529062fa..fa177b7b5 100644 --- a/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_alternating_legs_env.py +++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_alternating_legs_env.py @@ -3,10 +3,10 @@ """ import math -import os, inspect +import os, inspect currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe()))) parentdir = os.path.dirname(os.path.dirname(currentdir)) -os.sys.path.insert(0,parentdir) +os.sys.path.insert(0, parentdir) from gym import spaces import numpy as np @@ -31,10 +31,7 @@ class MinitaurAlternatingLegsEnv(minitaur_gym_env.MinitaurGymEnv): expenditure. """ - metadata = { - "render.modes": ["human", "rgb_array"], - "video.frames_per_second": 66 - } + metadata = {"render.modes": ["human", "rgb_array"], "video.frames_per_second": 66} def __init__(self, urdf_version=None, @@ -81,23 +78,23 @@ class MinitaurAlternatingLegsEnv(minitaur_gym_env.MinitaurGymEnv): # _swing_offset and _extension_offset is to mimick the bent legs. self._swing_offset = np.zeros(NUM_LEGS) self._extension_offset = np.zeros(NUM_LEGS) - super(MinitaurAlternatingLegsEnv, self).__init__( - urdf_version=urdf_version, - accurate_motor_model_enabled=True, - motor_overheat_protection=True, - hard_reset=False, - motor_kp=motor_kp, - motor_kd=motor_kd, - remove_default_joint_damping=remove_default_joint_damping, - control_latency=control_latency, - pd_latency=pd_latency, - on_rack=on_rack, - render=render, - num_steps_to_log=num_steps_to_log, - env_randomizer=env_randomizer, - log_path=log_path, - control_time_step=control_time_step, - action_repeat=action_repeat) + super(MinitaurAlternatingLegsEnv, + self).__init__(urdf_version=urdf_version, + accurate_motor_model_enabled=True, + motor_overheat_protection=True, + hard_reset=False, + motor_kp=motor_kp, + motor_kd=motor_kd, + remove_default_joint_damping=remove_default_joint_damping, + control_latency=control_latency, + pd_latency=pd_latency, + on_rack=on_rack, + render=render, + num_steps_to_log=num_steps_to_log, + env_randomizer=env_randomizer, + log_path=log_path, + control_time_step=control_time_step, + action_repeat=action_repeat) action_dim = 8 action_high = np.array([0.1] * action_dim) @@ -113,33 +110,29 @@ class MinitaurAlternatingLegsEnv(minitaur_gym_env.MinitaurGymEnv): # [swing leg 1, swing leg 2, swing leg 3, swing leg 4, # extension leg 1, extension leg 2, extension leg 3, extension leg 4] init_pose = [ - INIT_SWING_POS + self._swing_offset[0], - INIT_SWING_POS + self._swing_offset[1], - INIT_SWING_POS + self._swing_offset[2], - INIT_SWING_POS + self._swing_offset[3], + INIT_SWING_POS + self._swing_offset[0], INIT_SWING_POS + self._swing_offset[1], + INIT_SWING_POS + self._swing_offset[2], INIT_SWING_POS + self._swing_offset[3], INIT_EXTENSION_POS + self._extension_offset[0], INIT_EXTENSION_POS + self._extension_offset[1], INIT_EXTENSION_POS + self._extension_offset[2], INIT_EXTENSION_POS + self._extension_offset[3] ] initial_motor_angles = self._convert_from_leg_model(init_pose) - super(MinitaurAlternatingLegsEnv, self).reset( - initial_motor_angles=initial_motor_angles, reset_duration=0.5) + super(MinitaurAlternatingLegsEnv, self).reset(initial_motor_angles=initial_motor_angles, + reset_duration=0.5) return self._get_observation() def _convert_from_leg_model(self, leg_pose): motor_pose = np.zeros(NUM_MOTORS) for i in range(NUM_LEGS): motor_pose[2 * i] = leg_pose[NUM_LEGS + i] - (-1)**(i / 2) * leg_pose[i] - motor_pose[2 * i - + 1] = leg_pose[NUM_LEGS + i] + (-1)**(i / 2) * leg_pose[i] + motor_pose[2 * i + 1] = leg_pose[NUM_LEGS + i] + (-1)**(i / 2) * leg_pose[i] return motor_pose def _signal(self, t): initial_pose = np.array([ - INIT_SWING_POS, INIT_SWING_POS, INIT_SWING_POS, INIT_SWING_POS, - INIT_EXTENSION_POS, INIT_EXTENSION_POS, INIT_EXTENSION_POS, - INIT_EXTENSION_POS + INIT_SWING_POS, INIT_SWING_POS, INIT_SWING_POS, INIT_SWING_POS, INIT_EXTENSION_POS, + INIT_EXTENSION_POS, INIT_EXTENSION_POS, INIT_EXTENSION_POS ]) amplitude = STEP_AMPLITUDE period = STEP_PERIOD diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_alternating_legs_env_example.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_alternating_legs_env_example.py index f1266d354..226edf3c5 100644 --- a/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_alternating_legs_env_example.py +++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_alternating_legs_env_example.py @@ -3,10 +3,10 @@ """ import time -import os, inspect +import os, inspect currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe()))) parentdir = os.path.dirname(os.path.dirname(currentdir)) -os.sys.path.insert(0,parentdir) +os.sys.path.insert(0, parentdir) import os import numpy as np @@ -50,13 +50,11 @@ def hand_tuned_agent(observation, timestamp): pitch_compensation = pitch_gain * pitch + pitch_dot_gain * pitch_dot first_leg = [ - 0, -pitch_compensation, -pitch_compensation, 0, 0, - -pitch_compensation - roll_compensation, + 0, -pitch_compensation, -pitch_compensation, 0, 0, -pitch_compensation - roll_compensation, pitch_compensation + roll_compensation, 0 ] second_leg = [ - -pitch_compensation, 0, 0, -pitch_compensation, - pitch_compensation - roll_compensation, 0, 0, + -pitch_compensation, 0, 0, -pitch_compensation, pitch_compensation - roll_compensation, 0, 0, -pitch_compensation + roll_compensation ] if (timestamp // minitaur_alternating_legs_env.STEP_PERIOD) % 2: @@ -94,8 +92,7 @@ def hand_tuned_balance_example(log_path=None): for _ in range(steps): # Sleep to prevent serial buffer overflow on microcontroller. time.sleep(0.002) - action = hand_tuned_agent(observation, - environment.minitaur.GetTimeSinceReset()) + action = hand_tuned_agent(observation, environment.minitaur.GetTimeSinceReset()) observation, reward, done, _ = environment.step(action) sum_reward += reward if done: diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_ball_gym_env.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_ball_gym_env.py index c2ec7ded4..b40d6f0d4 100644 --- a/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_ball_gym_env.py +++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_ball_gym_env.py @@ -4,10 +4,10 @@ import math import random -import os, inspect +import os, inspect currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe()))) parentdir = os.path.dirname(os.path.dirname(currentdir)) -os.sys.path.insert(0,parentdir) +os.sys.path.insert(0, parentdir) from gym import spaces import numpy as np @@ -52,61 +52,52 @@ class MinitaurBallGymEnv(minitaur_gym_env.MinitaurGymEnv): that its walking gait is clearer to visualize. render: Whether to render the simulation. """ - super(MinitaurBallGymEnv, self).__init__( - urdf_root=urdf_root, - self_collision_enabled=self_collision_enabled, - pd_control_enabled=pd_control_enabled, - leg_model_enabled=leg_model_enabled, - on_rack=on_rack, - render=render) + super(MinitaurBallGymEnv, self).__init__(urdf_root=urdf_root, + self_collision_enabled=self_collision_enabled, + pd_control_enabled=pd_control_enabled, + leg_model_enabled=leg_model_enabled, + on_rack=on_rack, + render=render) self._cam_dist = 2.0 self._cam_yaw = -70 self._cam_pitch = -30 self.action_space = spaces.Box(np.array([-1]), np.array([1])) - self.observation_space = spaces.Box(np.array([-math.pi, 0]), - np.array([math.pi, 100])) + self.observation_space = spaces.Box(np.array([-math.pi, 0]), np.array([math.pi, 100])) def reset(self): self._ball_id = 0 super(MinitaurBallGymEnv, self).reset() self._init_ball_theta = random.uniform(-INIT_BALL_ANGLE, INIT_BALL_ANGLE) self._init_ball_distance = INIT_BALL_DISTANCE - self._ball_pos = [self._init_ball_distance * - math.cos(self._init_ball_theta), - self._init_ball_distance * - math.sin(self._init_ball_theta), 1] + self._ball_pos = [ + self._init_ball_distance * math.cos(self._init_ball_theta), + self._init_ball_distance * math.sin(self._init_ball_theta), 1 + ] self._ball_id = self._pybullet_client.loadURDF( "%s/sphere_with_restitution.urdf" % self._urdf_root, self._ball_pos) return self._get_observation() def _get_observation(self): world_translation_minitaur, world_rotation_minitaur = ( - self._pybullet_client.getBasePositionAndOrientation( - self.minitaur.quadruped)) + self._pybullet_client.getBasePositionAndOrientation(self.minitaur.quadruped)) world_translation_ball, world_rotation_ball = ( self._pybullet_client.getBasePositionAndOrientation(self._ball_id)) - minitaur_translation_world, minitaur_rotation_world = ( - self._pybullet_client.invertTransform(world_translation_minitaur, - world_rotation_minitaur)) - minitaur_translation_ball, _ = ( - self._pybullet_client.multiplyTransforms(minitaur_translation_world, - minitaur_rotation_world, - world_translation_ball, - world_rotation_ball)) - distance = math.sqrt(minitaur_translation_ball[0]**2 + - minitaur_translation_ball[1]**2) - angle = math.atan2(minitaur_translation_ball[0], - minitaur_translation_ball[1]) + minitaur_translation_world, minitaur_rotation_world = (self._pybullet_client.invertTransform( + world_translation_minitaur, world_rotation_minitaur)) + minitaur_translation_ball, _ = (self._pybullet_client.multiplyTransforms( + minitaur_translation_world, minitaur_rotation_world, world_translation_ball, + world_rotation_ball)) + distance = math.sqrt(minitaur_translation_ball[0]**2 + minitaur_translation_ball[1]**2) + angle = math.atan2(minitaur_translation_ball[0], minitaur_translation_ball[1]) self._observation = [angle - math.pi / 2, distance] return self._observation def _transform_action_to_motor_command(self, action): if self._leg_model_enabled: for i, action_component in enumerate(action): - if not (-self._action_bound - ACTION_EPS <= - action_component <= self._action_bound + ACTION_EPS): - raise ValueError("{}th action {} out of bounds.".format - (i, action_component)) + if not (-self._action_bound - ACTION_EPS <= action_component <= + self._action_bound + ACTION_EPS): + raise ValueError("{}th action {} out of bounds.".format(i, action_component)) action = self._apply_steering_to_locomotion(action) action = self.minitaur.ConvertFromLegModel(action) return action @@ -126,15 +117,12 @@ class MinitaurBallGymEnv(minitaur_gym_env.MinitaurGymEnv): return action def _distance_to_ball(self): - world_translation_minitaur, _ = ( - self._pybullet_client.getBasePositionAndOrientation( - self.minitaur.quadruped)) - world_translation_ball, _ = ( - self._pybullet_client.getBasePositionAndOrientation( - self._ball_id)) - distance = math.sqrt( - (world_translation_ball[0] - world_translation_minitaur[0])**2 + - (world_translation_ball[1] - world_translation_minitaur[1])**2) + world_translation_minitaur, _ = (self._pybullet_client.getBasePositionAndOrientation( + self.minitaur.quadruped)) + world_translation_ball, _ = (self._pybullet_client.getBasePositionAndOrientation( + self._ball_id)) + distance = math.sqrt((world_translation_ball[0] - world_translation_minitaur[0])**2 + + (world_translation_ball[1] - world_translation_minitaur[1])**2) return distance def _goal_state(self): diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_ball_gym_env_example.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_ball_gym_env_example.py index b52b945ad..404dc26ac 100644 --- a/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_ball_gym_env_example.py +++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_ball_gym_env_example.py @@ -31,4 +31,4 @@ def main(): if __name__ == '__main__': - main()
\ No newline at end of file + main() diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_derpy.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_derpy.py index 2b5eb2698..7946955d4 100644 --- a/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_derpy.py +++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_derpy.py @@ -6,13 +6,13 @@ It is the result of first pass system identification for the derpy robot. The """ import math -import os, inspect +import os, inspect currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe()))) parentdir = os.path.dirname(os.path.dirname(currentdir)) -os.sys.path.insert(0,parentdir) +os.sys.path.insert(0, parentdir) import numpy as np -from pybullet_envs.minitaur.envs import minitaur +from pybullet_envs.minitaur.envs import minitaur KNEE_CONSTRAINT_POINT_LONG = [0, 0.0055, 0.088] KNEE_CONSTRAINT_POINT_SHORT = [0, 0.0055, 0.100] @@ -46,13 +46,12 @@ class MinitaurDerpy(minitaur.Minitaur): "%s/quadruped/minitaur_derpy.urdf" % self._urdf_root, init_position, useFixedBase=self._on_rack, - flags=( - self._pybullet_client.URDF_USE_SELF_COLLISION_EXCLUDE_PARENT)) + flags=(self._pybullet_client.URDF_USE_SELF_COLLISION_EXCLUDE_PARENT)) else: - self.quadruped = self._pybullet_client.loadURDF( - "%s/quadruped/minitaur_derpy.urdf" % self._urdf_root, - init_position, - useFixedBase=self._on_rack) + self.quadruped = self._pybullet_client.loadURDF("%s/quadruped/minitaur_derpy.urdf" % + self._urdf_root, + init_position, + useFixedBase=self._on_rack) self._BuildJointNameToIdDict() self._BuildUrdfIds() if self._remove_default_joint_damping: @@ -62,10 +61,9 @@ class MinitaurDerpy(minitaur.Minitaur): self._RecordInertiaInfoFromURDF() self.ResetPose(add_constraint=True) else: - self._pybullet_client.resetBasePositionAndOrientation( - self.quadruped, init_position, minitaur.INIT_ORIENTATION) - self._pybullet_client.resetBaseVelocity(self.quadruped, [0, 0, 0], - [0, 0, 0]) + self._pybullet_client.resetBasePositionAndOrientation(self.quadruped, init_position, + minitaur.INIT_ORIENTATION) + self._pybullet_client.resetBaseVelocity(self.quadruped, [0, 0, 0], [0, 0, 0]) self.ResetPose(add_constraint=False) self._overheat_counter = np.zeros(self.num_motors) @@ -103,68 +101,60 @@ class MinitaurDerpy(minitaur.Minitaur): knee_angle = -2.1834 leg_position = minitaur.LEG_POSITION[leg_id] - self._pybullet_client.resetJointState( - self.quadruped, - self._joint_name_to_id["motor_" + leg_position + "L_joint"], - self._motor_direction[2 * leg_id] * half_pi, - targetVelocity=0) - self._pybullet_client.resetJointState( - self.quadruped, - self._joint_name_to_id["knee_" + leg_position + "L_joint"], - self._motor_direction[2 * leg_id] * knee_angle, - targetVelocity=0) - self._pybullet_client.resetJointState( - self.quadruped, - self._joint_name_to_id["motor_" + leg_position + "R_joint"], - self._motor_direction[2 * leg_id + 1] * half_pi, - targetVelocity=0) - self._pybullet_client.resetJointState( - self.quadruped, - self._joint_name_to_id["knee_" + leg_position + "R_joint"], - self._motor_direction[2 * leg_id + 1] * knee_angle, - targetVelocity=0) + self._pybullet_client.resetJointState(self.quadruped, + self._joint_name_to_id["motor_" + leg_position + + "L_joint"], + self._motor_direction[2 * leg_id] * half_pi, + targetVelocity=0) + self._pybullet_client.resetJointState(self.quadruped, + self._joint_name_to_id["knee_" + leg_position + + "L_joint"], + self._motor_direction[2 * leg_id] * knee_angle, + targetVelocity=0) + self._pybullet_client.resetJointState(self.quadruped, + self._joint_name_to_id["motor_" + leg_position + + "R_joint"], + self._motor_direction[2 * leg_id + 1] * half_pi, + targetVelocity=0) + self._pybullet_client.resetJointState(self.quadruped, + self._joint_name_to_id["knee_" + leg_position + + "R_joint"], + self._motor_direction[2 * leg_id + 1] * knee_angle, + targetVelocity=0) if add_constraint: if leg_id < 2: self._pybullet_client.createConstraint( - self.quadruped, - self._joint_name_to_id["knee_" + leg_position + "R_joint"], - self.quadruped, - self._joint_name_to_id["knee_" + leg_position + "L_joint"], - self._pybullet_client.JOINT_POINT2POINT, [0, 0, 0], - KNEE_CONSTRAINT_POINT_SHORT, KNEE_CONSTRAINT_POINT_LONG) + self.quadruped, self._joint_name_to_id["knee_" + leg_position + "R_joint"], + self.quadruped, self._joint_name_to_id["knee_" + leg_position + "L_joint"], + self._pybullet_client.JOINT_POINT2POINT, [0, 0, 0], KNEE_CONSTRAINT_POINT_SHORT, + KNEE_CONSTRAINT_POINT_LONG) else: self._pybullet_client.createConstraint( - self.quadruped, - self._joint_name_to_id["knee_" + leg_position + "R_joint"], - self.quadruped, - self._joint_name_to_id["knee_" + leg_position + "L_joint"], - self._pybullet_client.JOINT_POINT2POINT, [0, 0, 0], - KNEE_CONSTRAINT_POINT_LONG, KNEE_CONSTRAINT_POINT_SHORT) + self.quadruped, self._joint_name_to_id["knee_" + leg_position + "R_joint"], + self.quadruped, self._joint_name_to_id["knee_" + leg_position + "L_joint"], + self._pybullet_client.JOINT_POINT2POINT, [0, 0, 0], KNEE_CONSTRAINT_POINT_LONG, + KNEE_CONSTRAINT_POINT_SHORT) if self._accurate_motor_model_enabled or self._pd_control_enabled: # Disable the default motor in pybullet. self._pybullet_client.setJointMotorControl2( bodyIndex=self.quadruped, - jointIndex=( - self._joint_name_to_id["motor_" + leg_position + "L_joint"]), + jointIndex=(self._joint_name_to_id["motor_" + leg_position + "L_joint"]), controlMode=self._pybullet_client.VELOCITY_CONTROL, targetVelocity=0, force=knee_friction_force) self._pybullet_client.setJointMotorControl2( bodyIndex=self.quadruped, - jointIndex=( - self._joint_name_to_id["motor_" + leg_position + "R_joint"]), + jointIndex=(self._joint_name_to_id["motor_" + leg_position + "R_joint"]), controlMode=self._pybullet_client.VELOCITY_CONTROL, targetVelocity=0, force=knee_friction_force) else: - self._SetDesiredMotorAngleByName( - "motor_" + leg_position + "L_joint", - self._motor_direction[2 * leg_id] * half_pi) - self._SetDesiredMotorAngleByName( - "motor_" + leg_position + "R_joint", - self._motor_direction[2 * leg_id + 1] * half_pi) + self._SetDesiredMotorAngleByName("motor_" + leg_position + "L_joint", + self._motor_direction[2 * leg_id] * half_pi) + self._SetDesiredMotorAngleByName("motor_" + leg_position + "R_joint", + self._motor_direction[2 * leg_id + 1] * half_pi) self._pybullet_client.setJointMotorControl2( bodyIndex=self.quadruped, diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_four_leg_stand_env.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_four_leg_stand_env.py index 0774f1710..1008f8062 100644 --- a/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_four_leg_stand_env.py +++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_four_leg_stand_env.py @@ -32,10 +32,7 @@ class MinitaurFourLegStandEnv(minitaur_gym_env.MinitaurGymEnv): action to zero and the height of the robot base. It prefers a similar pose to the signal while keeping balance. """ - metadata = { - "render.modes": ["human", "rgb_array"], - "video.frames_per_second": 166 - } + metadata = {"render.modes": ["human", "rgb_array"], "video.frames_per_second": 166} def __init__(self, urdf_version=None, @@ -101,23 +98,23 @@ class MinitaurFourLegStandEnv(minitaur_gym_env.MinitaurGymEnv): self._extension_offset = np.zeros(NUM_LEGS) self._use_angular_velocity_in_observation = use_motor_angle_in_observation self._use_motor_angle_in_observation = use_motor_angle_in_observation - super(MinitaurFourLegStandEnv, self).__init__( - urdf_version=urdf_version, - control_time_step=control_time_step, - action_repeat=action_repeat, - remove_default_joint_damping=remove_default_joint_damping, - accurate_motor_model_enabled=True, - motor_overheat_protection=True, - hard_reset=hard_reset, - motor_kp=motor_kp, - motor_kd=motor_kd, - control_latency=control_latency, - pd_latency=pd_latency, - on_rack=on_rack, - render=render, - env_randomizer=env_randomizer, - reflection = False, - log_path=log_path) + super(MinitaurFourLegStandEnv, + self).__init__(urdf_version=urdf_version, + control_time_step=control_time_step, + action_repeat=action_repeat, + remove_default_joint_damping=remove_default_joint_damping, + accurate_motor_model_enabled=True, + motor_overheat_protection=True, + hard_reset=hard_reset, + motor_kp=motor_kp, + motor_kd=motor_kd, + control_latency=control_latency, + pd_latency=pd_latency, + on_rack=on_rack, + render=render, + env_randomizer=env_randomizer, + reflection=False, + log_path=log_path) action_dim = 4 action_low = np.array([-1.0] * action_dim) @@ -138,20 +135,17 @@ class MinitaurFourLegStandEnv(minitaur_gym_env.MinitaurGymEnv): # [swing leg 1, swing leg 2, swing leg 3, swing leg 4, # extension leg 1, extension leg 2, extension leg 3, extension leg 4] init_pose = [ - INIT_SWING_POS + self._swing_offset[0], - INIT_SWING_POS + self._swing_offset[1], - INIT_SWING_POS + self._swing_offset[2], - INIT_SWING_POS + self._swing_offset[3], + INIT_SWING_POS + self._swing_offset[0], INIT_SWING_POS + self._swing_offset[1], + INIT_SWING_POS + self._swing_offset[2], INIT_SWING_POS + self._swing_offset[3], INIT_EXTENSION_POS + self._extension_offset[0], INIT_EXTENSION_POS + self._extension_offset[1], INIT_EXTENSION_POS + self._extension_offset[2], INIT_EXTENSION_POS + self._extension_offset[3] ] initial_motor_angles = self._convert_from_leg_model(init_pose) - self._pybullet_client.resetBasePositionAndOrientation( - 0, [0, 0, 0], [0, 0, 0, 1]) - super(MinitaurFourLegStandEnv, self).reset( - initial_motor_angles=initial_motor_angles, reset_duration=0.5) + self._pybullet_client.resetBasePositionAndOrientation(0, [0, 0, 0], [0, 0, 0, 1]) + super(MinitaurFourLegStandEnv, self).reset(initial_motor_angles=initial_motor_angles, + reset_duration=0.5) return self._get_observation() def step(self, action): @@ -180,10 +174,8 @@ class MinitaurFourLegStandEnv(minitaur_gym_env.MinitaurGymEnv): time.sleep(time_to_sleep) base_pos = self.minitaur.GetBasePosition() # Keep the previous orientation of the camera set by the user. - [yaw, pitch, - dist] = self._pybullet_client.getDebugVisualizerCamera()[8:11] - self._pybullet_client.resetDebugVisualizerCamera(dist, yaw, pitch, - base_pos) + [yaw, pitch, dist] = self._pybullet_client.getDebugVisualizerCamera()[8:11] + self._pybullet_client.resetDebugVisualizerCamera(dist, yaw, pitch, base_pos) action = self._transform_action_to_motor_command(action) t = self._env_step_counter % MOVING_FLOOR_TOTAL_STEP if t == 0: @@ -197,8 +189,8 @@ class MinitaurFourLegStandEnv(minitaur_gym_env.MinitaurGymEnv): t = float(float(t) / float(MOVING_FLOOR_TOTAL_STEP)) ori = map(operator.add, [x * (1.0 - t) for x in self._cur_ori], [x * t for x in self._goal_ori]) - ori=list(ori) - print("ori=",ori) + ori = list(ori) + print("ori=", ori) self._pybullet_client.resetBasePositionAndOrientation(0, [0, 0, 0], ori) if self._env_step_counter % PERTURBATION_TOTAL_STEP == 0: self._perturbation_magnitude = random.uniform(0.0, 0.0) @@ -218,8 +210,8 @@ class MinitaurFourLegStandEnv(minitaur_gym_env.MinitaurGymEnv): obs = self._get_true_observation() reward = self._reward() if self._log_path is not None: - minitaur_logging.update_episode_proto(self._episode_proto, self.minitaur, - action, self._env_step_counter) + minitaur_logging.update_episode_proto(self._episode_proto, self.minitaur, action, + self._env_step_counter) if done: self.minitaur.Terminate() return np.array(self._get_observation()), reward, done, {} @@ -228,15 +220,13 @@ class MinitaurFourLegStandEnv(minitaur_gym_env.MinitaurGymEnv): motor_pose = np.zeros(NUM_MOTORS) for i in range(NUM_LEGS): motor_pose[2 * i] = leg_pose[NUM_LEGS + i] - (-1)**(i / 2) * leg_pose[i] - motor_pose[2 * i - + 1] = leg_pose[NUM_LEGS + i] + (-1)**(i / 2) * leg_pose[i] + motor_pose[2 * i + 1] = leg_pose[NUM_LEGS + i] + (-1)**(i / 2) * leg_pose[i] return motor_pose def _signal(self, t): initial_pose = np.array([ - INIT_SWING_POS, INIT_SWING_POS, INIT_SWING_POS, INIT_SWING_POS, - INIT_EXTENSION_POS, INIT_EXTENSION_POS, INIT_EXTENSION_POS, - INIT_EXTENSION_POS + INIT_SWING_POS, INIT_SWING_POS, INIT_SWING_POS, INIT_SWING_POS, INIT_EXTENSION_POS, + INIT_EXTENSION_POS, INIT_EXTENSION_POS, INIT_EXTENSION_POS ]) signal = initial_pose return signal @@ -268,8 +258,7 @@ class MinitaurFourLegStandEnv(minitaur_gym_env.MinitaurGymEnv): rot_mat = self._pybullet_client.getMatrixFromQuaternion(orientation) local_up = rot_mat[6:] _, _, height = self.minitaur.GetBasePosition() - local_global_up_dot_product = np.dot( - np.asarray([0, 0, 1]), np.asarray(local_up)) + local_global_up_dot_product = np.dot(np.asarray([0, 0, 1]), np.asarray(local_up)) return local_global_up_dot_product < 0.85 or height < 0.15 def _reward(self): diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_four_leg_stand_env_example.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_four_leg_stand_env_example.py index c154e879a..183213fce 100644 --- a/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_four_leg_stand_env_example.py +++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_four_leg_stand_env_example.py @@ -5,7 +5,6 @@ import numpy as np import tensorflow as tf from pybullet_envs.minitaur.envs import minitaur_four_leg_stand_env - FLAGS = tf.flags.FLAGS tf.flags.DEFINE_string("log_path", None, "The directory to write the log file.") NUM_LEGS = 4 diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_gym_env.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_gym_env.py index 7553c33fa..44297d11b 100644 --- a/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_gym_env.py +++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_gym_env.py @@ -4,10 +4,10 @@ import math import time -import os, inspect +import os, inspect currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe()))) parentdir = os.path.dirname(os.path.dirname(currentdir)) -os.sys.path.insert(0,parentdir) +os.sys.path.insert(0, parentdir) import gym from gym import spaces @@ -64,10 +64,7 @@ class MinitaurGymEnv(gym.Env): expenditure. """ - metadata = { - "render.modes": ["human", "rgb_array"], - "video.frames_per_second": 100 - } + metadata = {"render.modes": ["human", "rgb_array"], "video.frames_per_second": 100} def __init__(self, urdf_root=pybullet_data.getDataPath(), @@ -184,17 +181,14 @@ class MinitaurGymEnv(gym.Env): self._action_repeat = 1 self.control_time_step = self._time_step * self._action_repeat # TODO(b/73829334): Fix the value of self._num_bullet_solver_iterations. - self._num_bullet_solver_iterations = int( - NUM_SIMULATION_ITERATION_STEPS / self._action_repeat) + self._num_bullet_solver_iterations = int(NUM_SIMULATION_ITERATION_STEPS / self._action_repeat) self._urdf_root = urdf_root self._self_collision_enabled = self_collision_enabled self._motor_velocity_limit = motor_velocity_limit self._observation = [] self._true_observation = [] self._objectives = [] - self._objective_weights = [ - distance_weight, energy_weight, drift_weight, shake_weight - ] + self._objective_weights = [distance_weight, energy_weight, drift_weight, shake_weight] self._env_step_counter = 0 self._num_steps_to_log = num_steps_to_log self._is_render = render @@ -226,12 +220,10 @@ class MinitaurGymEnv(gym.Env): self._urdf_version = urdf_version self._ground_id = None self._reflection = reflection - self._env_randomizers = convert_to_list( - env_randomizer) if env_randomizer else [] + self._env_randomizers = convert_to_list(env_randomizer) if env_randomizer else [] self._episode_proto = minitaur_logging_pb2.MinitaurEpisode() if self._is_render: - self._pybullet_client = bullet_client.BulletClient( - connection_mode=pybullet.GUI) + self._pybullet_client = bullet_client.BulletClient(connection_mode=pybullet.GUI) else: self._pybullet_client = bullet_client.BulletClient() if self._urdf_version is None: @@ -249,7 +241,7 @@ class MinitaurGymEnv(gym.Env): self._hard_reset = hard_reset # This assignment need to be after reset() def close(self): - if self._env_step_counter>0: + if self._env_step_counter > 0: self.logging.save_episode(self._episode_proto) self.minitaur.Terminate() @@ -257,53 +249,48 @@ class MinitaurGymEnv(gym.Env): self._env_randomizers.append(env_randomizer) def reset(self, initial_motor_angles=None, reset_duration=1.0): - self._pybullet_client.configureDebugVisualizer( - self._pybullet_client.COV_ENABLE_RENDERING, 0) - if self._env_step_counter>0: + self._pybullet_client.configureDebugVisualizer(self._pybullet_client.COV_ENABLE_RENDERING, 0) + if self._env_step_counter > 0: self.logging.save_episode(self._episode_proto) self._episode_proto = minitaur_logging_pb2.MinitaurEpisode() - minitaur_logging.preallocate_episode_proto(self._episode_proto, - self._num_steps_to_log) + minitaur_logging.preallocate_episode_proto(self._episode_proto, self._num_steps_to_log) if self._hard_reset: self._pybullet_client.resetSimulation() self._pybullet_client.setPhysicsEngineParameter( numSolverIterations=int(self._num_bullet_solver_iterations)) self._pybullet_client.setTimeStep(self._time_step) - self._ground_id = self._pybullet_client.loadURDF( - "%s/plane.urdf" % self._urdf_root) + self._ground_id = self._pybullet_client.loadURDF("%s/plane.urdf" % self._urdf_root) if (self._reflection): - self._pybullet_client.changeVisualShape(self._ground_id,-1,rgbaColor=[1,1,1,0.8]) - self._pybullet_client.configureDebugVisualizer(self._pybullet_client.COV_ENABLE_PLANAR_REFLECTION,self._ground_id) + self._pybullet_client.changeVisualShape(self._ground_id, -1, rgbaColor=[1, 1, 1, 0.8]) + self._pybullet_client.configureDebugVisualizer( + self._pybullet_client.COV_ENABLE_PLANAR_REFLECTION, self._ground_id) self._pybullet_client.setGravity(0, 0, -10) acc_motor = self._accurate_motor_model_enabled motor_protect = self._motor_overheat_protection if self._urdf_version not in MINIATUR_URDF_VERSION_MAP: - raise ValueError( - "%s is not a supported urdf_version." % self._urdf_version) + raise ValueError("%s is not a supported urdf_version." % self._urdf_version) else: - self.minitaur = ( - MINIATUR_URDF_VERSION_MAP[self._urdf_version]( - pybullet_client=self._pybullet_client, - action_repeat=self._action_repeat, - urdf_root=self._urdf_root, - time_step=self._time_step, - self_collision_enabled=self._self_collision_enabled, - motor_velocity_limit=self._motor_velocity_limit, - pd_control_enabled=self._pd_control_enabled, - accurate_motor_model_enabled=acc_motor, - remove_default_joint_damping=self._remove_default_joint_damping, - motor_kp=self._motor_kp, - motor_kd=self._motor_kd, - control_latency=self._control_latency, - pd_latency=self._pd_latency, - observation_noise_stdev=self._observation_noise_stdev, - torque_control_enabled=self._torque_control_enabled, - motor_overheat_protection=motor_protect, - on_rack=self._on_rack)) - self.minitaur.Reset( - reload_urdf=False, - default_motor_angles=initial_motor_angles, - reset_time=reset_duration) + self.minitaur = (MINIATUR_URDF_VERSION_MAP[self._urdf_version]( + pybullet_client=self._pybullet_client, + action_repeat=self._action_repeat, + urdf_root=self._urdf_root, + time_step=self._time_step, + self_collision_enabled=self._self_collision_enabled, + motor_velocity_limit=self._motor_velocity_limit, + pd_control_enabled=self._pd_control_enabled, + accurate_motor_model_enabled=acc_motor, + remove_default_joint_damping=self._remove_default_joint_damping, + motor_kp=self._motor_kp, + motor_kd=self._motor_kd, + control_latency=self._control_latency, + pd_latency=self._pd_latency, + observation_noise_stdev=self._observation_noise_stdev, + torque_control_enabled=self._torque_control_enabled, + motor_overheat_protection=motor_protect, + on_rack=self._on_rack)) + self.minitaur.Reset(reload_urdf=False, + default_motor_angles=initial_motor_angles, + reset_time=reset_duration) # Loop over all env randomizers. for env_randomizer in self._env_randomizers: @@ -313,10 +300,9 @@ class MinitaurGymEnv(gym.Env): self._env_step_counter = 0 self._last_base_position = [0, 0, 0] self._objectives = [] - self._pybullet_client.resetDebugVisualizerCamera( - self._cam_dist, self._cam_yaw, self._cam_pitch, [0, 0, 0]) - self._pybullet_client.configureDebugVisualizer( - self._pybullet_client.COV_ENABLE_RENDERING, 1) + self._pybullet_client.resetDebugVisualizerCamera(self._cam_dist, self._cam_yaw, + self._cam_pitch, [0, 0, 0]) + self._pybullet_client.configureDebugVisualizer(self._pybullet_client.COV_ENABLE_RENDERING, 1) return self._get_observation() def seed(self, seed=None): @@ -328,8 +314,7 @@ class MinitaurGymEnv(gym.Env): for i, action_component in enumerate(action): if not (-self._action_bound - ACTION_EPS <= action_component <= self._action_bound + ACTION_EPS): - raise ValueError("{}th action {} out of bounds.".format( - i, action_component)) + raise ValueError("{}th action {} out of bounds.".format(i, action_component)) action = self.minitaur.ConvertFromLegModel(action) return action @@ -361,10 +346,8 @@ class MinitaurGymEnv(gym.Env): time.sleep(time_to_sleep) base_pos = self.minitaur.GetBasePosition() # Keep the previous orientation of the camera set by the user. - [yaw, pitch, - dist] = self._pybullet_client.getDebugVisualizerCamera()[8:11] - self._pybullet_client.resetDebugVisualizerCamera(dist, yaw, pitch, - base_pos) + [yaw, pitch, dist] = self._pybullet_client.getDebugVisualizerCamera()[8:11] + self._pybullet_client.resetDebugVisualizerCamera(dist, yaw, pitch, base_pos) for env_randomizer in self._env_randomizers: env_randomizer.randomize_step(self) @@ -374,8 +357,8 @@ class MinitaurGymEnv(gym.Env): reward = self._reward() done = self._termination() if self._log_path is not None: - minitaur_logging.update_episode_proto(self._episode_proto, self.minitaur, - action, self._env_step_counter) + minitaur_logging.update_episode_proto(self._episode_proto, self.minitaur, action, + self._env_step_counter) self._env_step_counter += 1 if done: self.minitaur.Terminate() @@ -392,11 +375,11 @@ class MinitaurGymEnv(gym.Env): pitch=self._cam_pitch, roll=0, upAxisIndex=2) - proj_matrix = self._pybullet_client.computeProjectionMatrixFOV( - fov=60, - aspect=float(RENDER_WIDTH) / RENDER_HEIGHT, - nearVal=0.1, - farVal=100.0) + proj_matrix = self._pybullet_client.computeProjectionMatrixFOV(fov=60, + aspect=float(RENDER_WIDTH) / + RENDER_HEIGHT, + nearVal=0.1, + farVal=100.0) (_, _, px, _, _) = self._pybullet_client.getCameraImage( width=RENDER_WIDTH, height=RENDER_HEIGHT, @@ -413,9 +396,8 @@ class MinitaurGymEnv(gym.Env): Returns: A numpy array of motor angles. """ - return np.array( - self._observation[MOTOR_ANGLE_OBSERVATION_INDEX: - MOTOR_ANGLE_OBSERVATION_INDEX + NUM_MOTORS]) + return np.array(self._observation[MOTOR_ANGLE_OBSERVATION_INDEX:MOTOR_ANGLE_OBSERVATION_INDEX + + NUM_MOTORS]) def get_minitaur_motor_velocities(self): """Get the minitaur's motor velocities. @@ -424,8 +406,8 @@ class MinitaurGymEnv(gym.Env): A numpy array of motor velocities. """ return np.array( - self._observation[MOTOR_VELOCITY_OBSERVATION_INDEX: - MOTOR_VELOCITY_OBSERVATION_INDEX + NUM_MOTORS]) + self._observation[MOTOR_VELOCITY_OBSERVATION_INDEX:MOTOR_VELOCITY_OBSERVATION_INDEX + + NUM_MOTORS]) def get_minitaur_motor_torques(self): """Get the minitaur's motor torques. @@ -434,8 +416,8 @@ class MinitaurGymEnv(gym.Env): A numpy array of motor torques. """ return np.array( - self._observation[MOTOR_TORQUE_OBSERVATION_INDEX: - MOTOR_TORQUE_OBSERVATION_INDEX + NUM_MOTORS]) + self._observation[MOTOR_TORQUE_OBSERVATION_INDEX:MOTOR_TORQUE_OBSERVATION_INDEX + + NUM_MOTORS]) def get_minitaur_base_orientation(self): """Get the minitaur's base orientation, represented by a quaternion. @@ -459,8 +441,7 @@ class MinitaurGymEnv(gym.Env): rot_mat = self._pybullet_client.getMatrixFromQuaternion(orientation) local_up = rot_mat[6:] pos = self.minitaur.GetBasePosition() - return (np.dot(np.asarray([0, 0, 1]), np.asarray(local_up)) < 0.85 or - pos[2] < 0.13) + return (np.dot(np.asarray([0, 0, 1]), np.asarray(local_up)) < 0.85 or pos[2] < 0.13) def _termination(self): position = self.minitaur.GetBasePosition() @@ -483,9 +464,7 @@ class MinitaurGymEnv(gym.Env): np.dot(self.minitaur.GetMotorTorques(), self.minitaur.GetMotorVelocities())) * self._time_step objectives = [forward_reward, energy_reward, drift_reward, shake_reward] - weighted_objectives = [ - o * w for o, w in zip(objectives, self._objective_weights) - ] + weighted_objectives = [o * w for o, w in zip(objectives, self._objective_weights)] reward = sum(weighted_objectives) self._objectives.append(objectives) return reward @@ -552,10 +531,8 @@ class MinitaurGymEnv(gym.Env): upper_bound = np.zeros(self._get_observation_dimension()) num_motors = self.minitaur.num_motors upper_bound[0:num_motors] = math.pi # Joint angle. - upper_bound[num_motors:2 * num_motors] = ( - motor.MOTOR_SPEED_LIMIT) # Joint velocity. - upper_bound[2 * num_motors:3 * num_motors] = ( - motor.OBSERVED_TORQUE_LIMIT) # Joint torque. + upper_bound[num_motors:2 * num_motors] = (motor.MOTOR_SPEED_LIMIT) # Joint velocity. + upper_bound[2 * num_motors:3 * num_motors] = (motor.OBSERVED_TORQUE_LIMIT) # Joint torque. upper_bound[3 * num_motors:] = 1.0 # Quaternion of base orientation. return upper_bound @@ -577,7 +554,6 @@ class MinitaurGymEnv(gym.Env): _seed = seed _step = step - def set_time_step(self, control_step, simulation_step=0.001): """Sets the time step of the environment. @@ -591,18 +567,15 @@ class MinitaurGymEnv(gym.Env): ValueError: If the control step is smaller than the simulation step. """ if control_step < simulation_step: - raise ValueError( - "Control step should be larger than or equal to simulation step.") + raise ValueError("Control step should be larger than or equal to simulation step.") self.control_time_step = control_step self._time_step = simulation_step self._action_repeat = int(round(control_step / simulation_step)) - self._num_bullet_solver_iterations = ( - NUM_SIMULATION_ITERATION_STEPS / self._action_repeat) + self._num_bullet_solver_iterations = (NUM_SIMULATION_ITERATION_STEPS / self._action_repeat) self._pybullet_client.setPhysicsEngineParameter( numSolverIterations=self._num_bullet_solver_iterations) self._pybullet_client.setTimeStep(self._time_step) - self.minitaur.SetTimeSteps( - action_repeat=self._action_repeat, simulation_step=self._time_step) + self.minitaur.SetTimeSteps(action_repeat=self._action_repeat, simulation_step=self._time_step) @property def pybullet_client(self): diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_gym_env_example.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_gym_env_example.py index c05022648..8441879ad 100644 --- a/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_gym_env_example.py +++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_gym_env_example.py @@ -8,8 +8,8 @@ import os import inspect currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe()))) parentdir = os.path.dirname(os.path.dirname(os.path.dirname(currentdir))) -print("parentdir=",parentdir) -os.sys.path.insert(0,parentdir) +print("parentdir=", parentdir) +os.sys.path.insert(0, parentdir) import argparse import numpy as np @@ -17,7 +17,6 @@ import tensorflow as tf from pybullet_envs.minitaur.envs import minitaur_gym_env import time - #FLAGS = flags.FLAGS #flags.DEFINE_enum( # "example_name", "sine", ["sine", "reset", "stand", "overheat"], @@ -60,7 +59,7 @@ def ResetPoseExample(log_path=None): action = [math.pi / 2] * 8 for _ in range(steps): _, _, done, _ = environment.step(action) - time.sleep(1./100.) + time.sleep(1. / 100.) if done: break @@ -104,7 +103,7 @@ def MotorOverheatExample(log_path=None): observation, _, _, _ = environment.step(action) current_row.extend(observation.tolist()) actions_and_observations.append(current_row) - time.sleep(1./100.) + time.sleep(1. / 100.) if FLAGS.output_filename is not None: WriteToCSV(FLAGS.output_filename, actions_and_observations) @@ -151,7 +150,7 @@ def SineStandExample(log_path=None): observation, _, _, _ = environment.step(action) current_row.extend(observation.tolist()) actions_and_observations.append(current_row) - time.sleep(1./100.) + time.sleep(1. / 100.) if FLAGS.output_filename is not None: WriteToCSV(FLAGS.output_filename, actions_and_observations) @@ -199,7 +198,7 @@ def SinePolicyExample(log_path=None): a4 = math.sin(t * speed + math.pi) * amplitude2 action = [a1, a2, a2, a1, a3, a4, a4, a3] _, reward, done, _ = environment.step(action) - time.sleep(1./100.) + time.sleep(1. / 100.) sum_reward += reward if done: @@ -207,14 +206,15 @@ def SinePolicyExample(log_path=None): environment.reset() - - def main(): parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter) - parser.add_argument('--env', help='environment ID (0==sine, 1==stand, 2=reset, 3=overheat)',type=int, default=0) + parser.add_argument('--env', + help='environment ID (0==sine, 1==stand, 2=reset, 3=overheat)', + type=int, + default=0) args = parser.parse_args() print("--env=" + str(args.env)) - + if (args.env == 0): SinePolicyExample() if (args.env == 1): @@ -224,6 +224,6 @@ def main(): if (args.env == 3): MotorOverheatExample() -if __name__ == '__main__': - main() +if __name__ == '__main__': + main() diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_logging.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_logging.py index c40594042..e8467a5ae 100644 --- a/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_logging.py +++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_logging.py @@ -10,10 +10,10 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -import os, inspect +import os, inspect currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe()))) parentdir = os.path.dirname(os.path.dirname(currentdir)) -os.sys.path.insert(0,parentdir) +os.sys.path.insert(0, parentdir) import datetime import os @@ -74,9 +74,8 @@ def update_episode_proto(episode_proto, minitaur, action, step): """ max_num_steps = len(episode_proto.state_action) if step >= max_num_steps: - tf.logging.warning( - "{}th step is not recorded in the logging since only {} steps were " - "pre-allocated.".format(step, max_num_steps)) + tf.logging.warning("{}th step is not recorded in the logging since only {} steps were " + "pre-allocated.".format(step, max_num_steps)) return step_log = episode_proto.state_action[step] step_log.info_valid = minitaur.IsObservationValid() @@ -95,8 +94,7 @@ def update_episode_proto(episode_proto, minitaur, action, step): _update_base_state(step_log.base_position, minitaur.GetBasePosition()) _update_base_state(step_log.base_orientation, minitaur.GetBaseRollPitchYaw()) - _update_base_state(step_log.base_angular_vel, - minitaur.GetBaseRollPitchYawRate()) + _update_base_state(step_log.base_angular_vel, minitaur.GetBaseRollPitchYawRate()) class MinitaurLogging(object): @@ -124,10 +122,8 @@ class MinitaurLogging(object): if not tf.gfile.Exists(self._log_path): tf.gfile.MakeDirs(self._log_path) ts = time.time() - time_stamp = datetime.datetime.fromtimestamp(ts).strftime( - "%Y-%m-%d-%H%M%S") - log_path = os.path.join(self._log_path, - "minitaur_log_{}".format(time_stamp)) + time_stamp = datetime.datetime.fromtimestamp(ts).strftime("%Y-%m-%d-%H%M%S") + log_path = os.path.join(self._log_path, "minitaur_log_{}".format(time_stamp)) with tf.gfile.Open(log_path, "w") as f: f.write(episode_proto.SerializeToString()) return log_path diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_logging_pb2.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_logging_pb2.py index ff564386a..9650c4b24 100644 --- a/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_logging_pb2.py +++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_logging_pb2.py @@ -3,12 +3,12 @@ import sys -import os, inspect +import os, inspect currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe()))) parentdir = os.path.dirname(os.path.dirname(currentdir)) -os.sys.path.insert(0,parentdir) +os.sys.path.insert(0, parentdir) -_b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1')) +_b = sys.version_info[0] < 3 and (lambda x: x) or (lambda x: x.encode('latin1')) from google.protobuf import descriptor as _descriptor from google.protobuf import message as _message from google.protobuf import reflection as _reflection @@ -18,168 +18,271 @@ from google.protobuf import descriptor_pb2 _sym_db = _symbol_database.Default() - from pybullet_envs.minitaur.envs import timestamp_pb2 as timestamp__pb2 from pybullet_envs.minitaur.envs import vector_pb2 as vector__pb2 - DESCRIPTOR = _descriptor.FileDescriptor( - name='minitaur_logging.proto', - package='robotics.reinforcement_learning.minitaur.envs', - syntax='proto3', - serialized_pb=_b('\n\x16minitaur_logging.proto\x12-robotics.reinforcement_learning.minitaur.envs\x1a\x0ftimestamp.proto\x1a\x0cvector.proto\"k\n\x0fMinitaurEpisode\x12X\n\x0cstate_action\x18\x01 \x03(\x0b\x32\x42.robotics.reinforcement_learning.minitaur.envs.MinitaurStateAction\"U\n\x12MinitaurMotorState\x12\r\n\x05\x61ngle\x18\x01 \x01(\x01\x12\x10\n\x08velocity\x18\x02 \x01(\x01\x12\x0e\n\x06torque\x18\x03 \x01(\x01\x12\x0e\n\x06\x61\x63tion\x18\x04 \x01(\x01\"\xce\x02\n\x13MinitaurStateAction\x12\x12\n\ninfo_valid\x18\x06 \x01(\x08\x12(\n\x04time\x18\x01 \x01(\x0b\x32\x1a.google.protobuf.Timestamp\x12\x32\n\rbase_position\x18\x02 \x01(\x0b\x32\x1b.robotics.messages.Vector3d\x12\x35\n\x10\x62\x61se_orientation\x18\x03 \x01(\x0b\x32\x1b.robotics.messages.Vector3d\x12\x35\n\x10\x62\x61se_angular_vel\x18\x04 \x01(\x0b\x32\x1b.robotics.messages.Vector3d\x12W\n\x0cmotor_states\x18\x05 \x03(\x0b\x32\x41.robotics.reinforcement_learning.minitaur.envs.MinitaurMotorStateb\x06proto3') - , - dependencies=[timestamp__pb2.DESCRIPTOR,vector__pb2.DESCRIPTOR,]) - - - + name='minitaur_logging.proto', + package='robotics.reinforcement_learning.minitaur.envs', + syntax='proto3', + serialized_pb=_b( + '\n\x16minitaur_logging.proto\x12-robotics.reinforcement_learning.minitaur.envs\x1a\x0ftimestamp.proto\x1a\x0cvector.proto\"k\n\x0fMinitaurEpisode\x12X\n\x0cstate_action\x18\x01 \x03(\x0b\x32\x42.robotics.reinforcement_learning.minitaur.envs.MinitaurStateAction\"U\n\x12MinitaurMotorState\x12\r\n\x05\x61ngle\x18\x01 \x01(\x01\x12\x10\n\x08velocity\x18\x02 \x01(\x01\x12\x0e\n\x06torque\x18\x03 \x01(\x01\x12\x0e\n\x06\x61\x63tion\x18\x04 \x01(\x01\"\xce\x02\n\x13MinitaurStateAction\x12\x12\n\ninfo_valid\x18\x06 \x01(\x08\x12(\n\x04time\x18\x01 \x01(\x0b\x32\x1a.google.protobuf.Timestamp\x12\x32\n\rbase_position\x18\x02 \x01(\x0b\x32\x1b.robotics.messages.Vector3d\x12\x35\n\x10\x62\x61se_orientation\x18\x03 \x01(\x0b\x32\x1b.robotics.messages.Vector3d\x12\x35\n\x10\x62\x61se_angular_vel\x18\x04 \x01(\x0b\x32\x1b.robotics.messages.Vector3d\x12W\n\x0cmotor_states\x18\x05 \x03(\x0b\x32\x41.robotics.reinforcement_learning.minitaur.envs.MinitaurMotorStateb\x06proto3' + ), + dependencies=[ + timestamp__pb2.DESCRIPTOR, + vector__pb2.DESCRIPTOR, + ]) _MINITAUREPISODE = _descriptor.Descriptor( - name='MinitaurEpisode', - full_name='robotics.reinforcement_learning.minitaur.envs.MinitaurEpisode', - filename=None, - file=DESCRIPTOR, - containing_type=None, - fields=[ - _descriptor.FieldDescriptor( - name='state_action', full_name='robotics.reinforcement_learning.minitaur.envs.MinitaurEpisode.state_action', index=0, - number=1, type=11, cpp_type=10, label=3, - has_default_value=False, default_value=[], - message_type=None, enum_type=None, containing_type=None, - is_extension=False, extension_scope=None, - options=None, file=DESCRIPTOR), - ], - extensions=[ - ], - nested_types=[], - enum_types=[ - ], - options=None, - is_extendable=False, - syntax='proto3', - extension_ranges=[], - oneofs=[ - ], - serialized_start=104, - serialized_end=211, + name='MinitaurEpisode', + full_name='robotics.reinforcement_learning.minitaur.envs.MinitaurEpisode', + filename=None, + file=DESCRIPTOR, + containing_type=None, + fields=[ + _descriptor.FieldDescriptor( + name='state_action', + full_name='robotics.reinforcement_learning.minitaur.envs.MinitaurEpisode.state_action', + index=0, + number=1, + type=11, + cpp_type=10, + label=3, + has_default_value=False, + default_value=[], + message_type=None, + enum_type=None, + containing_type=None, + is_extension=False, + extension_scope=None, + options=None, + file=DESCRIPTOR), + ], + extensions=[], + nested_types=[], + enum_types=[], + options=None, + is_extendable=False, + syntax='proto3', + extension_ranges=[], + oneofs=[], + serialized_start=104, + serialized_end=211, ) - _MINITAURMOTORSTATE = _descriptor.Descriptor( - name='MinitaurMotorState', - full_name='robotics.reinforcement_learning.minitaur.envs.MinitaurMotorState', - filename=None, - file=DESCRIPTOR, - containing_type=None, - fields=[ - _descriptor.FieldDescriptor( - name='angle', full_name='robotics.reinforcement_learning.minitaur.envs.MinitaurMotorState.angle', index=0, - number=1, type=1, cpp_type=5, label=1, - has_default_value=False, default_value=float(0), - message_type=None, enum_type=None, containing_type=None, - is_extension=False, extension_scope=None, - options=None, file=DESCRIPTOR), - _descriptor.FieldDescriptor( - name='velocity', full_name='robotics.reinforcement_learning.minitaur.envs.MinitaurMotorState.velocity', index=1, - number=2, type=1, cpp_type=5, label=1, - has_default_value=False, default_value=float(0), - message_type=None, enum_type=None, containing_type=None, - is_extension=False, extension_scope=None, - options=None, file=DESCRIPTOR), - _descriptor.FieldDescriptor( - name='torque', full_name='robotics.reinforcement_learning.minitaur.envs.MinitaurMotorState.torque', index=2, - number=3, type=1, cpp_type=5, label=1, - has_default_value=False, default_value=float(0), - message_type=None, enum_type=None, containing_type=None, - is_extension=False, extension_scope=None, - options=None, file=DESCRIPTOR), - _descriptor.FieldDescriptor( - name='action', full_name='robotics.reinforcement_learning.minitaur.envs.MinitaurMotorState.action', index=3, - number=4, type=1, cpp_type=5, label=1, - has_default_value=False, default_value=float(0), - message_type=None, enum_type=None, containing_type=None, - is_extension=False, extension_scope=None, - options=None, file=DESCRIPTOR), - ], - extensions=[ - ], - nested_types=[], - enum_types=[ - ], - options=None, - is_extendable=False, - syntax='proto3', - extension_ranges=[], - oneofs=[ - ], - serialized_start=213, - serialized_end=298, + name='MinitaurMotorState', + full_name='robotics.reinforcement_learning.minitaur.envs.MinitaurMotorState', + filename=None, + file=DESCRIPTOR, + containing_type=None, + fields=[ + _descriptor.FieldDescriptor( + name='angle', + full_name='robotics.reinforcement_learning.minitaur.envs.MinitaurMotorState.angle', + index=0, + number=1, + type=1, + cpp_type=5, + label=1, + has_default_value=False, + default_value=float(0), + message_type=None, + enum_type=None, + containing_type=None, + is_extension=False, + extension_scope=None, + options=None, + file=DESCRIPTOR), + _descriptor.FieldDescriptor( + name='velocity', + full_name='robotics.reinforcement_learning.minitaur.envs.MinitaurMotorState.velocity', + index=1, + number=2, + type=1, + cpp_type=5, + label=1, + has_default_value=False, + default_value=float(0), + message_type=None, + enum_type=None, + containing_type=None, + is_extension=False, + extension_scope=None, + options=None, + file=DESCRIPTOR), + _descriptor.FieldDescriptor( + name='torque', + full_name='robotics.reinforcement_learning.minitaur.envs.MinitaurMotorState.torque', + index=2, + number=3, + type=1, + cpp_type=5, + label=1, + has_default_value=False, + default_value=float(0), + message_type=None, + enum_type=None, + containing_type=None, + is_extension=False, + extension_scope=None, + options=None, + file=DESCRIPTOR), + _descriptor.FieldDescriptor( + name='action', + full_name='robotics.reinforcement_learning.minitaur.envs.MinitaurMotorState.action', + index=3, + number=4, + type=1, + cpp_type=5, + label=1, + has_default_value=False, + default_value=float(0), + message_type=None, + enum_type=None, + containing_type=None, + is_extension=False, + extension_scope=None, + options=None, + file=DESCRIPTOR), + ], + extensions=[], + nested_types=[], + enum_types=[], + options=None, + is_extendable=False, + syntax='proto3', + extension_ranges=[], + oneofs=[], + serialized_start=213, + serialized_end=298, ) - _MINITAURSTATEACTION = _descriptor.Descriptor( - name='MinitaurStateAction', - full_name='robotics.reinforcement_learning.minitaur.envs.MinitaurStateAction', - filename=None, - file=DESCRIPTOR, - containing_type=None, - fields=[ - _descriptor.FieldDescriptor( - name='info_valid', full_name='robotics.reinforcement_learning.minitaur.envs.MinitaurStateAction.info_valid', index=0, - number=6, type=8, cpp_type=7, label=1, - has_default_value=False, default_value=False, - message_type=None, enum_type=None, containing_type=None, - is_extension=False, extension_scope=None, - options=None, file=DESCRIPTOR), - _descriptor.FieldDescriptor( - name='time', full_name='robotics.reinforcement_learning.minitaur.envs.MinitaurStateAction.time', index=1, - number=1, type=11, cpp_type=10, label=1, - has_default_value=False, default_value=None, - message_type=None, enum_type=None, containing_type=None, - is_extension=False, extension_scope=None, - options=None, file=DESCRIPTOR), - _descriptor.FieldDescriptor( - name='base_position', full_name='robotics.reinforcement_learning.minitaur.envs.MinitaurStateAction.base_position', index=2, - number=2, type=11, cpp_type=10, label=1, - has_default_value=False, default_value=None, - message_type=None, enum_type=None, containing_type=None, - is_extension=False, extension_scope=None, - options=None, file=DESCRIPTOR), - _descriptor.FieldDescriptor( - name='base_orientation', full_name='robotics.reinforcement_learning.minitaur.envs.MinitaurStateAction.base_orientation', index=3, - number=3, type=11, cpp_type=10, label=1, - has_default_value=False, default_value=None, - message_type=None, enum_type=None, containing_type=None, - is_extension=False, extension_scope=None, - options=None, file=DESCRIPTOR), - _descriptor.FieldDescriptor( - name='base_angular_vel', full_name='robotics.reinforcement_learning.minitaur.envs.MinitaurStateAction.base_angular_vel', index=4, - number=4, type=11, cpp_type=10, label=1, - has_default_value=False, default_value=None, - message_type=None, enum_type=None, containing_type=None, - is_extension=False, extension_scope=None, - options=None, file=DESCRIPTOR), - _descriptor.FieldDescriptor( - name='motor_states', full_name='robotics.reinforcement_learning.minitaur.envs.MinitaurStateAction.motor_states', index=5, - number=5, type=11, cpp_type=10, label=3, - has_default_value=False, default_value=[], - message_type=None, enum_type=None, containing_type=None, - is_extension=False, extension_scope=None, - options=None, file=DESCRIPTOR), - ], - extensions=[ - ], - nested_types=[], - enum_types=[ - ], - options=None, - is_extendable=False, - syntax='proto3', - extension_ranges=[], - oneofs=[ - ], - serialized_start=301, - serialized_end=635, + name='MinitaurStateAction', + full_name='robotics.reinforcement_learning.minitaur.envs.MinitaurStateAction', + filename=None, + file=DESCRIPTOR, + containing_type=None, + fields=[ + _descriptor.FieldDescriptor( + name='info_valid', + full_name= + 'robotics.reinforcement_learning.minitaur.envs.MinitaurStateAction.info_valid', + index=0, + number=6, + type=8, + cpp_type=7, + label=1, + has_default_value=False, + default_value=False, + message_type=None, + enum_type=None, + containing_type=None, + is_extension=False, + extension_scope=None, + options=None, + file=DESCRIPTOR), + _descriptor.FieldDescriptor( + name='time', + full_name='robotics.reinforcement_learning.minitaur.envs.MinitaurStateAction.time', + index=1, + number=1, + type=11, + cpp_type=10, + label=1, + has_default_value=False, + default_value=None, + message_type=None, + enum_type=None, + containing_type=None, + is_extension=False, + extension_scope=None, + options=None, + file=DESCRIPTOR), + _descriptor.FieldDescriptor( + name='base_position', + full_name= + 'robotics.reinforcement_learning.minitaur.envs.MinitaurStateAction.base_position', + index=2, + number=2, + type=11, + cpp_type=10, + label=1, + has_default_value=False, + default_value=None, + message_type=None, + enum_type=None, + containing_type=None, + is_extension=False, + extension_scope=None, + options=None, + file=DESCRIPTOR), + _descriptor.FieldDescriptor( + name='base_orientation', + full_name= + 'robotics.reinforcement_learning.minitaur.envs.MinitaurStateAction.base_orientation', + index=3, + number=3, + type=11, + cpp_type=10, + label=1, + has_default_value=False, + default_value=None, + message_type=None, + enum_type=None, + containing_type=None, + is_extension=False, + extension_scope=None, + options=None, + file=DESCRIPTOR), + _descriptor.FieldDescriptor( + name='base_angular_vel', + full_name= + 'robotics.reinforcement_learning.minitaur.envs.MinitaurStateAction.base_angular_vel', + index=4, + number=4, + type=11, + cpp_type=10, + label=1, + has_default_value=False, + default_value=None, + message_type=None, + enum_type=None, + containing_type=None, + is_extension=False, + extension_scope=None, + options=None, + file=DESCRIPTOR), + _descriptor.FieldDescriptor( + name='motor_states', + full_name= + 'robotics.reinforcement_learning.minitaur.envs.MinitaurStateAction.motor_states', + index=5, + number=5, + type=11, + cpp_type=10, + label=3, + has_default_value=False, + default_value=[], + message_type=None, + enum_type=None, + containing_type=None, + is_extension=False, + extension_scope=None, + options=None, + file=DESCRIPTOR), + ], + extensions=[], + nested_types=[], + enum_types=[], + options=None, + is_extendable=False, + syntax='proto3', + extension_ranges=[], + oneofs=[], + serialized_start=301, + serialized_end=635, ) _MINITAUREPISODE.fields_by_name['state_action'].message_type = _MINITAURSTATEACTION @@ -193,26 +296,34 @@ DESCRIPTOR.message_types_by_name['MinitaurMotorState'] = _MINITAURMOTORSTATE DESCRIPTOR.message_types_by_name['MinitaurStateAction'] = _MINITAURSTATEACTION _sym_db.RegisterFileDescriptor(DESCRIPTOR) -MinitaurEpisode = _reflection.GeneratedProtocolMessageType('MinitaurEpisode', (_message.Message,), dict( - DESCRIPTOR = _MINITAUREPISODE, - __module__ = 'minitaur_logging_pb2' - # @@protoc_insertion_point(class_scope:robotics.reinforcement_learning.minitaur.envs.MinitaurEpisode) - )) +MinitaurEpisode = _reflection.GeneratedProtocolMessageType( + 'MinitaurEpisode', + (_message.Message,), + dict( + DESCRIPTOR=_MINITAUREPISODE, + __module__='minitaur_logging_pb2' + # @@protoc_insertion_point(class_scope:robotics.reinforcement_learning.minitaur.envs.MinitaurEpisode) + )) _sym_db.RegisterMessage(MinitaurEpisode) -MinitaurMotorState = _reflection.GeneratedProtocolMessageType('MinitaurMotorState', (_message.Message,), dict( - DESCRIPTOR = _MINITAURMOTORSTATE, - __module__ = 'minitaur_logging_pb2' - # @@protoc_insertion_point(class_scope:robotics.reinforcement_learning.minitaur.envs.MinitaurMotorState) - )) +MinitaurMotorState = _reflection.GeneratedProtocolMessageType( + 'MinitaurMotorState', + (_message.Message,), + dict( + DESCRIPTOR=_MINITAURMOTORSTATE, + __module__='minitaur_logging_pb2' + # @@protoc_insertion_point(class_scope:robotics.reinforcement_learning.minitaur.envs.MinitaurMotorState) + )) _sym_db.RegisterMessage(MinitaurMotorState) -MinitaurStateAction = _reflection.GeneratedProtocolMessageType('MinitaurStateAction', (_message.Message,), dict( - DESCRIPTOR = _MINITAURSTATEACTION, - __module__ = 'minitaur_logging_pb2' - # @@protoc_insertion_point(class_scope:robotics.reinforcement_learning.minitaur.envs.MinitaurStateAction) - )) +MinitaurStateAction = _reflection.GeneratedProtocolMessageType( + 'MinitaurStateAction', + (_message.Message,), + dict( + DESCRIPTOR=_MINITAURSTATEACTION, + __module__='minitaur_logging_pb2' + # @@protoc_insertion_point(class_scope:robotics.reinforcement_learning.minitaur.envs.MinitaurStateAction) + )) _sym_db.RegisterMessage(MinitaurStateAction) - # @@protoc_insertion_point(module_scope) diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_proto_dump_example.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_proto_dump_example.py index ca0534ad5..f194eb268 100644 --- a/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_proto_dump_example.py +++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_proto_dump_example.py @@ -15,13 +15,13 @@ fields = episode.ListFields() recs = [] for rec in fields[0][1]: - #print(rec.time) - for motorState in rec.motor_states: - #print("motorState.angle=",motorState.angle) - #print("motorState.velocity=",motorState.velocity) - #print("motorState.action=",motorState.action) - #print("motorState.torque=",motorState.torque) - recs.append([motorState.angle,motorState.velocity,motorState.action,motorState.torque]) - + #print(rec.time) + for motorState in rec.motor_states: + #print("motorState.angle=",motorState.angle) + #print("motorState.velocity=",motorState.velocity) + #print("motorState.action=",motorState.action) + #print("motorState.torque=",motorState.torque) + recs.append([motorState.angle, motorState.velocity, motorState.action, motorState.torque]) + a = numpy.array(recs) -numpy.savetxt("motorq_qdot_action_torque.csv", a, delimiter=",")
\ No newline at end of file +numpy.savetxt("motorq_qdot_action_torque.csv", a, delimiter=",") diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_raibert_controller.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_raibert_controller.py index dcecf26a6..7fb43cbca 100644 --- a/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_raibert_controller.py +++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_raibert_controller.py @@ -17,8 +17,8 @@ DIAGONAL_LEG_PAIR_2 = (1, 2) class BehaviorParameters( collections.namedtuple("BehaviorParameters", [ - "stance_duration", "desired_forward_speed", "turning_speed", - "standing_height", "desired_incline_angle" + "stance_duration", "desired_forward_speed", "turning_speed", "standing_height", + "desired_incline_angle" ])): __slots__ = () @@ -28,18 +28,16 @@ class BehaviorParameters( turning_speed=0, standing_height=0.21, desired_incline_angle=0): - return super(BehaviorParameters, cls).__new__( - cls, stance_duration, desired_forward_speed, turning_speed, - standing_height, desired_incline_angle) + return super(BehaviorParameters, + cls).__new__(cls, stance_duration, desired_forward_speed, turning_speed, + standing_height, desired_incline_angle) def motor_angles_to_leg_pose(motor_angles): leg_pose = np.zeros(_NUM_MOTORS) for i in range(_NUM_LEGS): - leg_pose[i] = 0.5 * (-1)**(i // 2) * ( - motor_angles[2 * i + 1] - motor_angles[2 * i]) - leg_pose[_NUM_LEGS + i] = 0.5 * ( - motor_angles[2 * i] + motor_angles[2 * i + 1]) + leg_pose[i] = 0.5 * (-1)**(i // 2) * (motor_angles[2 * i + 1] - motor_angles[2 * i]) + leg_pose[_NUM_LEGS + i] = 0.5 * (motor_angles[2 * i] + motor_angles[2 * i + 1]) return leg_pose @@ -47,8 +45,7 @@ def leg_pose_to_motor_angles(leg_pose): motor_pose = np.zeros(_NUM_MOTORS) for i in range(_NUM_LEGS): motor_pose[2 * i] = leg_pose[_NUM_LEGS + i] - (-1)**(i // 2) * leg_pose[i] - motor_pose[2 * i + 1] = ( - leg_pose[_NUM_LEGS + i] + (-1)**(i // 2) * leg_pose[i]) + motor_pose[2 * i + 1] = (leg_pose[_NUM_LEGS + i] + (-1)**(i // 2) * leg_pose[i]) return motor_pose @@ -85,8 +82,7 @@ def foot_position_to_leg_pose(foot_position): ext = math.acos(cos_ext) hip_toe = math.sqrt(hip_toe_sqr) - cos_theta = (hip_toe_sqr + hip_ankle_sqr - - (l3 - l2)**2) / (2 * hip_ankle * hip_toe) + cos_theta = (hip_toe_sqr + hip_ankle_sqr - (l3 - l2)**2) / (2 * hip_ankle * hip_toe) assert cos_theta > 0 theta = math.acos(cos_theta) @@ -94,8 +90,7 @@ def foot_position_to_leg_pose(foot_position): return (-sw, ext) -def foot_horizontal_position_to_leg_swing(foot_horizontal_position, - leg_extension): +def foot_horizontal_position_to_leg_swing(foot_horizontal_position, leg_extension): """Computes the target leg swing. Sometimes it is more convenient to plan in the hybrid space. @@ -119,8 +114,7 @@ def foot_horizontal_position_to_leg_swing(foot_horizontal_position, # Cap the foot horizontal (projected) position so the target leg pose is # always feasible. - foot_position = max( - min(toe_hip_len * 0.8, foot_horizontal_position), -toe_hip_len * 0.5) + foot_position = max(min(toe_hip_len * 0.8, foot_horizontal_position), -toe_hip_len * 0.5) sw_and_theta = math.asin(foot_position / toe_hip_len) @@ -171,9 +165,7 @@ def generate_swing_trajectory(phase, init_pose, end_pose): b = (phi * phi * delta_2 - delta_1) / delta_p - delta = ( - a * normalized_phase * normalized_phase + b * normalized_phase + - init_delta) + delta = (a * normalized_phase * normalized_phase + b * normalized_phase + init_delta) l1 = _UPPER_LEG_LEN l2 = _LOWER_SHORT_LEG_LEN @@ -209,10 +201,9 @@ class RaibertSwingLegController(object): leg_pose_set = [] for i in raibiert_controller.swing_set: target_foot_horizontal_position = ( - raibiert_controller.behavior_parameters.stance_duration / 2 * - current_speed + self._speed_gain * - (current_speed - - raibiert_controller.behavior_parameters.desired_forward_speed)) + raibiert_controller.behavior_parameters.stance_duration / 2 * current_speed + + self._speed_gain * + (current_speed - raibiert_controller.behavior_parameters.desired_forward_speed)) # Use the swing phase [0, 1] to track the foot. The idea is # straightforward: @@ -221,19 +212,19 @@ class RaibertSwingLegController(object): # 3) Find the next leg pose on the curve based on how much time left. # 1) Convert the target foot - target_leg_extension = ( - raibiert_controller.nominal_leg_extension - - self._leg_extension_clearance) - target_leg_swing = foot_horizontal_position_to_leg_swing( - target_foot_horizontal_position, leg_extension=target_leg_extension) + target_leg_extension = (raibiert_controller.nominal_leg_extension - + self._leg_extension_clearance) + target_leg_swing = foot_horizontal_position_to_leg_swing(target_foot_horizontal_position, + leg_extension=target_leg_extension) target_leg_pose = (target_leg_swing, target_leg_extension) # 2) Generates the curve from the current leg pose to the target leg pose. # and Find the next leg pose on the curve based on current swing phase. - desired_leg_pose = self._leg_trajectory_generator( - phase, raibiert_controller.swing_start_leg_pose, target_leg_pose) + desired_leg_pose = self._leg_trajectory_generator(phase, + raibiert_controller.swing_start_leg_pose, + target_leg_pose) leg_pose_set.append(desired_leg_pose) @@ -244,9 +235,7 @@ class RaibertSwingLegController(object): class RaibertStanceLegController(object): - def __init__(self, - speed_gain=0.1, - leg_trajectory_generator=generate_stance_trajectory): + def __init__(self, speed_gain=0.1, leg_trajectory_generator=generate_stance_trajectory): self._speed_gain = speed_gain self._leg_trajectory_generator = leg_trajectory_generator @@ -255,20 +244,18 @@ class RaibertStanceLegController(object): current_speed = raibiert_controller.estimate_base_velocity() leg_pose_set = [] for i in raibiert_controller.stance_set: - desired_forward_speed = ( - raibiert_controller.behavior_parameters.desired_forward_speed) - target_foot_position = -( - raibiert_controller.behavior_parameters.stance_duration / 2 * - current_speed - self._speed_gain * - (current_speed - desired_forward_speed)) + desired_forward_speed = (raibiert_controller.behavior_parameters.desired_forward_speed) + target_foot_position = -(raibiert_controller.behavior_parameters.stance_duration / 2 * + current_speed - self._speed_gain * + (current_speed - desired_forward_speed)) target_leg_pose = (foot_horizontal_position_to_leg_swing( - target_foot_position, - leg_extension=raibiert_controller.nominal_leg_extension), + target_foot_position, leg_extension=raibiert_controller.nominal_leg_extension), raibiert_controller.nominal_leg_extension) - desired_leg_pose = self._leg_trajectory_generator( - phase, raibiert_controller.stance_start_leg_pose, target_leg_pose) + desired_leg_pose = self._leg_trajectory_generator(phase, + raibiert_controller.stance_start_leg_pose, + target_leg_pose) leg_pose_set.append(desired_leg_pose) @@ -288,8 +275,7 @@ class MinitaurRaibertTrottingController(object): self._robot = robot self._behavior_parameters = behavior_parameters - nominal_leg_pose = foot_position_to_leg_pose( - (0, -self._behavior_parameters.standing_height)) + nominal_leg_pose = foot_position_to_leg_pose((0, -self._behavior_parameters.standing_height)) self._nominal_leg_extension = nominal_leg_pose[1] self._swing_leg_controller = swing_leg_controller @@ -337,8 +323,7 @@ class MinitaurRaibertTrottingController(object): # extract the swing leg pose from the current_leg_pose leg_pose = [] for index in leg_indices: - leg_pose.append( - [current_leg_pose[index], current_leg_pose[index + _NUM_LEGS]]) + leg_pose.append([current_leg_pose[index], current_leg_pose[index + _NUM_LEGS]]) leg_pose = np.array(leg_pose) return np.mean(leg_pose, axis=0) @@ -353,13 +338,13 @@ class MinitaurRaibertTrottingController(object): def get_phase(self): """Compute the current stance/swing phase.""" - return math.fmod(self._time, self._behavior_parameters.stance_duration - ) / self._behavior_parameters.stance_duration + return math.fmod( + self._time, + self._behavior_parameters.stance_duration) / self._behavior_parameters.stance_duration def update_swing_stance_set(self): """Switch the set of swing/stance legs based on timing.""" - swing_stance_phase = math.fmod( - self._time, 2 * self._behavior_parameters.stance_duration) + swing_stance_phase = math.fmod(self._time, 2 * self._behavior_parameters.stance_duration) if swing_stance_phase < self._behavior_parameters.stance_duration: return (DIAGONAL_LEG_PAIR_1, DIAGONAL_LEG_PAIR_2) return (DIAGONAL_LEG_PAIR_2, DIAGONAL_LEG_PAIR_1) @@ -387,8 +372,8 @@ class MinitaurRaibertTrottingController(object): toe_hip_len = math.sqrt(x**2 + y**2) horizontal_dist = toe_hip_len * delta_sw phase = self.get_phase() - speed = 0 if phase < 0.1 else horizontal_dist / ( - self._behavior_parameters.stance_duration * phase) + speed = 0 if phase < 0.1 else horizontal_dist / (self._behavior_parameters.stance_duration * + phase) return speed def get_swing_leg_action(self): diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_raibert_controller_example.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_raibert_controller_example.py index 87958dde5..8adeee376 100644 --- a/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_raibert_controller_example.py +++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_raibert_controller_example.py @@ -1,6 +1,5 @@ #The example to run the raibert controller in a Minitaur gym env. - from __future__ import absolute_import from __future__ import division from __future__ import print_function @@ -9,8 +8,7 @@ import os import inspect currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe()))) parentdir = os.path.dirname(os.path.dirname(os.path.dirname(currentdir))) -os.sys.path.insert(0,parentdir) - +os.sys.path.insert(0, parentdir) import tensorflow as tf from pybullet_envs.minitaur.envs import minitaur_raibert_controller @@ -21,9 +19,8 @@ FLAGS = tf.app.flags.FLAGS flags.DEFINE_float("motor_kp", 1.0, "The position gain of the motor.") flags.DEFINE_float("motor_kd", 0.015, "The speed gain of the motor.") -flags.DEFINE_float( - "control_latency", 0.02, "The latency between sensor measurement and action" - " execution the robot.") +flags.DEFINE_float("control_latency", 0.02, "The latency between sensor measurement and action" + " execution the robot.") flags.DEFINE_string("log_path", None, "The directory to write the log file.") @@ -55,20 +52,18 @@ def main(argv): log_path=FLAGS.log_path) env.reset() - controller = minitaur_raibert_controller.MinitaurRaibertTrottingController( - env.minitaur) + controller = minitaur_raibert_controller.MinitaurRaibertTrottingController(env.minitaur) tstart = env.minitaur.GetTimeSinceReset() for _ in range(1000): t = env.minitaur.GetTimeSinceReset() - tstart - controller.behavior_parameters = ( - minitaur_raibert_controller.BehaviorParameters( - desired_forward_speed=speed(t))) + controller.behavior_parameters = (minitaur_raibert_controller.BehaviorParameters( + desired_forward_speed=speed(t))) controller.update(t) env.step(controller.get_action()) finally: env.close() + if __name__ == "__main__": tf.app.run(main) - diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_rainbow_dash.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_rainbow_dash.py index c33b4af77..d1787ea35 100644 --- a/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_rainbow_dash.py +++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_rainbow_dash.py @@ -4,10 +4,10 @@ It is the result of first pass system identification for the rainbow dash robot. """ -import os, inspect +import os, inspect currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe()))) parentdir = os.path.dirname(os.path.dirname(currentdir)) -os.sys.path.insert(0,parentdir) +os.sys.path.insert(0, parentdir) import math @@ -46,13 +46,12 @@ class MinitaurRainbowDash(minitaur.Minitaur): "%s/quadruped/minitaur_rainbow_dash.urdf" % self._urdf_root, init_position, useFixedBase=self._on_rack, - flags=( - self._pybullet_client.URDF_USE_SELF_COLLISION_EXCLUDE_PARENT)) + flags=(self._pybullet_client.URDF_USE_SELF_COLLISION_EXCLUDE_PARENT)) else: - self.quadruped = self._pybullet_client.loadURDF( - "%s/quadruped/minitaur_rainbow_dash.urdf" % self._urdf_root, - init_position, - useFixedBase=self._on_rack) + self.quadruped = self._pybullet_client.loadURDF("%s/quadruped/minitaur_rainbow_dash.urdf" % + self._urdf_root, + init_position, + useFixedBase=self._on_rack) self._BuildJointNameToIdDict() self._BuildUrdfIds() if self._remove_default_joint_damping: @@ -62,10 +61,9 @@ class MinitaurRainbowDash(minitaur.Minitaur): self._RecordInertiaInfoFromURDF() self.ResetPose(add_constraint=True) else: - self._pybullet_client.resetBasePositionAndOrientation( - self.quadruped, init_position, minitaur.INIT_ORIENTATION) - self._pybullet_client.resetBaseVelocity(self.quadruped, [0, 0, 0], - [0, 0, 0]) + self._pybullet_client.resetBasePositionAndOrientation(self.quadruped, init_position, + minitaur.INIT_ORIENTATION) + self._pybullet_client.resetBaseVelocity(self.quadruped, [0, 0, 0], [0, 0, 0]) self.ResetPose(add_constraint=False) self._overheat_counter = np.zeros(self.num_motors) @@ -103,68 +101,60 @@ class MinitaurRainbowDash(minitaur.Minitaur): knee_angle = -2.1834 leg_position = minitaur.LEG_POSITION[leg_id] - self._pybullet_client.resetJointState( - self.quadruped, - self._joint_name_to_id["motor_" + leg_position + "L_joint"], - self._motor_direction[2 * leg_id] * half_pi, - targetVelocity=0) - self._pybullet_client.resetJointState( - self.quadruped, - self._joint_name_to_id["knee_" + leg_position + "L_joint"], - self._motor_direction[2 * leg_id] * knee_angle, - targetVelocity=0) - self._pybullet_client.resetJointState( - self.quadruped, - self._joint_name_to_id["motor_" + leg_position + "R_joint"], - self._motor_direction[2 * leg_id + 1] * half_pi, - targetVelocity=0) - self._pybullet_client.resetJointState( - self.quadruped, - self._joint_name_to_id["knee_" + leg_position + "R_joint"], - self._motor_direction[2 * leg_id + 1] * knee_angle, - targetVelocity=0) + self._pybullet_client.resetJointState(self.quadruped, + self._joint_name_to_id["motor_" + leg_position + + "L_joint"], + self._motor_direction[2 * leg_id] * half_pi, + targetVelocity=0) + self._pybullet_client.resetJointState(self.quadruped, + self._joint_name_to_id["knee_" + leg_position + + "L_joint"], + self._motor_direction[2 * leg_id] * knee_angle, + targetVelocity=0) + self._pybullet_client.resetJointState(self.quadruped, + self._joint_name_to_id["motor_" + leg_position + + "R_joint"], + self._motor_direction[2 * leg_id + 1] * half_pi, + targetVelocity=0) + self._pybullet_client.resetJointState(self.quadruped, + self._joint_name_to_id["knee_" + leg_position + + "R_joint"], + self._motor_direction[2 * leg_id + 1] * knee_angle, + targetVelocity=0) if add_constraint: if leg_id < 2: self._pybullet_client.createConstraint( - self.quadruped, - self._joint_name_to_id["knee_" + leg_position + "R_joint"], - self.quadruped, - self._joint_name_to_id["knee_" + leg_position + "L_joint"], - self._pybullet_client.JOINT_POINT2POINT, [0, 0, 0], - KNEE_CONSTRAINT_POINT_SHORT, KNEE_CONSTRAINT_POINT_LONG) + self.quadruped, self._joint_name_to_id["knee_" + leg_position + "R_joint"], + self.quadruped, self._joint_name_to_id["knee_" + leg_position + "L_joint"], + self._pybullet_client.JOINT_POINT2POINT, [0, 0, 0], KNEE_CONSTRAINT_POINT_SHORT, + KNEE_CONSTRAINT_POINT_LONG) else: self._pybullet_client.createConstraint( - self.quadruped, - self._joint_name_to_id["knee_" + leg_position + "R_joint"], - self.quadruped, - self._joint_name_to_id["knee_" + leg_position + "L_joint"], - self._pybullet_client.JOINT_POINT2POINT, [0, 0, 0], - KNEE_CONSTRAINT_POINT_LONG, KNEE_CONSTRAINT_POINT_SHORT) + self.quadruped, self._joint_name_to_id["knee_" + leg_position + "R_joint"], + self.quadruped, self._joint_name_to_id["knee_" + leg_position + "L_joint"], + self._pybullet_client.JOINT_POINT2POINT, [0, 0, 0], KNEE_CONSTRAINT_POINT_LONG, + KNEE_CONSTRAINT_POINT_SHORT) if self._accurate_motor_model_enabled or self._pd_control_enabled: # Disable the default motor in pybullet. self._pybullet_client.setJointMotorControl2( bodyIndex=self.quadruped, - jointIndex=( - self._joint_name_to_id["motor_" + leg_position + "L_joint"]), + jointIndex=(self._joint_name_to_id["motor_" + leg_position + "L_joint"]), controlMode=self._pybullet_client.VELOCITY_CONTROL, targetVelocity=0, force=knee_friction_force) self._pybullet_client.setJointMotorControl2( bodyIndex=self.quadruped, - jointIndex=( - self._joint_name_to_id["motor_" + leg_position + "R_joint"]), + jointIndex=(self._joint_name_to_id["motor_" + leg_position + "R_joint"]), controlMode=self._pybullet_client.VELOCITY_CONTROL, targetVelocity=0, force=knee_friction_force) else: - self._SetDesiredMotorAngleByName( - "motor_" + leg_position + "L_joint", - self._motor_direction[2 * leg_id] * half_pi) - self._SetDesiredMotorAngleByName( - "motor_" + leg_position + "R_joint", - self._motor_direction[2 * leg_id + 1] * half_pi) + self._SetDesiredMotorAngleByName("motor_" + leg_position + "L_joint", + self._motor_direction[2 * leg_id] * half_pi) + self._SetDesiredMotorAngleByName("motor_" + leg_position + "R_joint", + self._motor_direction[2 * leg_id + 1] * half_pi) self._pybullet_client.setJointMotorControl2( bodyIndex=self.quadruped, diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_randomize_terrain_gym_env.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_randomize_terrain_gym_env.py index 540965a2f..a9492f309 100644 --- a/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_randomize_terrain_gym_env.py +++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_randomize_terrain_gym_env.py @@ -42,20 +42,17 @@ class MinitaurRandomizeTerrainGymEnv(minitaur_gym_env.MinitaurGymEnv): fileName=terrain_file_name, flags=1, meshScale=[0.5, 0.5, 0.5]) - self._pybullet_client.createMultiBody(terrain_mass, - terrain_collision_shape_id, - terrain_visual_shape_id, - terrain_position, + self._pybullet_client.createMultiBody(terrain_mass, terrain_collision_shape_id, + terrain_visual_shape_id, terrain_position, terrain_orientation) self._pybullet_client.setGravity(0, 0, -10) - self.minitaur = (minitaur.Minitaur( - pybullet_client=self._pybullet_client, - urdf_root=self._urdf_root, - time_step=self._time_step, - self_collision_enabled=self._self_collision_enabled, - motor_velocity_limit=self._motor_velocity_limit, - pd_control_enabled=self._pd_control_enabled, - on_rack=self._on_rack)) + self.minitaur = (minitaur.Minitaur(pybullet_client=self._pybullet_client, + urdf_root=self._urdf_root, + time_step=self._time_step, + self_collision_enabled=self._self_collision_enabled, + motor_velocity_limit=self._motor_velocity_limit, + pd_control_enabled=self._pd_control_enabled, + on_rack=self._on_rack)) self._last_base_position = [0, 0, 0] for _ in xrange(100): if self._pd_control_enabled: @@ -78,6 +75,5 @@ class MinitaurRandomizeTerrainGymEnv(minitaur_gym_env.MinitaurGymEnv): asset_source = os.path.join(terrain_dir, terrain_file_name) asset_destination = os.path.join(FLAGS.storage_dir, terrain_file_name) gfile.Copy(asset_source, asset_destination, overwrite=True) - terrain_file_name_complete = os.path.join(FLAGS.storage_dir, - terrain_file_name) + terrain_file_name_complete = os.path.join(FLAGS.storage_dir, terrain_file_name) return terrain_file_name_complete diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_randomize_terrain_gym_env_example.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_randomize_terrain_gym_env_example.py index 0e5638790..3ecf6dc9c 100644 --- a/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_randomize_terrain_gym_env_example.py +++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_randomize_terrain_gym_env_example.py @@ -21,10 +21,7 @@ def ResetTerrainExample(): num_reset = 10 steps = 100 env = minitaur_randomize_terrain_gym_env.MinitaurRandomizeTerrainGymEnv( - render=True, - leg_model_enabled=False, - motor_velocity_limit=np.inf, - pd_control_enabled=True) + render=True, leg_model_enabled=False, motor_velocity_limit=np.inf, pd_control_enabled=True) action = [math.pi / 2] * 8 for _ in xrange(num_reset): env.reset() @@ -37,10 +34,7 @@ def ResetTerrainExample(): def SinePolicyExample(): """An example of minitaur walking with a sine gait.""" env = minitaur_randomize_terrain_gym_env.MinitaurRandomizeTerrainGymEnv( - render=True, - motor_velocity_limit=np.inf, - pd_control_enabled=True, - on_rack=False) + render=True, motor_velocity_limit=np.inf, pd_control_enabled=True, on_rack=False) sum_reward = 0 steps = 200 amplitude_1_bound = 0.5 diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_reactive_env.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_reactive_env.py index 8ae8314f0..7f899cb4a 100644 --- a/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_reactive_env.py +++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_reactive_env.py @@ -2,10 +2,10 @@ """ -import os, inspect +import os, inspect currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe()))) parentdir = os.path.dirname(os.path.dirname(currentdir)) -os.sys.path.insert(0,parentdir) +os.sys.path.insert(0, parentdir) import collections import math @@ -19,8 +19,7 @@ NUM_LEGS = 4 NUM_MOTORS = 2 * NUM_LEGS MinitaurPose = collections.namedtuple( - "MinitaurPose", - "swing_angle_1, swing_angle_2, swing_angle_3, swing_angle_4, " + "MinitaurPose", "swing_angle_1, swing_angle_2, swing_angle_3, swing_angle_4, " "extension_angle_1, extension_angle_2, extension_angle_3, " "extension_angle_4") @@ -35,10 +34,7 @@ class MinitaurReactiveEnv(minitaur_gym_env.MinitaurGymEnv): expenditure. """ - metadata = { - "render.modes": ["human", "rgb_array"], - "video.frames_per_second": 166 - } + metadata = {"render.modes": ["human", "rgb_array"], "video.frames_per_second": 166} def __init__(self, urdf_version=None, @@ -96,24 +92,24 @@ class MinitaurReactiveEnv(minitaur_gym_env.MinitaurGymEnv): """ self._use_angle_in_observation = use_angle_in_observation - super(MinitaurReactiveEnv, self).__init__( - urdf_version=urdf_version, - energy_weight=energy_weight, - accurate_motor_model_enabled=accurate_motor_model_enabled, - motor_overheat_protection=True, - motor_kp=motor_kp, - motor_kd=motor_kd, - remove_default_joint_damping=remove_default_joint_damping, - control_latency=control_latency, - pd_latency=pd_latency, - on_rack=on_rack, - render=render, - hard_reset=hard_reset, - num_steps_to_log=num_steps_to_log, - env_randomizer=env_randomizer, - log_path=log_path, - control_time_step=control_time_step, - action_repeat=action_repeat) + super(MinitaurReactiveEnv, + self).__init__(urdf_version=urdf_version, + energy_weight=energy_weight, + accurate_motor_model_enabled=accurate_motor_model_enabled, + motor_overheat_protection=True, + motor_kp=motor_kp, + motor_kd=motor_kd, + remove_default_joint_damping=remove_default_joint_damping, + control_latency=control_latency, + pd_latency=pd_latency, + on_rack=on_rack, + render=render, + hard_reset=hard_reset, + num_steps_to_log=num_steps_to_log, + env_randomizer=env_randomizer, + log_path=log_path, + control_time_step=control_time_step, + action_repeat=action_repeat) action_dim = 8 action_low = np.array([-0.5] * action_dim) @@ -126,34 +122,31 @@ class MinitaurReactiveEnv(minitaur_gym_env.MinitaurGymEnv): def reset(self): # TODO(b/73666007): Use composition instead of inheritance. # (http://go/design-for-testability-no-inheritance). - init_pose = MinitaurPose( - swing_angle_1=INIT_SWING_POS, - swing_angle_2=INIT_SWING_POS, - swing_angle_3=INIT_SWING_POS, - swing_angle_4=INIT_SWING_POS, - extension_angle_1=INIT_EXTENSION_POS, - extension_angle_2=INIT_EXTENSION_POS, - extension_angle_3=INIT_EXTENSION_POS, - extension_angle_4=INIT_EXTENSION_POS) + init_pose = MinitaurPose(swing_angle_1=INIT_SWING_POS, + swing_angle_2=INIT_SWING_POS, + swing_angle_3=INIT_SWING_POS, + swing_angle_4=INIT_SWING_POS, + extension_angle_1=INIT_EXTENSION_POS, + extension_angle_2=INIT_EXTENSION_POS, + extension_angle_3=INIT_EXTENSION_POS, + extension_angle_4=INIT_EXTENSION_POS) # TODO(b/73734502): Refactor input of _convert_from_leg_model to namedtuple. initial_motor_angles = self._convert_from_leg_model(list(init_pose)) - super(MinitaurReactiveEnv, self).reset( - initial_motor_angles=initial_motor_angles, reset_duration=0.5) + super(MinitaurReactiveEnv, self).reset(initial_motor_angles=initial_motor_angles, + reset_duration=0.5) return self._get_observation() def _convert_from_leg_model(self, leg_pose): motor_pose = np.zeros(NUM_MOTORS) for i in range(NUM_LEGS): motor_pose[int(2 * i)] = leg_pose[NUM_LEGS + i] - (-1)**int(i / 2) * leg_pose[i] - motor_pose[int(2 * i + 1)] = ( - leg_pose[NUM_LEGS + i] + (-1)**int(i / 2) * leg_pose[i]) + motor_pose[int(2 * i + 1)] = (leg_pose[NUM_LEGS + i] + (-1)**int(i / 2) * leg_pose[i]) return motor_pose def _signal(self, t): initial_pose = np.array([ - INIT_SWING_POS, INIT_SWING_POS, INIT_SWING_POS, INIT_SWING_POS, - INIT_EXTENSION_POS, INIT_EXTENSION_POS, INIT_EXTENSION_POS, - INIT_EXTENSION_POS + INIT_SWING_POS, INIT_SWING_POS, INIT_SWING_POS, INIT_SWING_POS, INIT_EXTENSION_POS, + INIT_EXTENSION_POS, INIT_EXTENSION_POS, INIT_EXTENSION_POS ]) return initial_pose @@ -214,8 +207,7 @@ class MinitaurReactiveEnv(minitaur_gym_env.MinitaurGymEnv): upper_bound_pitch_dot = 2 * math.pi / self._time_step upper_bound_motor_angle = 2 * math.pi upper_bound = [ - upper_bound_roll, upper_bound_pitch, upper_bound_roll_dot, - upper_bound_pitch_dot + upper_bound_roll, upper_bound_pitch, upper_bound_roll_dot, upper_bound_pitch_dot ] if self._use_angle_in_observation: diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_reactive_env_example.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_reactive_env_example.py index 2c2be7106..e0d4000c1 100644 --- a/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_reactive_env_example.py +++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_reactive_env_example.py @@ -10,17 +10,14 @@ import time import inspect currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe()))) parentdir = os.path.dirname(os.path.dirname(os.path.dirname(currentdir))) -print("parentdir=",parentdir) -os.sys.path.insert(0,parentdir) - - +print("parentdir=", parentdir) +os.sys.path.insert(0, parentdir) import tensorflow as tf from pybullet_envs.minitaur.agents.scripts import utility import pybullet_data from pybullet_envs.minitaur.envs import simple_ppo_agent - flags = tf.app.flags FLAGS = tf.app.flags.FLAGS LOG_DIR = os.path.join(pybullet_data.getDataPath(), "policies/ppo/minitaur_reactive_env") @@ -36,13 +33,12 @@ def main(argv): network = config.network with tf.Session() as sess: - agent = simple_ppo_agent.SimplePPOPolicy( - sess, - env, - network, - policy_layers=policy_layers, - value_layers=value_layers, - checkpoint=os.path.join(LOG_DIR, CHECKPOINT)) + agent = simple_ppo_agent.SimplePPOPolicy(sess, + env, + network, + policy_layers=policy_layers, + value_layers=value_layers, + checkpoint=os.path.join(LOG_DIR, CHECKPOINT)) sum_reward = 0 observation = env.reset() diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_stand_gym_env.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_stand_gym_env.py index 3ea1b2e09..8135b7265 100644 --- a/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_stand_gym_env.py +++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_stand_gym_env.py @@ -29,10 +29,7 @@ class MinitaurStandGymEnv(minitaur_gym_env.MinitaurGymEnv): function is based on how long the minitaur stays standing up. """ - metadata = { - "render.modes": ["human", "rgb_array"], - "video.frames_per_second": 50 - } + metadata = {"render.modes": ["human", "rgb_array"], "video.frames_per_second": 50} def __init__(self, urdf_root=pybullet_data.getDataPath(), @@ -53,16 +50,15 @@ class MinitaurStandGymEnv(minitaur_gym_env.MinitaurGymEnv): pd_control_enabled: Whether to use PD controller for each motor. render: Whether to render the simulation. """ - super(MinitaurStandGymEnv, self).__init__( - urdf_root=urdf_root, - action_repeat=action_repeat, - observation_noise_stdev=observation_noise_stdev, - self_collision_enabled=self_collision_enabled, - motor_velocity_limit=motor_velocity_limit, - pd_control_enabled=pd_control_enabled, - accurate_motor_model_enabled=True, - motor_overheat_protection=True, - render=render) + super(MinitaurStandGymEnv, self).__init__(urdf_root=urdf_root, + action_repeat=action_repeat, + observation_noise_stdev=observation_noise_stdev, + self_collision_enabled=self_collision_enabled, + motor_velocity_limit=motor_velocity_limit, + pd_control_enabled=pd_control_enabled, + accurate_motor_model_enabled=True, + motor_overheat_protection=True, + render=render) # Set the action dimension to 1, and reset the action space. action_dim = 1 action_high = np.array([self._action_bound] * action_dim) @@ -85,8 +81,8 @@ class MinitaurStandGymEnv(minitaur_gym_env.MinitaurGymEnv): for t in range(5000): if self._is_render: base_pos = self.minitaur.GetBasePosition() - self._pybullet_client.resetDebugVisualizerCamera( - self._cam_dist, self._cam_yaw, self._cam_pitch, base_pos) + self._pybullet_client.resetDebugVisualizerCamera(self._cam_dist, self._cam_yaw, + self._cam_pitch, base_pos) state = self._get_true_observation() action = self._policy_flip(t, state[24:28]) self.minitaur.ApplyAction(action) @@ -226,8 +222,7 @@ class MinitaurStandGymEnv(minitaur_gym_env.MinitaurGymEnv): # Lower the signal a little, so that it becomes positive only for a short # amount time. lower_signal = -0.94 - signal_unit = math.copysign(intensity, - math.sin(time_step * speed) + lower_signal) + signal_unit = math.copysign(intensity, math.sin(time_step * speed) + lower_signal) # Only extend the leg, don't shorten. if signal_unit < 0: signal_unit = 0 diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_stand_gym_env_example.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_stand_gym_env_example.py index 848c39d4a..aa07bd029 100644 --- a/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_stand_gym_env_example.py +++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_stand_gym_env_example.py @@ -13,9 +13,8 @@ from pybullet_envs.minitaur.envs import minitaur_stand_gym_env def StandUpExample(): """An example that the minitaur stands up.""" steps = 1000 - environment = minitaur_stand_gym_env.MinitaurStandGymEnv( - render=True, - motor_velocity_limit=np.inf) + environment = minitaur_stand_gym_env.MinitaurStandGymEnv(render=True, + motor_velocity_limit=np.inf) action = [0.5] _, _, done, _ = environment.step(action) for t in range(steps): diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_trotting_env.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_trotting_env.py index 97f820f54..e79e54926 100644 --- a/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_trotting_env.py +++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_trotting_env.py @@ -24,10 +24,7 @@ class MinitaurTrottingEnv(minitaur_gym_env.MinitaurGymEnv): controller (e.g. a neural network). """ - metadata = { - "render.modes": ["human", "rgb_array"], - "video.frames_per_second": 166 - } + metadata = {"render.modes": ["human", "rgb_array"], "video.frames_per_second": 166} def __init__(self, urdf_version=None, @@ -103,8 +100,8 @@ class MinitaurTrottingEnv(minitaur_gym_env.MinitaurGymEnv): # The reset position. self._init_pose = [ - init_swing, init_swing, init_swing, init_swing, init_extension, - init_extension, init_extension, init_extension + init_swing, init_swing, init_swing, init_swing, init_extension, init_extension, + init_extension, init_extension ] self._step_frequency = step_frequency @@ -112,23 +109,23 @@ class MinitaurTrottingEnv(minitaur_gym_env.MinitaurGymEnv): self._swing_amplitude = swing_amplitude self._use_signal_in_observation = use_signal_in_observation self._use_angle_in_observation = use_angle_in_observation - super(MinitaurTrottingEnv, self).__init__( - urdf_version=urdf_version, - accurate_motor_model_enabled=accurate_motor_model_enabled, - motor_overheat_protection=True, - motor_kp=motor_kp, - motor_kd=motor_kd, - remove_default_joint_damping=remove_default_joint_damping, - control_latency=control_latency, - pd_latency=pd_latency, - on_rack=on_rack, - render=render, - hard_reset=hard_reset, - num_steps_to_log=num_steps_to_log, - env_randomizer=env_randomizer, - log_path=log_path, - control_time_step=control_time_step, - action_repeat=action_repeat) + super(MinitaurTrottingEnv, + self).__init__(urdf_version=urdf_version, + accurate_motor_model_enabled=accurate_motor_model_enabled, + motor_overheat_protection=True, + motor_kp=motor_kp, + motor_kd=motor_kd, + remove_default_joint_damping=remove_default_joint_damping, + control_latency=control_latency, + pd_latency=pd_latency, + on_rack=on_rack, + render=render, + hard_reset=hard_reset, + num_steps_to_log=num_steps_to_log, + env_randomizer=env_randomizer, + log_path=log_path, + control_time_step=control_time_step, + action_repeat=action_repeat) action_dim = NUM_LEGS * 2 action_high = np.array([0.25] * action_dim) @@ -144,8 +141,8 @@ class MinitaurTrottingEnv(minitaur_gym_env.MinitaurGymEnv): # [swing leg 1, swing leg 2, swing leg 3, swing leg 4, # extension leg 1, extension leg 2, extension leg 3, extension leg 4] initial_motor_angles = self._convert_from_leg_model(self._init_pose) - super(MinitaurTrottingEnv, self).reset( - initial_motor_angles=initial_motor_angles, reset_duration=0.5) + super(MinitaurTrottingEnv, self).reset(initial_motor_angles=initial_motor_angles, + reset_duration=0.5) return self._get_observation() def _convert_from_leg_model(self, leg_pose): @@ -161,8 +158,7 @@ class MinitaurTrottingEnv(minitaur_gym_env.MinitaurGymEnv): motor_pose = np.zeros(NUM_MOTORS) for i in range(NUM_LEGS): motor_pose[int(2 * i)] = leg_pose[NUM_LEGS + i] - (-1)**int(i / 2) * leg_pose[i] - motor_pose[int(2 * i - + 1)] = leg_pose[NUM_LEGS + i] + (-1)**int(i / 2) * leg_pose[i] + motor_pose[int(2 * i + 1)] = leg_pose[NUM_LEGS + i] + (-1)**int(i / 2) * leg_pose[i] return motor_pose def _gen_signal(self, t, phase): @@ -179,8 +175,7 @@ class MinitaurTrottingEnv(minitaur_gym_env.MinitaurGymEnv): The desired leg extension and swing angle at the current time. """ period = 1 / self._step_frequency - extension = self._extension_amplitude * math.cos( - 2 * math.pi / period * t + phase) + extension = self._extension_amplitude * math.cos(2 * math.pi / period * t + phase) swing = self._swing_amplitude * math.sin(2 * math.pi / period * t + phase) return extension, swing @@ -198,8 +193,8 @@ class MinitaurTrottingEnv(minitaur_gym_env.MinitaurGymEnv): ext_second_pair, sw_second_pair = self._gen_signal(t, math.pi) trotting_signal = np.array([ - sw_first_pair, sw_second_pair, sw_second_pair, sw_first_pair, - ext_first_pair, ext_second_pair, ext_second_pair, ext_first_pair + sw_first_pair, sw_second_pair, sw_second_pair, sw_first_pair, ext_first_pair, + ext_second_pair, ext_second_pair, ext_first_pair ]) signal = np.array(self._init_pose) + trotting_signal return signal @@ -286,8 +281,7 @@ class MinitaurTrottingEnv(minitaur_gym_env.MinitaurGymEnv): """ upper_bound = [] upper_bound.extend([2 * math.pi] * 2) # Roll, pitch, yaw of the base. - upper_bound.extend( - [2 * math.pi / self._time_step] * 2) # Roll, pitch, yaw rate. + upper_bound.extend([2 * math.pi / self._time_step] * 2) # Roll, pitch, yaw rate. if self._use_signal_in_observation: upper_bound.extend([2 * math.pi] * NUM_MOTORS) # Signal if self._use_angle_in_observation: diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_trotting_env_example.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_trotting_env_example.py index e158e1160..e38f86428 100644 --- a/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_trotting_env_example.py +++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_trotting_env_example.py @@ -26,13 +26,12 @@ def main(argv): network = config.network with tf.Session() as sess: - agent = simple_ppo_agent.SimplePPOPolicy( - sess, - env, - network, - policy_layers=policy_layers, - value_layers=value_layers, - checkpoint=os.path.join(LOG_DIR, CHECKPOINT)) + agent = simple_ppo_agent.SimplePPOPolicy(sess, + env, + network, + policy_layers=policy_layers, + value_layers=value_layers, + checkpoint=os.path.join(LOG_DIR, CHECKPOINT)) sum_reward = 0 observation = env.reset() @@ -48,4 +47,3 @@ def main(argv): if __name__ == "__main__": tf.app.run(main) - diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs/motor.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs/motor.py index 20e271e1b..dc852b02b 100644 --- a/examples/pybullet/gym/pybullet_envs/minitaur/envs/motor.py +++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs/motor.py @@ -8,8 +8,7 @@ MOTOR_VOLTAGE = 16.0 MOTOR_RESISTANCE = 0.186 MOTOR_TORQUE_CONSTANT = 0.0954 MOTOR_VISCOUS_DAMPING = 0 -MOTOR_SPEED_LIMIT = MOTOR_VOLTAGE / ( - MOTOR_VISCOUS_DAMPING + MOTOR_TORQUE_CONSTANT) +MOTOR_SPEED_LIMIT = MOTOR_VOLTAGE / (MOTOR_VISCOUS_DAMPING + MOTOR_TORQUE_CONSTANT) NUM_MOTORS = 8 @@ -124,21 +123,19 @@ class MotorModel(object): observed_torque: The torque observed by the sensor. """ observed_torque = np.clip( - self._torque_constant * - (np.asarray(pwm) * self._voltage / self._resistance), + self._torque_constant * (np.asarray(pwm) * self._voltage / self._resistance), -OBSERVED_TORQUE_LIMIT, OBSERVED_TORQUE_LIMIT) # Net voltage is clipped at 50V by diodes on the motor controller. voltage_net = np.clip( np.asarray(pwm) * self._voltage - - (self._torque_constant + self._viscous_damping) * - np.asarray(true_motor_velocity), -VOLTAGE_CLIPPING, VOLTAGE_CLIPPING) + (self._torque_constant + self._viscous_damping) * np.asarray(true_motor_velocity), + -VOLTAGE_CLIPPING, VOLTAGE_CLIPPING) current = voltage_net / self._resistance current_sign = np.sign(current) current_magnitude = np.absolute(current) # Saturate torque based on empirical current relation. - actual_torque = np.interp(current_magnitude, self._current_table, - self._torque_table) + actual_torque = np.interp(current_magnitude, self._current_table, self._torque_table) actual_torque = np.multiply(current_sign, actual_torque) actual_torque = np.multiply(self._strength_ratios, actual_torque) return actual_torque, observed_torque diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs/simple_ppo_agent.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs/simple_ppo_agent.py index e3c4b4ed3..85e35ad52 100644 --- a/examples/pybullet/gym/pybullet_envs/minitaur/envs/simple_ppo_agent.py +++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs/simple_ppo_agent.py @@ -4,7 +4,6 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function - import tensorflow as tf from pybullet_envs.agents.ppo import normalize from pybullet_envs.agents import utility @@ -20,29 +19,25 @@ class SimplePPOPolicy(object): https://cs.corp.google.com/piper///depot/google3/robotics/reinforcement_learning/agents/scripts/visualize.py. """ - def __init__(self, sess, env, network, policy_layers, value_layers, - checkpoint): + def __init__(self, sess, env, network, policy_layers, value_layers, checkpoint): self.env = env self.sess = sess observation_size = len(env.observation_space.low) action_size = len(env.action_space.low) - self.observation_placeholder = tf.placeholder( - tf.float32, [None, observation_size], name="Input") - self._observ_filter = normalize.StreamingNormalize( - self.observation_placeholder[0], - center=True, - scale=True, - clip=5, - name="normalize_observ") - self._restore_policy( - network, - policy_layers=policy_layers, - value_layers=value_layers, - action_size=action_size, - checkpoint=checkpoint) + self.observation_placeholder = tf.placeholder(tf.float32, [None, observation_size], + name="Input") + self._observ_filter = normalize.StreamingNormalize(self.observation_placeholder[0], + center=True, + scale=True, + clip=5, + name="normalize_observ") + self._restore_policy(network, + policy_layers=policy_layers, + value_layers=value_layers, + action_size=action_size, + checkpoint=checkpoint) - def _restore_policy(self, network, policy_layers, value_layers, action_size, - checkpoint): + def _restore_policy(self, network, policy_layers, value_layers, action_size, checkpoint): """Restore the PPO policy from a TensorFlow checkpoint. Args: @@ -56,24 +51,21 @@ class SimplePPOPolicy(object): """ observ = self._observ_filter.transform(self.observation_placeholder) with tf.variable_scope("network/rnn"): - self.network = network( - policy_layers=policy_layers, - value_layers=value_layers, - action_size=action_size) + self.network = network(policy_layers=policy_layers, + value_layers=value_layers, + action_size=action_size) with tf.variable_scope("temporary"): - self.last_state = tf.Variable( - self.network.zero_state(1, tf.float32), False) + self.last_state = tf.Variable(self.network.zero_state(1, tf.float32), False) self.sess.run(self.last_state.initializer) with tf.variable_scope("network"): - (mean_action, _, _), new_state = tf.nn.dynamic_rnn( - self.network, - observ[:, None], - tf.ones(1), - self.last_state, - tf.float32, - swap_memory=True) + (mean_action, _, _), new_state = tf.nn.dynamic_rnn(self.network, + observ[:, None], + tf.ones(1), + self.last_state, + tf.float32, + swap_memory=True) self.mean_action = mean_action self.update_state = self.last_state.assign(new_state) diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs/simple_ppo_agent_example.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs/simple_ppo_agent_example.py index 0d6f6f807..08808f98d 100644 --- a/examples/pybullet/gym/pybullet_envs/minitaur/envs/simple_ppo_agent_example.py +++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs/simple_ppo_agent_example.py @@ -29,8 +29,7 @@ from pybullet_envs.minitaur.agents import simple_ppo_agent flags = tf.app.flags FLAGS = tf.app.flags.FLAGS -flags.DEFINE_string("logdir", None, - "The directory that contains checkpoint and config.") +flags.DEFINE_string("logdir", None, "The directory that contains checkpoint and config.") flags.DEFINE_string("checkpoint", None, "The checkpoint file path.") flags.DEFINE_string("log_path", None, "The output path to write log.") @@ -44,13 +43,13 @@ def main(argv): network = config.network with tf.Session() as sess: - agent = simple_ppo_agent.SimplePPOPolicy( - sess, - env, - network, - policy_layers=policy_layers, - value_layers=value_layers, - checkpoint=os.path.join(FLAGS.logdir, FLAGS.checkpoint)) + agent = simple_ppo_agent.SimplePPOPolicy(sess, + env, + network, + policy_layers=policy_layers, + value_layers=value_layers, + checkpoint=os.path.join(FLAGS.logdir, + FLAGS.checkpoint)) sum_reward = 0 observation = env.reset() diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs/timestamp_pb2.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs/timestamp_pb2.py index 25e3a230d..bd1bf7703 100644 --- a/examples/pybullet/gym/pybullet_envs/minitaur/envs/timestamp_pb2.py +++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs/timestamp_pb2.py @@ -2,7 +2,7 @@ # source: timestamp.proto import sys -_b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1')) +_b = sys.version_info[0] < 3 and (lambda x: x) or (lambda x: x.encode('latin1')) from google.protobuf import descriptor as _descriptor from google.protobuf import message as _message from google.protobuf import reflection as _reflection @@ -12,65 +12,76 @@ from google.protobuf import descriptor_pb2 _sym_db = _symbol_database.Default() - - - DESCRIPTOR = _descriptor.FileDescriptor( - name='timestamp.proto', - package='google.protobuf', - syntax='proto3', - serialized_pb=_b('\n\x0ftimestamp.proto\x12\x0fgoogle.protobuf\"+\n\tTimestamp\x12\x0f\n\x07seconds\x18\x01 \x01(\x03\x12\r\n\x05nanos\x18\x02 \x01(\x05\x62\x06proto3') -) - - - + name='timestamp.proto', + package='google.protobuf', + syntax='proto3', + serialized_pb=_b( + '\n\x0ftimestamp.proto\x12\x0fgoogle.protobuf\"+\n\tTimestamp\x12\x0f\n\x07seconds\x18\x01 \x01(\x03\x12\r\n\x05nanos\x18\x02 \x01(\x05\x62\x06proto3' + )) _TIMESTAMP = _descriptor.Descriptor( - name='Timestamp', - full_name='google.protobuf.Timestamp', - filename=None, - file=DESCRIPTOR, - containing_type=None, - fields=[ - _descriptor.FieldDescriptor( - name='seconds', full_name='google.protobuf.Timestamp.seconds', index=0, - number=1, type=3, cpp_type=2, label=1, - has_default_value=False, default_value=0, - message_type=None, enum_type=None, containing_type=None, - is_extension=False, extension_scope=None, - options=None, file=DESCRIPTOR), - _descriptor.FieldDescriptor( - name='nanos', full_name='google.protobuf.Timestamp.nanos', index=1, - number=2, type=5, cpp_type=1, label=1, - has_default_value=False, default_value=0, - message_type=None, enum_type=None, containing_type=None, - is_extension=False, extension_scope=None, - options=None, file=DESCRIPTOR), - ], - extensions=[ - ], - nested_types=[], - enum_types=[ - ], - options=None, - is_extendable=False, - syntax='proto3', - extension_ranges=[], - oneofs=[ - ], - serialized_start=36, - serialized_end=79, + name='Timestamp', + full_name='google.protobuf.Timestamp', + filename=None, + file=DESCRIPTOR, + containing_type=None, + fields=[ + _descriptor.FieldDescriptor(name='seconds', + full_name='google.protobuf.Timestamp.seconds', + index=0, + number=1, + type=3, + cpp_type=2, + label=1, + has_default_value=False, + default_value=0, + message_type=None, + enum_type=None, + containing_type=None, + is_extension=False, + extension_scope=None, + options=None, + file=DESCRIPTOR), + _descriptor.FieldDescriptor(name='nanos', + full_name='google.protobuf.Timestamp.nanos', + index=1, + number=2, + type=5, + cpp_type=1, + label=1, + has_default_value=False, + default_value=0, + message_type=None, + enum_type=None, + containing_type=None, + is_extension=False, + extension_scope=None, + options=None, + file=DESCRIPTOR), + ], + extensions=[], + nested_types=[], + enum_types=[], + options=None, + is_extendable=False, + syntax='proto3', + extension_ranges=[], + oneofs=[], + serialized_start=36, + serialized_end=79, ) DESCRIPTOR.message_types_by_name['Timestamp'] = _TIMESTAMP _sym_db.RegisterFileDescriptor(DESCRIPTOR) -Timestamp = _reflection.GeneratedProtocolMessageType('Timestamp', (_message.Message,), dict( - DESCRIPTOR = _TIMESTAMP, - __module__ = 'timestamp_pb2' - # @@protoc_insertion_point(class_scope:google.protobuf.Timestamp) - )) +Timestamp = _reflection.GeneratedProtocolMessageType( + 'Timestamp', + (_message.Message,), + dict(DESCRIPTOR=_TIMESTAMP, + __module__='timestamp_pb2' + # @@protoc_insertion_point(class_scope:google.protobuf.Timestamp) + )) _sym_db.RegisterMessage(Timestamp) - # @@protoc_insertion_point(module_scope) diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs/vector_pb2.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs/vector_pb2.py index 67bacb9cd..400c6c805 100644 --- a/examples/pybullet/gym/pybullet_envs/minitaur/envs/vector_pb2.py +++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs/vector_pb2.py @@ -2,7 +2,7 @@ # source: vector.proto import sys -_b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1')) +_b = sys.version_info[0] < 3 and (lambda x: x) or (lambda x: x.encode('latin1')) from google.protobuf import descriptor as _descriptor from google.protobuf import message as _message from google.protobuf import reflection as _reflection @@ -12,348 +12,494 @@ from google.protobuf import descriptor_pb2 _sym_db = _symbol_database.Default() - - - DESCRIPTOR = _descriptor.FileDescriptor( - name='vector.proto', - package='robotics.messages', - syntax='proto3', - serialized_pb=_b('\n\x0cvector.proto\x12\x11robotics.messages\"6\n\x08Vector4d\x12\t\n\x01x\x18\x01 \x01(\x01\x12\t\n\x01y\x18\x02 \x01(\x01\x12\t\n\x01z\x18\x03 \x01(\x01\x12\t\n\x01w\x18\x04 \x01(\x01\"6\n\x08Vector4f\x12\t\n\x01x\x18\x01 \x01(\x02\x12\t\n\x01y\x18\x02 \x01(\x02\x12\t\n\x01z\x18\x03 \x01(\x02\x12\t\n\x01w\x18\x04 \x01(\x02\"+\n\x08Vector3d\x12\t\n\x01x\x18\x01 \x01(\x01\x12\t\n\x01y\x18\x02 \x01(\x01\x12\t\n\x01z\x18\x03 \x01(\x01\"+\n\x08Vector3f\x12\t\n\x01x\x18\x01 \x01(\x02\x12\t\n\x01y\x18\x02 \x01(\x02\x12\t\n\x01z\x18\x03 \x01(\x02\" \n\x08Vector2d\x12\t\n\x01x\x18\x01 \x01(\x01\x12\t\n\x01y\x18\x02 \x01(\x01\" \n\x08Vector2f\x12\t\n\x01x\x18\x01 \x01(\x02\x12\t\n\x01y\x18\x02 \x01(\x02\"\x1b\n\x07Vectord\x12\x10\n\x04\x64\x61ta\x18\x01 \x03(\x01\x42\x02\x10\x01\"\x1b\n\x07Vectorf\x12\x10\n\x04\x64\x61ta\x18\x01 \x03(\x02\x42\x02\x10\x01\x62\x06proto3') -) - - - + name='vector.proto', + package='robotics.messages', + syntax='proto3', + serialized_pb=_b( + '\n\x0cvector.proto\x12\x11robotics.messages\"6\n\x08Vector4d\x12\t\n\x01x\x18\x01 \x01(\x01\x12\t\n\x01y\x18\x02 \x01(\x01\x12\t\n\x01z\x18\x03 \x01(\x01\x12\t\n\x01w\x18\x04 \x01(\x01\"6\n\x08Vector4f\x12\t\n\x01x\x18\x01 \x01(\x02\x12\t\n\x01y\x18\x02 \x01(\x02\x12\t\n\x01z\x18\x03 \x01(\x02\x12\t\n\x01w\x18\x04 \x01(\x02\"+\n\x08Vector3d\x12\t\n\x01x\x18\x01 \x01(\x01\x12\t\n\x01y\x18\x02 \x01(\x01\x12\t\n\x01z\x18\x03 \x01(\x01\"+\n\x08Vector3f\x12\t\n\x01x\x18\x01 \x01(\x02\x12\t\n\x01y\x18\x02 \x01(\x02\x12\t\n\x01z\x18\x03 \x01(\x02\" \n\x08Vector2d\x12\t\n\x01x\x18\x01 \x01(\x01\x12\t\n\x01y\x18\x02 \x01(\x01\" \n\x08Vector2f\x12\t\n\x01x\x18\x01 \x01(\x02\x12\t\n\x01y\x18\x02 \x01(\x02\"\x1b\n\x07Vectord\x12\x10\n\x04\x64\x61ta\x18\x01 \x03(\x01\x42\x02\x10\x01\"\x1b\n\x07Vectorf\x12\x10\n\x04\x64\x61ta\x18\x01 \x03(\x02\x42\x02\x10\x01\x62\x06proto3' + )) _VECTOR4D = _descriptor.Descriptor( - name='Vector4d', - full_name='robotics.messages.Vector4d', - filename=None, - file=DESCRIPTOR, - containing_type=None, - fields=[ - _descriptor.FieldDescriptor( - name='x', full_name='robotics.messages.Vector4d.x', index=0, - number=1, type=1, cpp_type=5, label=1, - has_default_value=False, default_value=float(0), - message_type=None, enum_type=None, containing_type=None, - is_extension=False, extension_scope=None, - options=None, file=DESCRIPTOR), - _descriptor.FieldDescriptor( - name='y', full_name='robotics.messages.Vector4d.y', index=1, - number=2, type=1, cpp_type=5, label=1, - has_default_value=False, default_value=float(0), - message_type=None, enum_type=None, containing_type=None, - is_extension=False, extension_scope=None, - options=None, file=DESCRIPTOR), - _descriptor.FieldDescriptor( - name='z', full_name='robotics.messages.Vector4d.z', index=2, - number=3, type=1, cpp_type=5, label=1, - has_default_value=False, default_value=float(0), - message_type=None, enum_type=None, containing_type=None, - is_extension=False, extension_scope=None, - options=None, file=DESCRIPTOR), - _descriptor.FieldDescriptor( - name='w', full_name='robotics.messages.Vector4d.w', index=3, - number=4, type=1, cpp_type=5, label=1, - has_default_value=False, default_value=float(0), - message_type=None, enum_type=None, containing_type=None, - is_extension=False, extension_scope=None, - options=None, file=DESCRIPTOR), - ], - extensions=[ - ], - nested_types=[], - enum_types=[ - ], - options=None, - is_extendable=False, - syntax='proto3', - extension_ranges=[], - oneofs=[ - ], - serialized_start=35, - serialized_end=89, + name='Vector4d', + full_name='robotics.messages.Vector4d', + filename=None, + file=DESCRIPTOR, + containing_type=None, + fields=[ + _descriptor.FieldDescriptor(name='x', + full_name='robotics.messages.Vector4d.x', + index=0, + number=1, + type=1, + cpp_type=5, + label=1, + has_default_value=False, + default_value=float(0), + message_type=None, + enum_type=None, + containing_type=None, + is_extension=False, + extension_scope=None, + options=None, + file=DESCRIPTOR), + _descriptor.FieldDescriptor(name='y', + full_name='robotics.messages.Vector4d.y', + index=1, + number=2, + type=1, + cpp_type=5, + label=1, + has_default_value=False, + default_value=float(0), + message_type=None, + enum_type=None, + containing_type=None, + is_extension=False, + extension_scope=None, + options=None, + file=DESCRIPTOR), + _descriptor.FieldDescriptor(name='z', + full_name='robotics.messages.Vector4d.z', + index=2, + number=3, + type=1, + cpp_type=5, + label=1, + has_default_value=False, + default_value=float(0), + message_type=None, + enum_type=None, + containing_type=None, + is_extension=False, + extension_scope=None, + options=None, + file=DESCRIPTOR), + _descriptor.FieldDescriptor(name='w', + full_name='robotics.messages.Vector4d.w', + index=3, + number=4, + type=1, + cpp_type=5, + label=1, + has_default_value=False, + default_value=float(0), + message_type=None, + enum_type=None, + containing_type=None, + is_extension=False, + extension_scope=None, + options=None, + file=DESCRIPTOR), + ], + extensions=[], + nested_types=[], + enum_types=[], + options=None, + is_extendable=False, + syntax='proto3', + extension_ranges=[], + oneofs=[], + serialized_start=35, + serialized_end=89, ) - _VECTOR4F = _descriptor.Descriptor( - name='Vector4f', - full_name='robotics.messages.Vector4f', - filename=None, - file=DESCRIPTOR, - containing_type=None, - fields=[ - _descriptor.FieldDescriptor( - name='x', full_name='robotics.messages.Vector4f.x', index=0, - number=1, type=2, cpp_type=6, label=1, - has_default_value=False, default_value=float(0), - message_type=None, enum_type=None, containing_type=None, - is_extension=False, extension_scope=None, - options=None, file=DESCRIPTOR), - _descriptor.FieldDescriptor( - name='y', full_name='robotics.messages.Vector4f.y', index=1, - number=2, type=2, cpp_type=6, label=1, - has_default_value=False, default_value=float(0), - message_type=None, enum_type=None, containing_type=None, - is_extension=False, extension_scope=None, - options=None, file=DESCRIPTOR), - _descriptor.FieldDescriptor( - name='z', full_name='robotics.messages.Vector4f.z', index=2, - number=3, type=2, cpp_type=6, label=1, - has_default_value=False, default_value=float(0), - message_type=None, enum_type=None, containing_type=None, - is_extension=False, extension_scope=None, - options=None, file=DESCRIPTOR), - _descriptor.FieldDescriptor( - name='w', full_name='robotics.messages.Vector4f.w', index=3, - number=4, type=2, cpp_type=6, label=1, - has_default_value=False, default_value=float(0), - message_type=None, enum_type=None, containing_type=None, - is_extension=False, extension_scope=None, - options=None, file=DESCRIPTOR), - ], - extensions=[ - ], - nested_types=[], - enum_types=[ - ], - options=None, - is_extendable=False, - syntax='proto3', - extension_ranges=[], - oneofs=[ - ], - serialized_start=91, - serialized_end=145, + name='Vector4f', + full_name='robotics.messages.Vector4f', + filename=None, + file=DESCRIPTOR, + containing_type=None, + fields=[ + _descriptor.FieldDescriptor(name='x', + full_name='robotics.messages.Vector4f.x', + index=0, + number=1, + type=2, + cpp_type=6, + label=1, + has_default_value=False, + default_value=float(0), + message_type=None, + enum_type=None, + containing_type=None, + is_extension=False, + extension_scope=None, + options=None, + file=DESCRIPTOR), + _descriptor.FieldDescriptor(name='y', + full_name='robotics.messages.Vector4f.y', + index=1, + number=2, + type=2, + cpp_type=6, + label=1, + has_default_value=False, + default_value=float(0), + message_type=None, + enum_type=None, + containing_type=None, + is_extension=False, + extension_scope=None, + options=None, + file=DESCRIPTOR), + _descriptor.FieldDescriptor(name='z', + full_name='robotics.messages.Vector4f.z', + index=2, + number=3, + type=2, + cpp_type=6, + label=1, + has_default_value=False, + default_value=float(0), + message_type=None, + enum_type=None, + containing_type=None, + is_extension=False, + extension_scope=None, + options=None, + file=DESCRIPTOR), + _descriptor.FieldDescriptor(name='w', + full_name='robotics.messages.Vector4f.w', + index=3, + number=4, + type=2, + cpp_type=6, + label=1, + has_default_value=False, + default_value=float(0), + message_type=None, + enum_type=None, + containing_type=None, + is_extension=False, + extension_scope=None, + options=None, + file=DESCRIPTOR), + ], + extensions=[], + nested_types=[], + enum_types=[], + options=None, + is_extendable=False, + syntax='proto3', + extension_ranges=[], + oneofs=[], + serialized_start=91, + serialized_end=145, ) - _VECTOR3D = _descriptor.Descriptor( - name='Vector3d', - full_name='robotics.messages.Vector3d', - filename=None, - file=DESCRIPTOR, - containing_type=None, - fields=[ - _descriptor.FieldDescriptor( - name='x', full_name='robotics.messages.Vector3d.x', index=0, - number=1, type=1, cpp_type=5, label=1, - has_default_value=False, default_value=float(0), - message_type=None, enum_type=None, containing_type=None, - is_extension=False, extension_scope=None, - options=None, file=DESCRIPTOR), - _descriptor.FieldDescriptor( - name='y', full_name='robotics.messages.Vector3d.y', index=1, - number=2, type=1, cpp_type=5, label=1, - has_default_value=False, default_value=float(0), - message_type=None, enum_type=None, containing_type=None, - is_extension=False, extension_scope=None, - options=None, file=DESCRIPTOR), - _descriptor.FieldDescriptor( - name='z', full_name='robotics.messages.Vector3d.z', index=2, - number=3, type=1, cpp_type=5, label=1, - has_default_value=False, default_value=float(0), - message_type=None, enum_type=None, containing_type=None, - is_extension=False, extension_scope=None, - options=None, file=DESCRIPTOR), - ], - extensions=[ - ], - nested_types=[], - enum_types=[ - ], - options=None, - is_extendable=False, - syntax='proto3', - extension_ranges=[], - oneofs=[ - ], - serialized_start=147, - serialized_end=190, + name='Vector3d', + full_name='robotics.messages.Vector3d', + filename=None, + file=DESCRIPTOR, + containing_type=None, + fields=[ + _descriptor.FieldDescriptor(name='x', + full_name='robotics.messages.Vector3d.x', + index=0, + number=1, + type=1, + cpp_type=5, + label=1, + has_default_value=False, + default_value=float(0), + message_type=None, + enum_type=None, + containing_type=None, + is_extension=False, + extension_scope=None, + options=None, + file=DESCRIPTOR), + _descriptor.FieldDescriptor(name='y', + full_name='robotics.messages.Vector3d.y', + index=1, + number=2, + type=1, + cpp_type=5, + label=1, + has_default_value=False, + default_value=float(0), + message_type=None, + enum_type=None, + containing_type=None, + is_extension=False, + extension_scope=None, + options=None, + file=DESCRIPTOR), + _descriptor.FieldDescriptor(name='z', + full_name='robotics.messages.Vector3d.z', + index=2, + number=3, + type=1, + cpp_type=5, + label=1, + has_default_value=False, + default_value=float(0), + message_type=None, + enum_type=None, + containing_type=None, + is_extension=False, + extension_scope=None, + options=None, + file=DESCRIPTOR), + ], + extensions=[], + nested_types=[], + enum_types=[], + options=None, + is_extendable=False, + syntax='proto3', + extension_ranges=[], + oneofs=[], + serialized_start=147, + serialized_end=190, ) - _VECTOR3F = _descriptor.Descriptor( - name='Vector3f', - full_name='robotics.messages.Vector3f', - filename=None, - file=DESCRIPTOR, - containing_type=None, - fields=[ - _descriptor.FieldDescriptor( - name='x', full_name='robotics.messages.Vector3f.x', index=0, - number=1, type=2, cpp_type=6, label=1, - has_default_value=False, default_value=float(0), - message_type=None, enum_type=None, containing_type=None, - is_extension=False, extension_scope=None, - options=None, file=DESCRIPTOR), - _descriptor.FieldDescriptor( - name='y', full_name='robotics.messages.Vector3f.y', index=1, - number=2, type=2, cpp_type=6, label=1, - has_default_value=False, default_value=float(0), - message_type=None, enum_type=None, containing_type=None, - is_extension=False, extension_scope=None, - options=None, file=DESCRIPTOR), - _descriptor.FieldDescriptor( - name='z', full_name='robotics.messages.Vector3f.z', index=2, - number=3, type=2, cpp_type=6, label=1, - has_default_value=False, default_value=float(0), - message_type=None, enum_type=None, containing_type=None, - is_extension=False, extension_scope=None, - options=None, file=DESCRIPTOR), - ], - extensions=[ - ], - nested_types=[], - enum_types=[ - ], - options=None, - is_extendable=False, - syntax='proto3', - extension_ranges=[], - oneofs=[ - ], - serialized_start=192, - serialized_end=235, + name='Vector3f', + full_name='robotics.messages.Vector3f', + filename=None, + file=DESCRIPTOR, + containing_type=None, + fields=[ + _descriptor.FieldDescriptor(name='x', + full_name='robotics.messages.Vector3f.x', + index=0, + number=1, + type=2, + cpp_type=6, + label=1, + has_default_value=False, + default_value=float(0), + message_type=None, + enum_type=None, + containing_type=None, + is_extension=False, + extension_scope=None, + options=None, + file=DESCRIPTOR), + _descriptor.FieldDescriptor(name='y', + full_name='robotics.messages.Vector3f.y', + index=1, + number=2, + type=2, + cpp_type=6, + label=1, + has_default_value=False, + default_value=float(0), + message_type=None, + enum_type=None, + containing_type=None, + is_extension=False, + extension_scope=None, + options=None, + file=DESCRIPTOR), + _descriptor.FieldDescriptor(name='z', + full_name='robotics.messages.Vector3f.z', + index=2, + number=3, + type=2, + cpp_type=6, + label=1, + has_default_value=False, + default_value=float(0), + message_type=None, + enum_type=None, + containing_type=None, + is_extension=False, + extension_scope=None, + options=None, + file=DESCRIPTOR), + ], + extensions=[], + nested_types=[], + enum_types=[], + options=None, + is_extendable=False, + syntax='proto3', + extension_ranges=[], + oneofs=[], + serialized_start=192, + serialized_end=235, ) - _VECTOR2D = _descriptor.Descriptor( - name='Vector2d', - full_name='robotics.messages.Vector2d', - filename=None, - file=DESCRIPTOR, - containing_type=None, - fields=[ - _descriptor.FieldDescriptor( - name='x', full_name='robotics.messages.Vector2d.x', index=0, - number=1, type=1, cpp_type=5, label=1, - has_default_value=False, default_value=float(0), - message_type=None, enum_type=None, containing_type=None, - is_extension=False, extension_scope=None, - options=None, file=DESCRIPTOR), - _descriptor.FieldDescriptor( - name='y', full_name='robotics.messages.Vector2d.y', index=1, - number=2, type=1, cpp_type=5, label=1, - has_default_value=False, default_value=float(0), - message_type=None, enum_type=None, containing_type=None, - is_extension=False, extension_scope=None, - options=None, file=DESCRIPTOR), - ], - extensions=[ - ], - nested_types=[], - enum_types=[ - ], - options=None, - is_extendable=False, - syntax='proto3', - extension_ranges=[], - oneofs=[ - ], - serialized_start=237, - serialized_end=269, + name='Vector2d', + full_name='robotics.messages.Vector2d', + filename=None, + file=DESCRIPTOR, + containing_type=None, + fields=[ + _descriptor.FieldDescriptor(name='x', + full_name='robotics.messages.Vector2d.x', + index=0, + number=1, + type=1, + cpp_type=5, + label=1, + has_default_value=False, + default_value=float(0), + message_type=None, + enum_type=None, + containing_type=None, + is_extension=False, + extension_scope=None, + options=None, + file=DESCRIPTOR), + _descriptor.FieldDescriptor(name='y', + full_name='robotics.messages.Vector2d.y', + index=1, + number=2, + type=1, + cpp_type=5, + label=1, + has_default_value=False, + default_value=float(0), + message_type=None, + enum_type=None, + containing_type=None, + is_extension=False, + extension_scope=None, + options=None, + file=DESCRIPTOR), + ], + extensions=[], + nested_types=[], + enum_types=[], + options=None, + is_extendable=False, + syntax='proto3', + extension_ranges=[], + oneofs=[], + serialized_start=237, + serialized_end=269, ) - _VECTOR2F = _descriptor.Descriptor( - name='Vector2f', - full_name='robotics.messages.Vector2f', - filename=None, - file=DESCRIPTOR, - containing_type=None, - fields=[ - _descriptor.FieldDescriptor( - name='x', full_name='robotics.messages.Vector2f.x', index=0, - number=1, type=2, cpp_type=6, label=1, - has_default_value=False, default_value=float(0), - message_type=None, enum_type=None, containing_type=None, - is_extension=False, extension_scope=None, - options=None, file=DESCRIPTOR), - _descriptor.FieldDescriptor( - name='y', full_name='robotics.messages.Vector2f.y', index=1, - number=2, type=2, cpp_type=6, label=1, - has_default_value=False, default_value=float(0), - message_type=None, enum_type=None, containing_type=None, - is_extension=False, extension_scope=None, - options=None, file=DESCRIPTOR), - ], - extensions=[ - ], - nested_types=[], - enum_types=[ - ], - options=None, - is_extendable=False, - syntax='proto3', - extension_ranges=[], - oneofs=[ - ], - serialized_start=271, - serialized_end=303, + name='Vector2f', + full_name='robotics.messages.Vector2f', + filename=None, + file=DESCRIPTOR, + containing_type=None, + fields=[ + _descriptor.FieldDescriptor(name='x', + full_name='robotics.messages.Vector2f.x', + index=0, + number=1, + type=2, + cpp_type=6, + label=1, + has_default_value=False, + default_value=float(0), + message_type=None, + enum_type=None, + containing_type=None, + is_extension=False, + extension_scope=None, + options=None, + file=DESCRIPTOR), + _descriptor.FieldDescriptor(name='y', + full_name='robotics.messages.Vector2f.y', + index=1, + number=2, + type=2, + cpp_type=6, + label=1, + has_default_value=False, + default_value=float(0), + message_type=None, + enum_type=None, + containing_type=None, + is_extension=False, + extension_scope=None, + options=None, + file=DESCRIPTOR), + ], + extensions=[], + nested_types=[], + enum_types=[], + options=None, + is_extendable=False, + syntax='proto3', + extension_ranges=[], + oneofs=[], + serialized_start=271, + serialized_end=303, ) - _VECTORD = _descriptor.Descriptor( - name='Vectord', - full_name='robotics.messages.Vectord', - filename=None, - file=DESCRIPTOR, - containing_type=None, - fields=[ - _descriptor.FieldDescriptor( - name='data', full_name='robotics.messages.Vectord.data', index=0, - number=1, type=1, cpp_type=5, label=3, - has_default_value=False, default_value=[], - message_type=None, enum_type=None, containing_type=None, - is_extension=False, extension_scope=None, - options=_descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\020\001')), file=DESCRIPTOR), - ], - extensions=[ - ], - nested_types=[], - enum_types=[ - ], - options=None, - is_extendable=False, - syntax='proto3', - extension_ranges=[], - oneofs=[ - ], - serialized_start=305, - serialized_end=332, + name='Vectord', + full_name='robotics.messages.Vectord', + filename=None, + file=DESCRIPTOR, + containing_type=None, + fields=[ + _descriptor.FieldDescriptor(name='data', + full_name='robotics.messages.Vectord.data', + index=0, + number=1, + type=1, + cpp_type=5, + label=3, + has_default_value=False, + default_value=[], + message_type=None, + enum_type=None, + containing_type=None, + is_extension=False, + extension_scope=None, + options=_descriptor._ParseOptions( + descriptor_pb2.FieldOptions(), _b('\020\001')), + file=DESCRIPTOR), + ], + extensions=[], + nested_types=[], + enum_types=[], + options=None, + is_extendable=False, + syntax='proto3', + extension_ranges=[], + oneofs=[], + serialized_start=305, + serialized_end=332, ) - _VECTORF = _descriptor.Descriptor( - name='Vectorf', - full_name='robotics.messages.Vectorf', - filename=None, - file=DESCRIPTOR, - containing_type=None, - fields=[ - _descriptor.FieldDescriptor( - name='data', full_name='robotics.messages.Vectorf.data', index=0, - number=1, type=2, cpp_type=6, label=3, - has_default_value=False, default_value=[], - message_type=None, enum_type=None, containing_type=None, - is_extension=False, extension_scope=None, - options=_descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\020\001')), file=DESCRIPTOR), - ], - extensions=[ - ], - nested_types=[], - enum_types=[ - ], - options=None, - is_extendable=False, - syntax='proto3', - extension_ranges=[], - oneofs=[ - ], - serialized_start=334, - serialized_end=361, + name='Vectorf', + full_name='robotics.messages.Vectorf', + filename=None, + file=DESCRIPTOR, + containing_type=None, + fields=[ + _descriptor.FieldDescriptor(name='data', + full_name='robotics.messages.Vectorf.data', + index=0, + number=1, + type=2, + cpp_type=6, + label=3, + has_default_value=False, + default_value=[], + message_type=None, + enum_type=None, + containing_type=None, + is_extension=False, + extension_scope=None, + options=_descriptor._ParseOptions( + descriptor_pb2.FieldOptions(), _b('\020\001')), + file=DESCRIPTOR), + ], + extensions=[], + nested_types=[], + enum_types=[], + options=None, + is_extendable=False, + syntax='proto3', + extension_ranges=[], + oneofs=[], + serialized_start=334, + serialized_end=361, ) DESCRIPTOR.message_types_by_name['Vector4d'] = _VECTOR4D @@ -366,65 +512,82 @@ DESCRIPTOR.message_types_by_name['Vectord'] = _VECTORD DESCRIPTOR.message_types_by_name['Vectorf'] = _VECTORF _sym_db.RegisterFileDescriptor(DESCRIPTOR) -Vector4d = _reflection.GeneratedProtocolMessageType('Vector4d', (_message.Message,), dict( - DESCRIPTOR = _VECTOR4D, - __module__ = 'vector_pb2' - # @@protoc_insertion_point(class_scope:robotics.messages.Vector4d) - )) +Vector4d = _reflection.GeneratedProtocolMessageType( + 'Vector4d', + (_message.Message,), + dict(DESCRIPTOR=_VECTOR4D, + __module__='vector_pb2' + # @@protoc_insertion_point(class_scope:robotics.messages.Vector4d) + )) _sym_db.RegisterMessage(Vector4d) -Vector4f = _reflection.GeneratedProtocolMessageType('Vector4f', (_message.Message,), dict( - DESCRIPTOR = _VECTOR4F, - __module__ = 'vector_pb2' - # @@protoc_insertion_point(class_scope:robotics.messages.Vector4f) - )) +Vector4f = _reflection.GeneratedProtocolMessageType( + 'Vector4f', + (_message.Message,), + dict(DESCRIPTOR=_VECTOR4F, + __module__='vector_pb2' + # @@protoc_insertion_point(class_scope:robotics.messages.Vector4f) + )) _sym_db.RegisterMessage(Vector4f) -Vector3d = _reflection.GeneratedProtocolMessageType('Vector3d', (_message.Message,), dict( - DESCRIPTOR = _VECTOR3D, - __module__ = 'vector_pb2' - # @@protoc_insertion_point(class_scope:robotics.messages.Vector3d) - )) +Vector3d = _reflection.GeneratedProtocolMessageType( + 'Vector3d', + (_message.Message,), + dict(DESCRIPTOR=_VECTOR3D, + __module__='vector_pb2' + # @@protoc_insertion_point(class_scope:robotics.messages.Vector3d) + )) _sym_db.RegisterMessage(Vector3d) -Vector3f = _reflection.GeneratedProtocolMessageType('Vector3f', (_message.Message,), dict( - DESCRIPTOR = _VECTOR3F, - __module__ = 'vector_pb2' - # @@protoc_insertion_point(class_scope:robotics.messages.Vector3f) - )) +Vector3f = _reflection.GeneratedProtocolMessageType( + 'Vector3f', + (_message.Message,), + dict(DESCRIPTOR=_VECTOR3F, + __module__='vector_pb2' + # @@protoc_insertion_point(class_scope:robotics.messages.Vector3f) + )) _sym_db.RegisterMessage(Vector3f) -Vector2d = _reflection.GeneratedProtocolMessageType('Vector2d', (_message.Message,), dict( - DESCRIPTOR = _VECTOR2D, - __module__ = 'vector_pb2' - # @@protoc_insertion_point(class_scope:robotics.messages.Vector2d) - )) +Vector2d = _reflection.GeneratedProtocolMessageType( + 'Vector2d', + (_message.Message,), + dict(DESCRIPTOR=_VECTOR2D, + __module__='vector_pb2' + # @@protoc_insertion_point(class_scope:robotics.messages.Vector2d) + )) _sym_db.RegisterMessage(Vector2d) -Vector2f = _reflection.GeneratedProtocolMessageType('Vector2f', (_message.Message,), dict( - DESCRIPTOR = _VECTOR2F, - __module__ = 'vector_pb2' - # @@protoc_insertion_point(class_scope:robotics.messages.Vector2f) - )) +Vector2f = _reflection.GeneratedProtocolMessageType( + 'Vector2f', + (_message.Message,), + dict(DESCRIPTOR=_VECTOR2F, + __module__='vector_pb2' + # @@protoc_insertion_point(class_scope:robotics.messages.Vector2f) + )) _sym_db.RegisterMessage(Vector2f) -Vectord = _reflection.GeneratedProtocolMessageType('Vectord', (_message.Message,), dict( - DESCRIPTOR = _VECTORD, - __module__ = 'vector_pb2' - # @@protoc_insertion_point(class_scope:robotics.messages.Vectord) - )) +Vectord = _reflection.GeneratedProtocolMessageType( + 'Vectord', + (_message.Message,), + dict(DESCRIPTOR=_VECTORD, + __module__='vector_pb2' + # @@protoc_insertion_point(class_scope:robotics.messages.Vectord) + )) _sym_db.RegisterMessage(Vectord) -Vectorf = _reflection.GeneratedProtocolMessageType('Vectorf', (_message.Message,), dict( - DESCRIPTOR = _VECTORF, - __module__ = 'vector_pb2' - # @@protoc_insertion_point(class_scope:robotics.messages.Vectorf) - )) +Vectorf = _reflection.GeneratedProtocolMessageType( + 'Vectorf', + (_message.Message,), + dict(DESCRIPTOR=_VECTORF, + __module__='vector_pb2' + # @@protoc_insertion_point(class_scope:robotics.messages.Vectorf) + )) _sym_db.RegisterMessage(Vectorf) - _VECTORD.fields_by_name['data'].has_options = True -_VECTORD.fields_by_name['data']._options = _descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\020\001')) +_VECTORD.fields_by_name['data']._options = _descriptor._ParseOptions(descriptor_pb2.FieldOptions(), + _b('\020\001')) _VECTORF.fields_by_name['data'].has_options = True -_VECTORF.fields_by_name['data']._options = _descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\020\001')) +_VECTORF.fields_by_name['data']._options = _descriptor._ParseOptions(descriptor_pb2.FieldOptions(), + _b('\020\001')) # @@protoc_insertion_point(module_scope) diff --git a/examples/pybullet/gym/pybullet_envs/prediction/__init__.py b/examples/pybullet/gym/pybullet_envs/prediction/__init__.py index 26f76e102..a575195ec 100644 --- a/examples/pybullet/gym/pybullet_envs/prediction/__init__.py +++ b/examples/pybullet/gym/pybullet_envs/prediction/__init__.py @@ -1,3 +1,2 @@ from . import boxstack_pybullet_sim from . import pybullet_sim_gym_env - diff --git a/examples/pybullet/gym/pybullet_envs/prediction/boxstack_pybullet_sim.py b/examples/pybullet/gym/pybullet_envs/prediction/boxstack_pybullet_sim.py index 42bc3cffa..85e29cc60 100644 --- a/examples/pybullet/gym/pybullet_envs/prediction/boxstack_pybullet_sim.py +++ b/examples/pybullet/gym/pybullet_envs/prediction/boxstack_pybullet_sim.py @@ -14,10 +14,7 @@ class BoxStackPyBulletSim(object): """ - def __init__(self, - pybullet_client, - urdf_root= pybullet_data.getDataPath(), - time_step=0.01): + def __init__(self, pybullet_client, urdf_root=pybullet_data.getDataPath(), time_step=0.01): """Constructs an example simulation and reset it to the initial states. Args: @@ -28,11 +25,10 @@ class BoxStackPyBulletSim(object): """ self._pybullet_client = pybullet_client self._urdf_root = urdf_root - self.m_actions_taken_since_reset=0 + self.m_actions_taken_since_reset = 0 self.time_step = time_step self.stateId = -1 self.Reset(reload_urdf=True) - def Reset(self, reload_urdf=False): """Reset the minitaur to its initial states. @@ -41,58 +37,59 @@ class BoxStackPyBulletSim(object): reload_urdf: Whether to reload the urdf file. If not, Reset() just place the minitaur back to its starting position. """ - self.m_actions_taken_since_reset=0 - xPosRange=0.025 - yPosRange=0.025 + self.m_actions_taken_since_reset = 0 + xPosRange = 0.025 + yPosRange = 0.025 boxHalfExtents = 0.025 - + if reload_urdf: camInfo = self._pybullet_client.getDebugVisualizerCamera() - cameraDistance=camInfo[10] - print("cameraYaw=",camInfo[8]) - print("cameraPitch=",camInfo[9]) - print("camtarget=",camInfo[11]) - print("projectionMatrix=",camInfo[3]) - self._pybullet_client.resetDebugVisualizerCamera(cameraDistance=0.3, cameraYaw=camInfo[8], cameraPitch=camInfo[9],cameraTargetPosition=camInfo[11]) + cameraDistance = camInfo[10] + print("cameraYaw=", camInfo[8]) + print("cameraPitch=", camInfo[9]) + print("camtarget=", camInfo[11]) + print("projectionMatrix=", camInfo[3]) + self._pybullet_client.resetDebugVisualizerCamera(cameraDistance=0.3, + cameraYaw=camInfo[8], + cameraPitch=camInfo[9], + cameraTargetPosition=camInfo[11]) plane = self._pybullet_client.loadURDF("plane.urdf") texUid = self._pybullet_client.loadTexture("checker_blue.png") - self._pybullet_client.changeVisualShape(plane,-1, textureUniqueId = texUid) - - - self._numObjects=4 #random number? - - - self._cubes=[] - - red=[0.97,0.25,0.25,1] - green=[0.41,0.68,0.31,1] - yellow=[0.92,0.73,0,1] - blue=[0,0.55,0.81,1] - colors=[red,green,yellow,blue] - - for i in range (self._numObjects): - pos=[0,0,boxHalfExtents + i*2*boxHalfExtents] - orn = self._pybullet_client.getQuaternionFromEuler([0,0,0]) - orn=[0,0,0,1] - cube = self._pybullet_client.loadURDF("cube_small.urdf",pos,orn) - self._pybullet_client.changeVisualShape(cube,-1,rgbaColor=colors[i]) - self._cubes.append(cube) - + self._pybullet_client.changeVisualShape(plane, -1, textureUniqueId=texUid) + + self._numObjects = 4 #random number? + + self._cubes = [] + + red = [0.97, 0.25, 0.25, 1] + green = [0.41, 0.68, 0.31, 1] + yellow = [0.92, 0.73, 0, 1] + blue = [0, 0.55, 0.81, 1] + colors = [red, green, yellow, blue] + + for i in range(self._numObjects): + pos = [0, 0, boxHalfExtents + i * 2 * boxHalfExtents] + orn = self._pybullet_client.getQuaternionFromEuler([0, 0, 0]) + orn = [0, 0, 0, 1] + cube = self._pybullet_client.loadURDF("cube_small.urdf", pos, orn) + self._pybullet_client.changeVisualShape(cube, -1, rgbaColor=colors[i]) + self._cubes.append(cube) + self._pybullet_client.setGravity(0, 0, -10) self.stateId = self._pybullet_client.saveState() else: - if (self.stateId>=0): + if (self.stateId >= 0): self._pybullet_client.restoreState(self.stateId) - index=0 + index = 0 for i in self._cubes: - posX = random.uniform(-xPosRange,xPosRange) - posY = random.uniform(-yPosRange,yPosRange) - yaw = random.uniform(-math.pi,math.pi) - pos=[posX,posY,boxHalfExtents + index*2*boxHalfExtents] - index+=1 - orn = self._pybullet_client.getQuaternionFromEuler([0,0,yaw]) - self._pybullet_client.resetBasePositionAndOrientation(i,pos,orn) + posX = random.uniform(-xPosRange, xPosRange) + posY = random.uniform(-yPosRange, yPosRange) + yaw = random.uniform(-math.pi, math.pi) + pos = [posX, posY, boxHalfExtents + index * 2 * boxHalfExtents] + index += 1 + orn = self._pybullet_client.getQuaternionFromEuler([0, 0, yaw]) + self._pybullet_client.resetBasePositionAndOrientation(i, pos, orn) def GetActionDimension(self): """Get the length of the action list. @@ -100,7 +97,7 @@ class BoxStackPyBulletSim(object): Returns: The length of the action list. """ - return 4#self.num_motors + return 4 #self.num_motors def GetObservationUpperBound(self): """Get the upper bound of the observation. @@ -124,7 +121,7 @@ class BoxStackPyBulletSim(object): The length of the observation list. """ sz = len(self.GetObservation()) - print("sz=",sz) + print("sz=", sz) return sz def GetObservation(self): @@ -135,7 +132,7 @@ class BoxStackPyBulletSim(object): """ observation = [] for i in self._cubes: - pos,orn=self._pybullet_client.getBasePositionAndOrientation(i) + pos, orn = self._pybullet_client.getBasePositionAndOrientation(i) observation.extend(list(pos)) observation.extend(list(orn)) return observation @@ -143,14 +140,14 @@ class BoxStackPyBulletSim(object): def ApplyAction(self, action): """Set the desired action. """ - self.m_actions_taken_since_reset+=1 - + self.m_actions_taken_since_reset += 1 def Termination(self): - return self.m_actions_taken_since_reset>=200 - -def CreateSim(pybullet_client,urdf_root,time_step): + return self.m_actions_taken_since_reset >= 200 + + +def CreateSim(pybullet_client, urdf_root, time_step): sim = BoxStackPyBulletSim(pybullet_client=pybullet_client, - urdf_root=urdf_root, - time_step=time_step) + urdf_root=urdf_root, + time_step=time_step) return sim diff --git a/examples/pybullet/gym/pybullet_envs/prediction/pybullet_sim_gym_env.py b/examples/pybullet/gym/pybullet_envs/prediction/pybullet_sim_gym_env.py index 2f451529a..1c308a7fb 100644 --- a/examples/pybullet/gym/pybullet_envs/prediction/pybullet_sim_gym_env.py +++ b/examples/pybullet/gym/pybullet_envs/prediction/pybullet_sim_gym_env.py @@ -5,8 +5,7 @@ import os, inspect currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe()))) parentdir = os.path.dirname(os.path.dirname(currentdir)) -os.sys.path.insert(0,parentdir) - +os.sys.path.insert(0, parentdir) import math import time @@ -30,20 +29,18 @@ class PyBulletSimGymEnv(gym.Env): """ - metadata = { - "render.modes": ["human", "rgb_array"], - "video.frames_per_second": 50 - } + metadata = {"render.modes": ["human", "rgb_array"], "video.frames_per_second": 50} - def __init__(self,pybullet_sim_factory = boxstack_pybullet_sim, + def __init__(self, + pybullet_sim_factory=boxstack_pybullet_sim, render=True, render_sleep=False, debug_visualization=True, - hard_reset = False, + hard_reset=False, render_width=240, render_height=240, action_repeat=1, - time_step = 1./240., + time_step=1. / 240., num_bullet_solver_iterations=50, urdf_root=pybullet_data.getDataPath()): """Initialize the gym environment. @@ -55,45 +52,46 @@ class PyBulletSimGymEnv(gym.Env): self._time_step = time_step self._urdf_root = urdf_root self._observation = [] - self._action_repeat=action_repeat + self._action_repeat = action_repeat self._num_bullet_solver_iterations = num_bullet_solver_iterations self._env_step_counter = 0 self._is_render = render self._debug_visualization = debug_visualization self._render_sleep = render_sleep - self._render_width=render_width - self._render_height=render_height + self._render_width = render_width + self._render_height = render_height self._cam_dist = .3 self._cam_yaw = 50 self._cam_pitch = -35 self._hard_reset = True self._last_frame_time = 0.0 - optionstring='--width={} --height={}'.format(render_width,render_height) + optionstring = '--width={} --height={}'.format(render_width, render_height) print("urdf_root=" + self._urdf_root) if self._is_render: - self._pybullet_client = bullet_client.BulletClient( - connection_mode=pybullet.GUI, options=optionstring) + self._pybullet_client = bullet_client.BulletClient(connection_mode=pybullet.GUI, + options=optionstring) else: self._pybullet_client = bullet_client.BulletClient() - if (debug_visualization==False): - self._pybullet_client.configureDebugVisualizer(flag=self._pybullet_client.COV_ENABLE_GUI,enable=0) - self._pybullet_client.configureDebugVisualizer(flag=self._pybullet_client.COV_ENABLE_RGB_BUFFER_PREVIEW,enable=0) - self._pybullet_client.configureDebugVisualizer(flag=self._pybullet_client.COV_ENABLE_DEPTH_BUFFER_PREVIEW,enable=0) - self._pybullet_client.configureDebugVisualizer(flag=self._pybullet_client.COV_ENABLE_SEGMENTATION_MARK_PREVIEW,enable=0) - + if (debug_visualization == False): + self._pybullet_client.configureDebugVisualizer(flag=self._pybullet_client.COV_ENABLE_GUI, + enable=0) + self._pybullet_client.configureDebugVisualizer( + flag=self._pybullet_client.COV_ENABLE_RGB_BUFFER_PREVIEW, enable=0) + self._pybullet_client.configureDebugVisualizer( + flag=self._pybullet_client.COV_ENABLE_DEPTH_BUFFER_PREVIEW, enable=0) + self._pybullet_client.configureDebugVisualizer( + flag=self._pybullet_client.COV_ENABLE_SEGMENTATION_MARK_PREVIEW, enable=0) self._pybullet_client.setAdditionalSearchPath(urdf_root) self.seed() self.reset() - observation_high = ( - self._example_sim.GetObservationUpperBound()) - observation_low = ( - self._example_sim.GetObservationLowerBound()) + observation_high = (self._example_sim.GetObservationUpperBound()) + observation_low = (self._example_sim.GetObservationLowerBound()) action_dim = self._example_sim.GetActionDimension() self._action_bound = 1 @@ -104,7 +102,6 @@ class PyBulletSimGymEnv(gym.Env): self.viewer = None self._hard_reset = hard_reset # This assignment need to be after reset() - def configure(self, args): self._args = args @@ -163,7 +160,6 @@ class PyBulletSimGymEnv(gym.Env): #self._pybullet_client.resetDebugVisualizerCamera( # self._cam_dist, self._cam_yaw, self._cam_pitch, base_pos) - for _ in range(self._action_repeat): self._example_sim.ApplyAction(action) self._pybullet_client.stepSimulation() @@ -176,7 +172,7 @@ class PyBulletSimGymEnv(gym.Env): def render(self, mode="rgb_array", close=False): if mode != "rgb_array": return np.array([]) - base_pos = [0,0,0] + base_pos = [0, 0, 0] view_matrix = self._pybullet_client.computeViewMatrixFromYawPitchRoll( cameraTargetPosition=base_pos, distance=self._cam_dist, @@ -185,19 +181,24 @@ class PyBulletSimGymEnv(gym.Env): roll=0, upAxisIndex=2) proj_matrix = self._pybullet_client.computeProjectionMatrixFOV( - fov=60, aspect=float(self._render_width)/self._render_width, - nearVal=0.01, farVal=100.0) - proj_matrix=[1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, -1.0000200271606445, -1.0, 0.0, 0.0, -0.02000020071864128, 0.0] + fov=60, aspect=float(self._render_width) / self._render_width, nearVal=0.01, farVal=100.0) + proj_matrix = [ + 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, -1.0000200271606445, -1.0, 0.0, 0.0, + -0.02000020071864128, 0.0 + ] (_, _, px, _, _) = self._pybullet_client.getCameraImage( - width=self._render_width, height=self._render_height, viewMatrix=view_matrix, - projectionMatrix=proj_matrix, renderer=pybullet.ER_BULLET_HARDWARE_OPENGL)#ER_TINY_RENDERER) + width=self._render_width, + height=self._render_height, + viewMatrix=view_matrix, + projectionMatrix=proj_matrix, + renderer=pybullet.ER_BULLET_HARDWARE_OPENGL) #ER_TINY_RENDERER) rgb_array = np.array(px, dtype=np.uint8) rgb_array = np.reshape(rgb_array, (self._render_height, self._render_width, 4)) rgb_array = rgb_array[:, :, :3] return rgb_array def _termination(self): - terminate=self._example_sim.Termination() + terminate = self._example_sim.Termination() return terminate def _reward(self): diff --git a/examples/pybullet/gym/pybullet_envs/prediction/test_pybullet_sim_gym_env.py b/examples/pybullet/gym/pybullet_envs/prediction/test_pybullet_sim_gym_env.py index 23f734e4e..ae24b3a9d 100644 --- a/examples/pybullet/gym/pybullet_envs/prediction/test_pybullet_sim_gym_env.py +++ b/examples/pybullet/gym/pybullet_envs/prediction/test_pybullet_sim_gym_env.py @@ -5,7 +5,7 @@ import os import inspect currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe()))) parentdir = os.path.dirname(os.path.dirname(currentdir)) -os.sys.path.insert(0,parentdir) +os.sys.path.insert(0, parentdir) import math import numpy as np @@ -16,18 +16,18 @@ import time from pybullet_envs.prediction import boxstack_pybullet_sim from gym.wrappers.monitoring import video_recorder + def ResetPoseExample(steps): """An example that the minitaur stands still using the reset pose.""" - - - environment = pybullet_sim_gym_env.PyBulletSimGymEnv( - pybullet_sim_factory=boxstack_pybullet_sim, - debug_visualization=False, - render=True, action_repeat=30) + + environment = pybullet_sim_gym_env.PyBulletSimGymEnv(pybullet_sim_factory=boxstack_pybullet_sim, + debug_visualization=False, + render=True, + action_repeat=30) action = [math.pi / 2] * 8 - - vid = video_recorder.VideoRecorder(env=environment,path="vid.mp4") - + + vid = video_recorder.VideoRecorder(env=environment, path="vid.mp4") + for _ in range(steps): print(_) startsim = time.time() @@ -37,24 +37,21 @@ def ResetPoseExample(steps): #environment.render(mode='rgb_array') vid.capture_frame() stoprender = time.time() - print ("env.step " , (stopsim - startsim)) - print ("env.render " , (stoprender - startrender)) + print("env.step ", (stopsim - startsim)) + print("env.render ", (stoprender - startrender)) if done: - environment.reset() - - + environment.reset() def main(): - parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter) - parser.add_argument('--env', help='environment ID (0==reset)',type=int, default=0) - args = parser.parse_args() - print("--env=" + str(args.env)) - - if (args.env == 0): - ResetPoseExample(steps = 1000) + parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter) + parser.add_argument('--env', help='environment ID (0==reset)', type=int, default=0) + args = parser.parse_args() + print("--env=" + str(args.env)) -if __name__ == '__main__': - main() + if (args.env == 0): + ResetPoseExample(steps=1000) +if __name__ == '__main__': + main() diff --git a/examples/pybullet/gym/pybullet_envs/robot_bases.py b/examples/pybullet/gym/pybullet_envs/robot_bases.py index 36e5b8c76..51c2791c1 100644 --- a/examples/pybullet/gym/pybullet_envs/robot_bases.py +++ b/examples/pybullet/gym/pybullet_envs/robot_bases.py @@ -4,339 +4,404 @@ import numpy as np import os, inspect currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe()))) parentdir = os.path.dirname(currentdir) -os.sys.path.insert(0,parentdir) +os.sys.path.insert(0, parentdir) import pybullet_data class XmlBasedRobot: - """ + """ Base class for mujoco .xml based agents. """ - self_collision = True - def __init__(self, robot_name, action_dim, obs_dim, self_collision): - self.parts = None - self.objects = [] - self.jdict = None - self.ordered_joints = None - self.robot_body = None - - high = np.ones([action_dim]) - self.action_space = gym.spaces.Box(-high, high) - high = np.inf * np.ones([obs_dim]) - self.observation_space = gym.spaces.Box(-high, high) - - #self.model_xml = model_xml - self.robot_name = robot_name - self.self_collision = self_collision - - def addToScene(self, bullet_client, bodies): - self._p = bullet_client - - if self.parts is not None: - parts = self.parts - else: - parts = {} - - if self.jdict is not None: - joints = self.jdict - else: - joints = {} - - if self.ordered_joints is not None: - ordered_joints = self.ordered_joints - else: - ordered_joints = [] - - if np.isscalar(bodies): # streamline the case where bodies is actually just one body - bodies = [bodies] - - dump = 0 - for i in range(len(bodies)): - if self._p.getNumJoints(bodies[i]) == 0: - part_name, robot_name = self._p.getBodyInfo(bodies[i]) - self.robot_name = robot_name.decode("utf8") - part_name = part_name.decode("utf8") - parts[part_name] = BodyPart(self._p, part_name, bodies, i, -1) - for j in range(self._p.getNumJoints(bodies[i])): - self._p.setJointMotorControl2(bodies[i],j,pybullet.POSITION_CONTROL,positionGain=0.1,velocityGain=0.1,force=0) - jointInfo = self._p.getJointInfo(bodies[i], j) - joint_name=jointInfo[1] - part_name=jointInfo[12] - - joint_name = joint_name.decode("utf8") - part_name = part_name.decode("utf8") - - if dump: print("ROBOT PART '%s'" % part_name) - if dump: print("ROBOT JOINT '%s'" % joint_name) # limits = %+0.2f..%+0.2f effort=%0.3f speed=%0.3f" % ((joint_name,) + j.limits()) ) - - parts[part_name] = BodyPart(self._p, part_name, bodies, i, j) - - if part_name == self.robot_name: - self.robot_body = parts[part_name] + self_collision = True + + def __init__(self, robot_name, action_dim, obs_dim, self_collision): + self.parts = None + self.objects = [] + self.jdict = None + self.ordered_joints = None + self.robot_body = None + + high = np.ones([action_dim]) + self.action_space = gym.spaces.Box(-high, high) + high = np.inf * np.ones([obs_dim]) + self.observation_space = gym.spaces.Box(-high, high) + + #self.model_xml = model_xml + self.robot_name = robot_name + self.self_collision = self_collision + + def addToScene(self, bullet_client, bodies): + self._p = bullet_client + + if self.parts is not None: + parts = self.parts + else: + parts = {} + + if self.jdict is not None: + joints = self.jdict + else: + joints = {} + + if self.ordered_joints is not None: + ordered_joints = self.ordered_joints + else: + ordered_joints = [] + + if np.isscalar(bodies): # streamline the case where bodies is actually just one body + bodies = [bodies] + + dump = 0 + for i in range(len(bodies)): + if self._p.getNumJoints(bodies[i]) == 0: + part_name, robot_name = self._p.getBodyInfo(bodies[i]) + self.robot_name = robot_name.decode("utf8") + part_name = part_name.decode("utf8") + parts[part_name] = BodyPart(self._p, part_name, bodies, i, -1) + for j in range(self._p.getNumJoints(bodies[i])): + self._p.setJointMotorControl2(bodies[i], + j, + pybullet.POSITION_CONTROL, + positionGain=0.1, + velocityGain=0.1, + force=0) + jointInfo = self._p.getJointInfo(bodies[i], j) + joint_name = jointInfo[1] + part_name = jointInfo[12] + + joint_name = joint_name.decode("utf8") + part_name = part_name.decode("utf8") + + if dump: print("ROBOT PART '%s'" % part_name) + if dump: + print( + "ROBOT JOINT '%s'" % joint_name + ) # limits = %+0.2f..%+0.2f effort=%0.3f speed=%0.3f" % ((joint_name,) + j.limits()) ) + + parts[part_name] = BodyPart(self._p, part_name, bodies, i, j) + + if part_name == self.robot_name: + self.robot_body = parts[part_name] + + if i == 0 and j == 0 and self.robot_body is None: # if nothing else works, we take this as robot_body + parts[self.robot_name] = BodyPart(self._p, self.robot_name, bodies, 0, -1) + self.robot_body = parts[self.robot_name] + + if joint_name[:6] == "ignore": + Joint(self._p, joint_name, bodies, i, j).disable_motor() + continue + + if joint_name[:8] != "jointfix": + joints[joint_name] = Joint(self._p, joint_name, bodies, i, j) + ordered_joints.append(joints[joint_name]) + + joints[joint_name].power_coef = 100.0 + + # TODO: Maybe we need this + # joints[joint_name].power_coef, joints[joint_name].max_velocity = joints[joint_name].limits()[2:4] + # self.ordered_joints.append(joints[joint_name]) + # self.jdict[joint_name] = joints[joint_name] + + return parts, joints, ordered_joints, self.robot_body + + def reset_pose(self, position, orientation): + self.parts[self.robot_name].reset_pose(position, orientation) - if i == 0 and j == 0 and self.robot_body is None: # if nothing else works, we take this as robot_body - parts[self.robot_name] = BodyPart(self._p, self.robot_name, bodies, 0, -1) - self.robot_body = parts[self.robot_name] - - if joint_name[:6] == "ignore": - Joint(self._p, joint_name, bodies, i, j).disable_motor() - continue - - if joint_name[:8] != "jointfix": - joints[joint_name] = Joint(self._p, joint_name, bodies, i, j) - ordered_joints.append(joints[joint_name]) - - joints[joint_name].power_coef = 100.0 - - # TODO: Maybe we need this - # joints[joint_name].power_coef, joints[joint_name].max_velocity = joints[joint_name].limits()[2:4] - # self.ordered_joints.append(joints[joint_name]) - # self.jdict[joint_name] = joints[joint_name] - - return parts, joints, ordered_joints, self.robot_body - - def reset_pose(self, position, orientation): - self.parts[self.robot_name].reset_pose(position, orientation) class MJCFBasedRobot(XmlBasedRobot): - """ + """ Base class for mujoco .xml based agents. """ - def __init__(self, model_xml, robot_name, action_dim, obs_dim, self_collision=True): - XmlBasedRobot.__init__(self, robot_name, action_dim, obs_dim, self_collision) - self.model_xml = model_xml - self.doneLoading=0 - def reset(self, bullet_client): - - self._p = bullet_client - #print("Created bullet_client with id=", self._p._client) - if (self.doneLoading==0): - self.ordered_joints = [] - self.doneLoading=1 - if self.self_collision: - self.objects = self._p.loadMJCF(os.path.join(pybullet_data.getDataPath(),"mjcf", self.model_xml), flags=pybullet.URDF_USE_SELF_COLLISION|pybullet.URDF_USE_SELF_COLLISION_EXCLUDE_ALL_PARENTS) - self.parts, self.jdict, self.ordered_joints, self.robot_body = self.addToScene(self._p, self.objects ) - else: - self.objects = self._p.loadMJCF(os.path.join(pybullet_data.getDataPath(),"mjcf", self.model_xml)) - self.parts, self.jdict, self.ordered_joints, self.robot_body = self.addToScene(self._p, self.objects) - self.robot_specific_reset(self._p) - - s = self.calc_state() # optimization: calc_state() can calculate something in self.* for calc_potential() to use - - return s - - def calc_potential(self): - return 0 + def __init__(self, model_xml, robot_name, action_dim, obs_dim, self_collision=True): + XmlBasedRobot.__init__(self, robot_name, action_dim, obs_dim, self_collision) + self.model_xml = model_xml + self.doneLoading = 0 + + def reset(self, bullet_client): + + self._p = bullet_client + #print("Created bullet_client with id=", self._p._client) + if (self.doneLoading == 0): + self.ordered_joints = [] + self.doneLoading = 1 + if self.self_collision: + self.objects = self._p.loadMJCF(os.path.join(pybullet_data.getDataPath(), "mjcf", + self.model_xml), + flags=pybullet.URDF_USE_SELF_COLLISION | + pybullet.URDF_USE_SELF_COLLISION_EXCLUDE_ALL_PARENTS) + self.parts, self.jdict, self.ordered_joints, self.robot_body = self.addToScene( + self._p, self.objects) + else: + self.objects = self._p.loadMJCF( + os.path.join(pybullet_data.getDataPath(), "mjcf", self.model_xml)) + self.parts, self.jdict, self.ordered_joints, self.robot_body = self.addToScene( + self._p, self.objects) + self.robot_specific_reset(self._p) + + s = self.calc_state( + ) # optimization: calc_state() can calculate something in self.* for calc_potential() to use + + return s + + def calc_potential(self): + return 0 class URDFBasedRobot(XmlBasedRobot): - """ + """ Base class for URDF .xml based robots. """ - def __init__(self, model_urdf, robot_name, action_dim, obs_dim, basePosition=[0, 0, 0], baseOrientation=[0, 0, 0, 1], fixed_base=False, self_collision=False): - XmlBasedRobot.__init__(self, robot_name, action_dim, obs_dim, self_collision) - - self.model_urdf = model_urdf - self.basePosition = basePosition - self.baseOrientation = baseOrientation - self.fixed_base = fixed_base - - def reset(self, bullet_client): - self._p = bullet_client - self.ordered_joints = [] - - print(os.path.join(os.path.dirname(__file__), "data", self.model_urdf)) - - if self.self_collision: - self.parts, self.jdict, self.ordered_joints, self.robot_body = self.addToScene(self._p, - self._p.loadURDF(os.path.join(pybullet_data.getDataPath(), self.model_urdf), - basePosition=self.basePosition, - baseOrientation=self.baseOrientation, - useFixedBase=self.fixed_base, - flags=pybullet.URDF_USE_SELF_COLLISION)) - else: - self.parts, self.jdict, self.ordered_joints, self.robot_body = self.addToScene(self._p, - self._p.loadURDF(os.path.join(pybullet_data.getDataPath(), self.model_urdf), - basePosition=self.basePosition, - baseOrientation=self.baseOrientation, - useFixedBase=self.fixed_base)) - - self.robot_specific_reset(self._p) - - s = self.calc_state() # optimization: calc_state() can calculate something in self.* for calc_potential() to use - self.potential = self.calc_potential() - - return s - - def calc_potential(self): - return 0 + def __init__(self, + model_urdf, + robot_name, + action_dim, + obs_dim, + basePosition=[0, 0, 0], + baseOrientation=[0, 0, 0, 1], + fixed_base=False, + self_collision=False): + XmlBasedRobot.__init__(self, robot_name, action_dim, obs_dim, self_collision) + + self.model_urdf = model_urdf + self.basePosition = basePosition + self.baseOrientation = baseOrientation + self.fixed_base = fixed_base + + def reset(self, bullet_client): + self._p = bullet_client + self.ordered_joints = [] + + print(os.path.join(os.path.dirname(__file__), "data", self.model_urdf)) + + if self.self_collision: + self.parts, self.jdict, self.ordered_joints, self.robot_body = self.addToScene( + self._p, + self._p.loadURDF(os.path.join(pybullet_data.getDataPath(), self.model_urdf), + basePosition=self.basePosition, + baseOrientation=self.baseOrientation, + useFixedBase=self.fixed_base, + flags=pybullet.URDF_USE_SELF_COLLISION)) + else: + self.parts, self.jdict, self.ordered_joints, self.robot_body = self.addToScene( + self._p, + self._p.loadURDF(os.path.join(pybullet_data.getDataPath(), self.model_urdf), + basePosition=self.basePosition, + baseOrientation=self.baseOrientation, + useFixedBase=self.fixed_base)) + + self.robot_specific_reset(self._p) + + s = self.calc_state( + ) # optimization: calc_state() can calculate something in self.* for calc_potential() to use + self.potential = self.calc_potential() + + return s + + def calc_potential(self): + return 0 class SDFBasedRobot(XmlBasedRobot): - """ + """ Base class for SDF robots in a Scene. """ - def __init__(self, model_sdf, robot_name, action_dim, obs_dim, basePosition=[0, 0, 0], baseOrientation=[0, 0, 0, 1], fixed_base=False, self_collision=False): - XmlBasedRobot.__init__(self, robot_name, action_dim, obs_dim, self_collision) - - self.model_sdf = model_sdf - self.fixed_base = fixed_base + def __init__(self, + model_sdf, + robot_name, + action_dim, + obs_dim, + basePosition=[0, 0, 0], + baseOrientation=[0, 0, 0, 1], + fixed_base=False, + self_collision=False): + XmlBasedRobot.__init__(self, robot_name, action_dim, obs_dim, self_collision) - def reset(self, bullet_client): - self._p = bullet_client + self.model_sdf = model_sdf + self.fixed_base = fixed_base - self.ordered_joints = [] + def reset(self, bullet_client): + self._p = bullet_client - self.parts, self.jdict, self.ordered_joints, self.robot_body = self.addToScene(self._p, # TODO: Not sure if this works, try it with kuka - self._p.loadSDF(os.path.join("models_robot", self.model_sdf))) + self.ordered_joints = [] - self.robot_specific_reset(self._p) + self.parts, self.jdict, self.ordered_joints, self.robot_body = self.addToScene( + self._p, # TODO: Not sure if this works, try it with kuka + self._p.loadSDF(os.path.join("models_robot", self.model_sdf))) - s = self.calc_state() # optimization: calc_state() can calculate something in self.* for calc_potential() to use - self.potential = self.calc_potential() + self.robot_specific_reset(self._p) - return s + s = self.calc_state( + ) # optimization: calc_state() can calculate something in self.* for calc_potential() to use + self.potential = self.calc_potential() - def calc_potential(self): - return 0 + return s + def calc_potential(self): + return 0 -class Pose_Helper: # dummy class to comply to original interface - def __init__(self, body_part): - self.body_part = body_part - def xyz(self): - return self.body_part.current_position() +class Pose_Helper: # dummy class to comply to original interface - def rpy(self): - return pybullet.getEulerFromQuaternion(self.body_part.current_orientation()) - - def orientation(self): - return self.body_part.current_orientation() - -class BodyPart: - def __init__(self, bullet_client, body_name, bodies, bodyIndex, bodyPartIndex): - self.bodies = bodies - self._p = bullet_client - self.bodyIndex = bodyIndex - self.bodyPartIndex = bodyPartIndex - self.initialPosition = self.current_position() - self.initialOrientation = self.current_orientation() - self.bp_pose = Pose_Helper(self) + def __init__(self, body_part): + self.body_part = body_part - def state_fields_of_pose_of(self, body_id, link_id=-1): # a method you will most probably need a lot to get pose and orientation - if link_id == -1: - (x, y, z), (a, b, c, d) = self._p.getBasePositionAndOrientation(body_id) - else: - (x, y, z), (a, b, c, d), _, _, _, _ = self._p.getLinkState(body_id, link_id) - return np.array([x, y, z, a, b, c, d]) + def xyz(self): + return self.body_part.current_position() - def get_position(self): return self.current_position() + def rpy(self): + return pybullet.getEulerFromQuaternion(self.body_part.current_orientation()) - def get_pose(self): - return self.state_fields_of_pose_of(self.bodies[self.bodyIndex], self.bodyPartIndex) + def orientation(self): + return self.body_part.current_orientation() - def speed(self): - if self.bodyPartIndex == -1: - (vx, vy, vz), _ = self._p.getBaseVelocity(self.bodies[self.bodyIndex]) - else: - (x,y,z), (a,b,c,d), _,_,_,_, (vx, vy, vz), (vr,vp,vy) = self._p.getLinkState(self.bodies[self.bodyIndex], self.bodyPartIndex, computeLinkVelocity=1) - return np.array([vx, vy, vz]) - - def current_position(self): - return self.get_pose()[:3] - - def current_orientation(self): - return self.get_pose()[3:] - - def get_orientation(self): - return self.current_orientation() - - def reset_position(self, position): - self._p.resetBasePositionAndOrientation(self.bodies[self.bodyIndex], position, self.get_orientation()) - - def reset_orientation(self, orientation): - self._p.resetBasePositionAndOrientation(self.bodies[self.bodyIndex], self.get_position(), orientation) - - def reset_velocity(self, linearVelocity=[0,0,0], angularVelocity =[0,0,0]): - self._p.resetBaseVelocity(self.bodies[self.bodyIndex], linearVelocity, angularVelocity) - - def reset_pose(self, position, orientation): - self._p.resetBasePositionAndOrientation(self.bodies[self.bodyIndex], position, orientation) - - def pose(self): - return self.bp_pose - - def contact_list(self): - return self._p.getContactPoints(self.bodies[self.bodyIndex], -1, self.bodyPartIndex, -1) +class BodyPart: -class Joint: - def __init__(self, bullet_client, joint_name, bodies, bodyIndex, jointIndex): - self.bodies = bodies - self._p = bullet_client - self.bodyIndex = bodyIndex - self.jointIndex = jointIndex - self.joint_name = joint_name + def __init__(self, bullet_client, body_name, bodies, bodyIndex, bodyPartIndex): + self.bodies = bodies + self._p = bullet_client + self.bodyIndex = bodyIndex + self.bodyPartIndex = bodyPartIndex + self.initialPosition = self.current_position() + self.initialOrientation = self.current_orientation() + self.bp_pose = Pose_Helper(self) - jointInfo = self._p.getJointInfo(self.bodies[self.bodyIndex], self.jointIndex) - self.lowerLimit = jointInfo[8] - self.upperLimit = jointInfo[9] + def state_fields_of_pose_of( + self, body_id, + link_id=-1): # a method you will most probably need a lot to get pose and orientation + if link_id == -1: + (x, y, z), (a, b, c, d) = self._p.getBasePositionAndOrientation(body_id) + else: + (x, y, z), (a, b, c, d), _, _, _, _ = self._p.getLinkState(body_id, link_id) + return np.array([x, y, z, a, b, c, d]) - self.power_coeff = 0 + def get_position(self): + return self.current_position() - def set_state(self, x, vx): - self._p.resetJointState(self.bodies[self.bodyIndex], self.jointIndex, x, vx) + def get_pose(self): + return self.state_fields_of_pose_of(self.bodies[self.bodyIndex], self.bodyPartIndex) - def current_position(self): # just some synonyme method - return self.get_state() + def speed(self): + if self.bodyPartIndex == -1: + (vx, vy, vz), _ = self._p.getBaseVelocity(self.bodies[self.bodyIndex]) + else: + (x, y, z), (a, b, c, d), _, _, _, _, (vx, vy, vz), (vr, vp, vy) = self._p.getLinkState( + self.bodies[self.bodyIndex], self.bodyPartIndex, computeLinkVelocity=1) + return np.array([vx, vy, vz]) - def current_relative_position(self): - pos, vel = self.get_state() - pos_mid = 0.5 * (self.lowerLimit + self.upperLimit); - return ( - 2 * (pos - pos_mid) / (self.upperLimit - self.lowerLimit), - 0.1 * vel - ) + def current_position(self): + return self.get_pose()[:3] - def get_state(self): - x, vx,_,_ = self._p.getJointState(self.bodies[self.bodyIndex],self.jointIndex) - return x, vx + def current_orientation(self): + return self.get_pose()[3:] - def get_position(self): - x, _ = self.get_state() - return x + def get_orientation(self): + return self.current_orientation() - def get_orientation(self): - _,r = self.get_state() - return r + def reset_position(self, position): + self._p.resetBasePositionAndOrientation(self.bodies[self.bodyIndex], position, + self.get_orientation()) - def get_velocity(self): - _, vx = self.get_state() - return vx + def reset_orientation(self, orientation): + self._p.resetBasePositionAndOrientation(self.bodies[self.bodyIndex], self.get_position(), + orientation) - def set_position(self, position): - self._p.setJointMotorControl2(self.bodies[self.bodyIndex],self.jointIndex,pybullet.POSITION_CONTROL, targetPosition=position) + def reset_velocity(self, linearVelocity=[0, 0, 0], angularVelocity=[0, 0, 0]): + self._p.resetBaseVelocity(self.bodies[self.bodyIndex], linearVelocity, angularVelocity) - def set_velocity(self, velocity): - self._p.setJointMotorControl2(self.bodies[self.bodyIndex],self.jointIndex,pybullet.VELOCITY_CONTROL, targetVelocity=velocity) + def reset_pose(self, position, orientation): + self._p.resetBasePositionAndOrientation(self.bodies[self.bodyIndex], position, orientation) - def set_motor_torque(self, torque): # just some synonyme method - self.set_torque(torque) + def pose(self): + return self.bp_pose - def set_torque(self, torque): - self._p.setJointMotorControl2(bodyIndex=self.bodies[self.bodyIndex], jointIndex=self.jointIndex, controlMode=pybullet.TORQUE_CONTROL, force=torque) #, positionGain=0.1, velocityGain=0.1) + def contact_list(self): + return self._p.getContactPoints(self.bodies[self.bodyIndex], -1, self.bodyPartIndex, -1) - def reset_current_position(self, position, velocity): # just some synonyme method - self.reset_position(position, velocity) - def reset_position(self, position, velocity): - self._p.resetJointState(self.bodies[self.bodyIndex],self.jointIndex,targetValue=position, targetVelocity=velocity) - self.disable_motor() +class Joint: - def disable_motor(self): - self._p.setJointMotorControl2(self.bodies[self.bodyIndex],self.jointIndex,controlMode=pybullet.POSITION_CONTROL, targetPosition=0, targetVelocity=0, positionGain=0.1, velocityGain=0.1, force=0) + def __init__(self, bullet_client, joint_name, bodies, bodyIndex, jointIndex): + self.bodies = bodies + self._p = bullet_client + self.bodyIndex = bodyIndex + self.jointIndex = jointIndex + self.joint_name = joint_name + + jointInfo = self._p.getJointInfo(self.bodies[self.bodyIndex], self.jointIndex) + self.lowerLimit = jointInfo[8] + self.upperLimit = jointInfo[9] + + self.power_coeff = 0 + + def set_state(self, x, vx): + self._p.resetJointState(self.bodies[self.bodyIndex], self.jointIndex, x, vx) + + def current_position(self): # just some synonyme method + return self.get_state() + + def current_relative_position(self): + pos, vel = self.get_state() + pos_mid = 0.5 * (self.lowerLimit + self.upperLimit) + return (2 * (pos - pos_mid) / (self.upperLimit - self.lowerLimit), 0.1 * vel) + + def get_state(self): + x, vx, _, _ = self._p.getJointState(self.bodies[self.bodyIndex], self.jointIndex) + return x, vx + + def get_position(self): + x, _ = self.get_state() + return x + + def get_orientation(self): + _, r = self.get_state() + return r + + def get_velocity(self): + _, vx = self.get_state() + return vx + + def set_position(self, position): + self._p.setJointMotorControl2(self.bodies[self.bodyIndex], + self.jointIndex, + pybullet.POSITION_CONTROL, + targetPosition=position) + + def set_velocity(self, velocity): + self._p.setJointMotorControl2(self.bodies[self.bodyIndex], + self.jointIndex, + pybullet.VELOCITY_CONTROL, + targetVelocity=velocity) + + def set_motor_torque(self, torque): # just some synonyme method + self.set_torque(torque) + + def set_torque(self, torque): + self._p.setJointMotorControl2(bodyIndex=self.bodies[self.bodyIndex], + jointIndex=self.jointIndex, + controlMode=pybullet.TORQUE_CONTROL, + force=torque) #, positionGain=0.1, velocityGain=0.1) + + def reset_current_position(self, position, velocity): # just some synonyme method + self.reset_position(position, velocity) + + def reset_position(self, position, velocity): + self._p.resetJointState(self.bodies[self.bodyIndex], + self.jointIndex, + targetValue=position, + targetVelocity=velocity) + self.disable_motor() + + def disable_motor(self): + self._p.setJointMotorControl2(self.bodies[self.bodyIndex], + self.jointIndex, + controlMode=pybullet.POSITION_CONTROL, + targetPosition=0, + targetVelocity=0, + positionGain=0.1, + velocityGain=0.1, + force=0) diff --git a/examples/pybullet/gym/pybullet_envs/robot_locomotors.py b/examples/pybullet/gym/pybullet_envs/robot_locomotors.py index 7ed7a1272..43ff604ac 100644 --- a/examples/pybullet/gym/pybullet_envs/robot_locomotors.py +++ b/examples/pybullet/gym/pybullet_envs/robot_locomotors.py @@ -1,321 +1,344 @@ from robot_bases import XmlBasedRobot, MJCFBasedRobot, URDFBasedRobot import numpy as np -import pybullet +import pybullet import os import pybullet_data from robot_bases import BodyPart + class WalkerBase(MJCFBasedRobot): - def __init__(self, fn, robot_name, action_dim, obs_dim, power): - MJCFBasedRobot.__init__(self, fn, robot_name, action_dim, obs_dim) - self.power = power - self.camera_x = 0 - self.start_pos_x, self.start_pos_y, self.start_pos_z = 0, 0, 0 - self.walk_target_x = 1e3 # kilometer away - self.walk_target_y = 0 - self.body_xyz=[0,0,0] - - def robot_specific_reset(self, bullet_client): - self._p = bullet_client - for j in self.ordered_joints: - j.reset_current_position(self.np_random.uniform(low=-0.1, high=0.1), 0) - - self.feet = [self.parts[f] for f in self.foot_list] - self.feet_contact = np.array([0.0 for f in self.foot_list], dtype=np.float32) - self.scene.actor_introduce(self) - self.initial_z = None - - def apply_action(self, a): - assert (np.isfinite(a).all()) - for n, j in enumerate(self.ordered_joints): - j.set_motor_torque(self.power * j.power_coef * float(np.clip(a[n], -1, +1))) - - def calc_state(self): - j = np.array([j.current_relative_position() for j in self.ordered_joints], dtype=np.float32).flatten() - # even elements [0::2] position, scaled to -1..+1 between limits - # odd elements [1::2] angular speed, scaled to show -1..+1 - self.joint_speeds = j[1::2] - self.joints_at_limit = np.count_nonzero(np.abs(j[0::2]) > 0.99) - - body_pose = self.robot_body.pose() - parts_xyz = np.array([p.pose().xyz() for p in self.parts.values()]).flatten() - self.body_xyz = ( - parts_xyz[0::3].mean(), parts_xyz[1::3].mean(), body_pose.xyz()[2]) # torso z is more informative than mean z - self.body_rpy = body_pose.rpy() - z = self.body_xyz[2] - if self.initial_z == None: - self.initial_z = z - r, p, yaw = self.body_rpy - self.walk_target_theta = np.arctan2(self.walk_target_y - self.body_xyz[1], - self.walk_target_x - self.body_xyz[0]) - self.walk_target_dist = np.linalg.norm( - [self.walk_target_y - self.body_xyz[1], self.walk_target_x - self.body_xyz[0]]) - angle_to_target = self.walk_target_theta - yaw - - rot_speed = np.array( - [[np.cos(-yaw), -np.sin(-yaw), 0], - [np.sin(-yaw), np.cos(-yaw), 0], - [ 0, 0, 1]] - ) - vx, vy, vz = np.dot(rot_speed, self.robot_body.speed()) # rotate speed back to body point of view - - more = np.array([ z-self.initial_z, - np.sin(angle_to_target), np.cos(angle_to_target), - 0.3* vx , 0.3* vy , 0.3* vz , # 0.3 is just scaling typical speed into -1..+1, no physical sense here - r, p], dtype=np.float32) - return np.clip( np.concatenate([more] + [j] + [self.feet_contact]), -5, +5) - - def calc_potential(self): - # progress in potential field is speed*dt, typical speed is about 2-3 meter per second, this potential will change 2-3 per frame (not per second), - # all rewards have rew/frame units and close to 1.0 - debugmode=0 - if (debugmode): - print("calc_potential: self.walk_target_dist") - print(self.walk_target_dist) - print("self.scene.dt") - print(self.scene.dt) - print("self.scene.frame_skip") - print(self.scene.frame_skip) - print("self.scene.timestep") - print(self.scene.timestep) - return - self.walk_target_dist / self.scene.dt + + def __init__(self, fn, robot_name, action_dim, obs_dim, power): + MJCFBasedRobot.__init__(self, fn, robot_name, action_dim, obs_dim) + self.power = power + self.camera_x = 0 + self.start_pos_x, self.start_pos_y, self.start_pos_z = 0, 0, 0 + self.walk_target_x = 1e3 # kilometer away + self.walk_target_y = 0 + self.body_xyz = [0, 0, 0] + + def robot_specific_reset(self, bullet_client): + self._p = bullet_client + for j in self.ordered_joints: + j.reset_current_position(self.np_random.uniform(low=-0.1, high=0.1), 0) + + self.feet = [self.parts[f] for f in self.foot_list] + self.feet_contact = np.array([0.0 for f in self.foot_list], dtype=np.float32) + self.scene.actor_introduce(self) + self.initial_z = None + + def apply_action(self, a): + assert (np.isfinite(a).all()) + for n, j in enumerate(self.ordered_joints): + j.set_motor_torque(self.power * j.power_coef * float(np.clip(a[n], -1, +1))) + + def calc_state(self): + j = np.array([j.current_relative_position() for j in self.ordered_joints], + dtype=np.float32).flatten() + # even elements [0::2] position, scaled to -1..+1 between limits + # odd elements [1::2] angular speed, scaled to show -1..+1 + self.joint_speeds = j[1::2] + self.joints_at_limit = np.count_nonzero(np.abs(j[0::2]) > 0.99) + + body_pose = self.robot_body.pose() + parts_xyz = np.array([p.pose().xyz() for p in self.parts.values()]).flatten() + self.body_xyz = (parts_xyz[0::3].mean(), parts_xyz[1::3].mean(), body_pose.xyz()[2] + ) # torso z is more informative than mean z + self.body_rpy = body_pose.rpy() + z = self.body_xyz[2] + if self.initial_z == None: + self.initial_z = z + r, p, yaw = self.body_rpy + self.walk_target_theta = np.arctan2(self.walk_target_y - self.body_xyz[1], + self.walk_target_x - self.body_xyz[0]) + self.walk_target_dist = np.linalg.norm( + [self.walk_target_y - self.body_xyz[1], self.walk_target_x - self.body_xyz[0]]) + angle_to_target = self.walk_target_theta - yaw + + rot_speed = np.array([[np.cos(-yaw), -np.sin(-yaw), 0], [np.sin(-yaw), + np.cos(-yaw), 0], [0, 0, 1]]) + vx, vy, vz = np.dot(rot_speed, + self.robot_body.speed()) # rotate speed back to body point of view + + more = np.array( + [ + z - self.initial_z, + np.sin(angle_to_target), + np.cos(angle_to_target), + 0.3 * vx, + 0.3 * vy, + 0.3 * vz, # 0.3 is just scaling typical speed into -1..+1, no physical sense here + r, + p + ], + dtype=np.float32) + return np.clip(np.concatenate([more] + [j] + [self.feet_contact]), -5, +5) + + def calc_potential(self): + # progress in potential field is speed*dt, typical speed is about 2-3 meter per second, this potential will change 2-3 per frame (not per second), + # all rewards have rew/frame units and close to 1.0 + debugmode = 0 + if (debugmode): + print("calc_potential: self.walk_target_dist") + print(self.walk_target_dist) + print("self.scene.dt") + print(self.scene.dt) + print("self.scene.frame_skip") + print(self.scene.frame_skip) + print("self.scene.timestep") + print(self.scene.timestep) + return -self.walk_target_dist / self.scene.dt class Hopper(WalkerBase): - foot_list = ["foot"] + foot_list = ["foot"] - def __init__(self): - WalkerBase.__init__(self, "hopper.xml", "torso", action_dim=3, obs_dim=15, power=0.75) + def __init__(self): + WalkerBase.__init__(self, "hopper.xml", "torso", action_dim=3, obs_dim=15, power=0.75) - def alive_bonus(self, z, pitch): - return +1 if z > 0.8 and abs(pitch) < 1.0 else -1 + def alive_bonus(self, z, pitch): + return +1 if z > 0.8 and abs(pitch) < 1.0 else -1 class Walker2D(WalkerBase): - foot_list = ["foot", "foot_left"] + foot_list = ["foot", "foot_left"] - def __init__(self): - WalkerBase.__init__(self, "walker2d.xml", "torso", action_dim=6, obs_dim=22, power=0.40) + def __init__(self): + WalkerBase.__init__(self, "walker2d.xml", "torso", action_dim=6, obs_dim=22, power=0.40) - def alive_bonus(self, z, pitch): - return +1 if z > 0.8 and abs(pitch) < 1.0 else -1 + def alive_bonus(self, z, pitch): + return +1 if z > 0.8 and abs(pitch) < 1.0 else -1 - def robot_specific_reset(self, bullet_client): - WalkerBase.robot_specific_reset(self, bullet_client) - for n in ["foot_joint", "foot_left_joint"]: - self.jdict[n].power_coef = 30.0 + def robot_specific_reset(self, bullet_client): + WalkerBase.robot_specific_reset(self, bullet_client) + for n in ["foot_joint", "foot_left_joint"]: + self.jdict[n].power_coef = 30.0 class HalfCheetah(WalkerBase): - foot_list = ["ffoot", "fshin", "fthigh", "bfoot", "bshin", "bthigh"] # track these contacts with ground + foot_list = ["ffoot", "fshin", "fthigh", "bfoot", "bshin", + "bthigh"] # track these contacts with ground - def __init__(self): - WalkerBase.__init__(self, "half_cheetah.xml", "torso", action_dim=6, obs_dim=26, power=0.90) + def __init__(self): + WalkerBase.__init__(self, "half_cheetah.xml", "torso", action_dim=6, obs_dim=26, power=0.90) - def alive_bonus(self, z, pitch): - # Use contact other than feet to terminate episode: due to a lot of strange walks using knees - return +1 if np.abs(pitch) < 1.0 and not self.feet_contact[1] and not self.feet_contact[2] and not self.feet_contact[4] and not self.feet_contact[5] else -1 + def alive_bonus(self, z, pitch): + # Use contact other than feet to terminate episode: due to a lot of strange walks using knees + return +1 if np.abs(pitch) < 1.0 and not self.feet_contact[1] and not self.feet_contact[ + 2] and not self.feet_contact[4] and not self.feet_contact[5] else -1 - def robot_specific_reset(self, bullet_client): - WalkerBase.robot_specific_reset(self, bullet_client) - self.jdict["bthigh"].power_coef = 120.0 - self.jdict["bshin"].power_coef = 90.0 - self.jdict["bfoot"].power_coef = 60.0 - self.jdict["fthigh"].power_coef = 140.0 - self.jdict["fshin"].power_coef = 60.0 - self.jdict["ffoot"].power_coef = 30.0 + def robot_specific_reset(self, bullet_client): + WalkerBase.robot_specific_reset(self, bullet_client) + self.jdict["bthigh"].power_coef = 120.0 + self.jdict["bshin"].power_coef = 90.0 + self.jdict["bfoot"].power_coef = 60.0 + self.jdict["fthigh"].power_coef = 140.0 + self.jdict["fshin"].power_coef = 60.0 + self.jdict["ffoot"].power_coef = 30.0 class Ant(WalkerBase): - foot_list = ['front_left_foot', 'front_right_foot', 'left_back_foot', 'right_back_foot'] + foot_list = ['front_left_foot', 'front_right_foot', 'left_back_foot', 'right_back_foot'] - def __init__(self): - WalkerBase.__init__(self, "ant.xml", "torso", action_dim=8, obs_dim=28, power=2.5) + def __init__(self): + WalkerBase.__init__(self, "ant.xml", "torso", action_dim=8, obs_dim=28, power=2.5) - def alive_bonus(self, z, pitch): - return +1 if z > 0.26 else -1 # 0.25 is central sphere rad, die if it scrapes the ground + def alive_bonus(self, z, pitch): + return +1 if z > 0.26 else -1 # 0.25 is central sphere rad, die if it scrapes the ground class Humanoid(WalkerBase): - self_collision = True - foot_list = ["right_foot", "left_foot"] # "left_hand", "right_hand" - - def __init__(self): - WalkerBase.__init__(self, 'humanoid_symmetric.xml', 'torso', action_dim=17, obs_dim=44, power=0.41) - # 17 joints, 4 of them important for walking (hip, knee), others may as well be turned off, 17/4 = 4.25 - - def robot_specific_reset(self, bullet_client): - WalkerBase.robot_specific_reset(self, bullet_client) - self.motor_names = ["abdomen_z", "abdomen_y", "abdomen_x"] - self.motor_power = [100, 100, 100] - self.motor_names += ["right_hip_x", "right_hip_z", "right_hip_y", "right_knee"] - self.motor_power += [100, 100, 300, 200] - self.motor_names += ["left_hip_x", "left_hip_z", "left_hip_y", "left_knee"] - self.motor_power += [100, 100, 300, 200] - self.motor_names += ["right_shoulder1", "right_shoulder2", "right_elbow"] - self.motor_power += [75, 75, 75] - self.motor_names += ["left_shoulder1", "left_shoulder2", "left_elbow"] - self.motor_power += [75, 75, 75] - self.motors = [self.jdict[n] for n in self.motor_names] - if self.random_yaw: - position = [0,0,0] - orientation = [0,0,0] - yaw = self.np_random.uniform(low=-3.14, high=3.14) - if self.random_lean and self.np_random.randint(2)==0: - cpose.set_xyz(0, 0, 1.4) - if self.np_random.randint(2)==0: - pitch = np.pi/2 - position = [0, 0, 0.45] - else: - pitch = np.pi*3/2 - position = [0, 0, 0.25] - roll = 0 - orientation = [roll, pitch, yaw] - else: - position = [0, 0, 1.4] - orientation = [0, 0, yaw] # just face random direction, but stay straight otherwise - self.robot_body.reset_position(position) - self.robot_body.reset_orientation(orientation) - self.initial_z = 0.8 - - random_yaw = False - random_lean = False - - def apply_action(self, a): - assert( np.isfinite(a).all() ) - force_gain = 1 - for i, m, power in zip(range(17), self.motors, self.motor_power): - m.set_motor_torque(float(force_gain * power * self.power * np.clip(a[i], -1, +1))) - - def alive_bonus(self, z, pitch): - return +2 if z > 0.78 else -1 # 2 here because 17 joints produce a lot of electricity cost just from policy noise, living must be better than dying - - - - -def get_cube(_p, x, y, z): - body = _p.loadURDF(os.path.join(pybullet_data.getDataPath(),"cube_small.urdf"), [x, y, z]) - _p.changeDynamics(body,-1, mass=1.2)#match Roboschool - part_name, _ = _p.getBodyInfo(body) - part_name = part_name.decode("utf8") - bodies = [body] - return BodyPart(_p, part_name, bodies, 0, -1) + self_collision = True + foot_list = ["right_foot", "left_foot"] # "left_hand", "right_hand" + + def __init__(self): + WalkerBase.__init__(self, + 'humanoid_symmetric.xml', + 'torso', + action_dim=17, + obs_dim=44, + power=0.41) + # 17 joints, 4 of them important for walking (hip, knee), others may as well be turned off, 17/4 = 4.25 + + def robot_specific_reset(self, bullet_client): + WalkerBase.robot_specific_reset(self, bullet_client) + self.motor_names = ["abdomen_z", "abdomen_y", "abdomen_x"] + self.motor_power = [100, 100, 100] + self.motor_names += ["right_hip_x", "right_hip_z", "right_hip_y", "right_knee"] + self.motor_power += [100, 100, 300, 200] + self.motor_names += ["left_hip_x", "left_hip_z", "left_hip_y", "left_knee"] + self.motor_power += [100, 100, 300, 200] + self.motor_names += ["right_shoulder1", "right_shoulder2", "right_elbow"] + self.motor_power += [75, 75, 75] + self.motor_names += ["left_shoulder1", "left_shoulder2", "left_elbow"] + self.motor_power += [75, 75, 75] + self.motors = [self.jdict[n] for n in self.motor_names] + if self.random_yaw: + position = [0, 0, 0] + orientation = [0, 0, 0] + yaw = self.np_random.uniform(low=-3.14, high=3.14) + if self.random_lean and self.np_random.randint(2) == 0: + cpose.set_xyz(0, 0, 1.4) + if self.np_random.randint(2) == 0: + pitch = np.pi / 2 + position = [0, 0, 0.45] + else: + pitch = np.pi * 3 / 2 + position = [0, 0, 0.25] + roll = 0 + orientation = [roll, pitch, yaw] + else: + position = [0, 0, 1.4] + orientation = [0, 0, yaw] # just face random direction, but stay straight otherwise + self.robot_body.reset_position(position) + self.robot_body.reset_orientation(orientation) + self.initial_z = 0.8 + + random_yaw = False + random_lean = False + + def apply_action(self, a): + assert (np.isfinite(a).all()) + force_gain = 1 + for i, m, power in zip(range(17), self.motors, self.motor_power): + m.set_motor_torque(float(force_gain * power * self.power * np.clip(a[i], -1, +1))) + + def alive_bonus(self, z, pitch): + return +2 if z > 0.78 else -1 # 2 here because 17 joints produce a lot of electricity cost just from policy noise, living must be better than dying + + +def get_cube(_p, x, y, z): + body = _p.loadURDF(os.path.join(pybullet_data.getDataPath(), "cube_small.urdf"), [x, y, z]) + _p.changeDynamics(body, -1, mass=1.2) #match Roboschool + part_name, _ = _p.getBodyInfo(body) + part_name = part_name.decode("utf8") + bodies = [body] + return BodyPart(_p, part_name, bodies, 0, -1) def get_sphere(_p, x, y, z): - body = _p.loadURDF(os.path.join(pybullet_data.getDataPath(),"sphere2red_nocol.urdf"), [x, y, z]) - part_name, _ = _p.getBodyInfo(body) - part_name = part_name.decode("utf8") - bodies = [body] - return BodyPart(_p, part_name, bodies, 0, -1) + body = _p.loadURDF(os.path.join(pybullet_data.getDataPath(), "sphere2red_nocol.urdf"), [x, y, z]) + part_name, _ = _p.getBodyInfo(body) + part_name = part_name.decode("utf8") + bodies = [body] + return BodyPart(_p, part_name, bodies, 0, -1) + class HumanoidFlagrun(Humanoid): - def __init__(self): - Humanoid.__init__(self) - self.flag = None - - def robot_specific_reset(self, bullet_client): - Humanoid.robot_specific_reset(self, bullet_client) - self.flag_reposition() - - def flag_reposition(self): - self.walk_target_x = self.np_random.uniform(low=-self.scene.stadium_halflen, high=+self.scene.stadium_halflen) - self.walk_target_y = self.np_random.uniform(low=-self.scene.stadium_halfwidth, high=+self.scene.stadium_halfwidth) - more_compact = 0.5 # set to 1.0 whole football field - self.walk_target_x *= more_compact - self.walk_target_y *= more_compact - - if (self.flag): - #for b in self.flag.bodies: - # print("remove body uid",b) - # p.removeBody(b) - self._p.resetBasePositionAndOrientation(self.flag.bodies[0],[self.walk_target_x, self.walk_target_y, 0.7],[0,0,0,1]) - else: - self.flag = get_sphere(self._p, self.walk_target_x, self.walk_target_y, 0.7) - self.flag_timeout = 600/self.scene.frame_skip #match Roboschool - - def calc_state(self): - self.flag_timeout -= 1 - state = Humanoid.calc_state(self) - if self.walk_target_dist < 1 or self.flag_timeout <= 0: - self.flag_reposition() - state = Humanoid.calc_state(self) # caclulate state again, against new flag pos - self.potential = self.calc_potential() # avoid reward jump - return state + + def __init__(self): + Humanoid.__init__(self) + self.flag = None + + def robot_specific_reset(self, bullet_client): + Humanoid.robot_specific_reset(self, bullet_client) + self.flag_reposition() + + def flag_reposition(self): + self.walk_target_x = self.np_random.uniform(low=-self.scene.stadium_halflen, + high=+self.scene.stadium_halflen) + self.walk_target_y = self.np_random.uniform(low=-self.scene.stadium_halfwidth, + high=+self.scene.stadium_halfwidth) + more_compact = 0.5 # set to 1.0 whole football field + self.walk_target_x *= more_compact + self.walk_target_y *= more_compact + + if (self.flag): + #for b in self.flag.bodies: + # print("remove body uid",b) + # p.removeBody(b) + self._p.resetBasePositionAndOrientation(self.flag.bodies[0], + [self.walk_target_x, self.walk_target_y, 0.7], + [0, 0, 0, 1]) + else: + self.flag = get_sphere(self._p, self.walk_target_x, self.walk_target_y, 0.7) + self.flag_timeout = 600 / self.scene.frame_skip #match Roboschool + + def calc_state(self): + self.flag_timeout -= 1 + state = Humanoid.calc_state(self) + if self.walk_target_dist < 1 or self.flag_timeout <= 0: + self.flag_reposition() + state = Humanoid.calc_state(self) # caclulate state again, against new flag pos + self.potential = self.calc_potential() # avoid reward jump + return state class HumanoidFlagrunHarder(HumanoidFlagrun): - def __init__(self): - HumanoidFlagrun.__init__(self) - self.flag = None - self.aggressive_cube = None - self.frame = 0 - - def robot_specific_reset(self, bullet_client): - - HumanoidFlagrun.robot_specific_reset(self, bullet_client) - - self.frame = 0 - if (self.aggressive_cube): - self._p.resetBasePositionAndOrientation(self.aggressive_cube.bodies[0],[-1.5,0,0.05],[0,0,0,1]) - else: - self.aggressive_cube = get_cube(self._p, -1.5,0,0.05) - self.on_ground_frame_counter = 0 - self.crawl_start_potential = None - self.crawl_ignored_potential = 0.0 - self.initial_z = 0.8 - - def alive_bonus(self, z, pitch): - if self.frame%30==0 and self.frame>100 and self.on_ground_frame_counter==0: - target_xyz = np.array(self.body_xyz) - robot_speed = np.array(self.robot_body.speed()) - angle = self.np_random.uniform(low=-3.14, high=3.14) - from_dist = 4.0 - attack_speed = self.np_random.uniform(low=20.0, high=30.0) # speed 20..30 (* mass in cube.urdf = impulse) - time_to_travel = from_dist / attack_speed - target_xyz += robot_speed*time_to_travel # predict future position at the moment the cube hits the robot - position = [target_xyz[0] + from_dist*np.cos(angle), - target_xyz[1] + from_dist*np.sin(angle), - target_xyz[2] + 1.0] - attack_speed_vector = target_xyz - np.array(position) - attack_speed_vector *= attack_speed / np.linalg.norm(attack_speed_vector) - attack_speed_vector += self.np_random.uniform(low=-1.0, high=+1.0, size=(3,)) - self.aggressive_cube.reset_position(position) - self.aggressive_cube.reset_velocity(linearVelocity=attack_speed_vector) - if z < 0.8: - self.on_ground_frame_counter += 1 - elif self.on_ground_frame_counter > 0: - self.on_ground_frame_counter -= 1 - # End episode if the robot can't get up in 170 frames, to save computation and decorrelate observations. - self.frame += 1 - return self.potential_leak() if self.on_ground_frame_counter<170 else -1 - - def potential_leak(self): - z = self.body_xyz[2] # 0.00 .. 0.8 .. 1.05 normal walk, 1.2 when jumping - z = np.clip(z, 0, 0.8) - return z/0.8 + 1.0 # 1.00 .. 2.0 - - def calc_potential(self): - # We see alive bonus here as a leak from potential field. Value V(s) of a given state equals - # potential, if it is topped up with gamma*potential every frame. Gamma is assumed 0.99. - # - # 2.0 alive bonus if z>0.8, potential is 200, leak gamma=0.99, (1-0.99)*200==2.0 - # 1.0 alive bonus on the ground z==0, potential is 100, leak (1-0.99)*100==1.0 - # - # Why robot whould stand up: to receive 100 points in potential field difference. - flag_running_progress = Humanoid.calc_potential(self) - - # This disables crawl. - if self.body_xyz[2] < 0.8: - if self.crawl_start_potential is None: - self.crawl_start_potential = flag_running_progress - self.crawl_ignored_potential - #print("CRAWL START %+0.1f %+0.1f" % (self.crawl_start_potential, flag_running_progress)) - self.crawl_ignored_potential = flag_running_progress - self.crawl_start_potential - flag_running_progress = self.crawl_start_potential - else: - #print("CRAWL STOP %+0.1f %+0.1f" % (self.crawl_ignored_potential, flag_running_progress)) - flag_running_progress -= self.crawl_ignored_potential - self.crawl_start_potential = None - - return flag_running_progress + self.potential_leak()*100 + def __init__(self): + HumanoidFlagrun.__init__(self) + self.flag = None + self.aggressive_cube = None + self.frame = 0 + + def robot_specific_reset(self, bullet_client): + + HumanoidFlagrun.robot_specific_reset(self, bullet_client) + + self.frame = 0 + if (self.aggressive_cube): + self._p.resetBasePositionAndOrientation(self.aggressive_cube.bodies[0], [-1.5, 0, 0.05], + [0, 0, 0, 1]) + else: + self.aggressive_cube = get_cube(self._p, -1.5, 0, 0.05) + self.on_ground_frame_counter = 0 + self.crawl_start_potential = None + self.crawl_ignored_potential = 0.0 + self.initial_z = 0.8 + + def alive_bonus(self, z, pitch): + if self.frame % 30 == 0 and self.frame > 100 and self.on_ground_frame_counter == 0: + target_xyz = np.array(self.body_xyz) + robot_speed = np.array(self.robot_body.speed()) + angle = self.np_random.uniform(low=-3.14, high=3.14) + from_dist = 4.0 + attack_speed = self.np_random.uniform( + low=20.0, high=30.0) # speed 20..30 (* mass in cube.urdf = impulse) + time_to_travel = from_dist / attack_speed + target_xyz += robot_speed * time_to_travel # predict future position at the moment the cube hits the robot + position = [ + target_xyz[0] + from_dist * np.cos(angle), target_xyz[1] + from_dist * np.sin(angle), + target_xyz[2] + 1.0 + ] + attack_speed_vector = target_xyz - np.array(position) + attack_speed_vector *= attack_speed / np.linalg.norm(attack_speed_vector) + attack_speed_vector += self.np_random.uniform(low=-1.0, high=+1.0, size=(3,)) + self.aggressive_cube.reset_position(position) + self.aggressive_cube.reset_velocity(linearVelocity=attack_speed_vector) + if z < 0.8: + self.on_ground_frame_counter += 1 + elif self.on_ground_frame_counter > 0: + self.on_ground_frame_counter -= 1 + # End episode if the robot can't get up in 170 frames, to save computation and decorrelate observations. + self.frame += 1 + return self.potential_leak() if self.on_ground_frame_counter < 170 else -1 + + def potential_leak(self): + z = self.body_xyz[2] # 0.00 .. 0.8 .. 1.05 normal walk, 1.2 when jumping + z = np.clip(z, 0, 0.8) + return z / 0.8 + 1.0 # 1.00 .. 2.0 + + def calc_potential(self): + # We see alive bonus here as a leak from potential field. Value V(s) of a given state equals + # potential, if it is topped up with gamma*potential every frame. Gamma is assumed 0.99. + # + # 2.0 alive bonus if z>0.8, potential is 200, leak gamma=0.99, (1-0.99)*200==2.0 + # 1.0 alive bonus on the ground z==0, potential is 100, leak (1-0.99)*100==1.0 + # + # Why robot whould stand up: to receive 100 points in potential field difference. + flag_running_progress = Humanoid.calc_potential(self) + + # This disables crawl. + if self.body_xyz[2] < 0.8: + if self.crawl_start_potential is None: + self.crawl_start_potential = flag_running_progress - self.crawl_ignored_potential + #print("CRAWL START %+0.1f %+0.1f" % (self.crawl_start_potential, flag_running_progress)) + self.crawl_ignored_potential = flag_running_progress - self.crawl_start_potential + flag_running_progress = self.crawl_start_potential + else: + #print("CRAWL STOP %+0.1f %+0.1f" % (self.crawl_ignored_potential, flag_running_progress)) + flag_running_progress -= self.crawl_ignored_potential + self.crawl_start_potential = None + + return flag_running_progress + self.potential_leak() * 100 diff --git a/examples/pybullet/gym/pybullet_envs/robot_manipulators.py b/examples/pybullet/gym/pybullet_envs/robot_manipulators.py index 07e41888e..30f2a3805 100644 --- a/examples/pybullet/gym/pybullet_envs/robot_manipulators.py +++ b/examples/pybullet/gym/pybullet_envs/robot_manipulators.py @@ -3,308 +3,317 @@ import numpy as np class Reacher(MJCFBasedRobot): - TARG_LIMIT = 0.27 - - def __init__(self): - MJCFBasedRobot.__init__(self, 'reacher.xml', 'body0', action_dim=2, obs_dim=9) - - def robot_specific_reset(self, bullet_client): - self.jdict["target_x"].reset_current_position( - self.np_random.uniform(low=-self.TARG_LIMIT, high=self.TARG_LIMIT), 0) - self.jdict["target_y"].reset_current_position( - self.np_random.uniform(low=-self.TARG_LIMIT, high=self.TARG_LIMIT), 0) - self.fingertip = self.parts["fingertip"] - self.target = self.parts["target"] - self.central_joint = self.jdict["joint0"] - self.elbow_joint = self.jdict["joint1"] - self.central_joint.reset_current_position(self.np_random.uniform(low=-3.14, high=3.14), 0) - self.elbow_joint.reset_current_position(self.np_random.uniform(low=-3.14, high=3.14), 0) - - def apply_action(self, a): - assert (np.isfinite(a).all()) - self.central_joint.set_motor_torque(0.05 * float(np.clip(a[0], -1, +1))) - self.elbow_joint.set_motor_torque(0.05 * float(np.clip(a[1], -1, +1))) - - def calc_state(self): - theta, self.theta_dot = self.central_joint.current_relative_position() - self.gamma, self.gamma_dot = self.elbow_joint.current_relative_position() - target_x, _ = self.jdict["target_x"].current_position() - target_y, _ = self.jdict["target_y"].current_position() - self.to_target_vec = np.array(self.fingertip.pose().xyz()) - np.array(self.target.pose().xyz()) - return np.array([ - target_x, - target_y, - self.to_target_vec[0], - self.to_target_vec[1], - np.cos(theta), - np.sin(theta), - self.theta_dot, - self.gamma, - self.gamma_dot, - ]) - - def calc_potential(self): - return -100 * np.linalg.norm(self.to_target_vec) + TARG_LIMIT = 0.27 + + def __init__(self): + MJCFBasedRobot.__init__(self, 'reacher.xml', 'body0', action_dim=2, obs_dim=9) + + def robot_specific_reset(self, bullet_client): + self.jdict["target_x"].reset_current_position( + self.np_random.uniform(low=-self.TARG_LIMIT, high=self.TARG_LIMIT), 0) + self.jdict["target_y"].reset_current_position( + self.np_random.uniform(low=-self.TARG_LIMIT, high=self.TARG_LIMIT), 0) + self.fingertip = self.parts["fingertip"] + self.target = self.parts["target"] + self.central_joint = self.jdict["joint0"] + self.elbow_joint = self.jdict["joint1"] + self.central_joint.reset_current_position(self.np_random.uniform(low=-3.14, high=3.14), 0) + self.elbow_joint.reset_current_position(self.np_random.uniform(low=-3.14, high=3.14), 0) + + def apply_action(self, a): + assert (np.isfinite(a).all()) + self.central_joint.set_motor_torque(0.05 * float(np.clip(a[0], -1, +1))) + self.elbow_joint.set_motor_torque(0.05 * float(np.clip(a[1], -1, +1))) + + def calc_state(self): + theta, self.theta_dot = self.central_joint.current_relative_position() + self.gamma, self.gamma_dot = self.elbow_joint.current_relative_position() + target_x, _ = self.jdict["target_x"].current_position() + target_y, _ = self.jdict["target_y"].current_position() + self.to_target_vec = np.array(self.fingertip.pose().xyz()) - np.array(self.target.pose().xyz()) + return np.array([ + target_x, + target_y, + self.to_target_vec[0], + self.to_target_vec[1], + np.cos(theta), + np.sin(theta), + self.theta_dot, + self.gamma, + self.gamma_dot, + ]) + + def calc_potential(self): + return -100 * np.linalg.norm(self.to_target_vec) class Pusher(MJCFBasedRobot): - min_target_placement_radius = 0.5 - max_target_placement_radius = 0.8 - min_object_to_target_distance = 0.1 - max_object_to_target_distance = 0.4 - - def __init__(self): - MJCFBasedRobot.__init__(self, 'pusher.xml', 'body0', action_dim=7, obs_dim=55) - - def robot_specific_reset(self, bullet_client): - # parts - self.fingertip = self.parts["fingertip"] - self.target = self.parts["target"] - self.object = self.parts["object"] - - # joints - self.shoulder_pan_joint = self.jdict["shoulder_pan_joint"] - self.shoulder_lift_joint = self.jdict["shoulder_lift_joint"] - self.upper_arm_roll_joint = self.jdict["upper_arm_roll_joint"] - self.elbow_flex_joint = self.jdict["elbow_flex_joint"] - self.forearm_roll_joint = self.jdict["forearm_roll_joint"] - self.wrist_flex_joint = self.jdict["wrist_flex_joint"] - self.wrist_roll_joint = self.jdict["wrist_roll_joint"] - - self.target_pos = np.concatenate([ - self.np_random.uniform(low=-1, high=1, size=1), - self.np_random.uniform(low=-1, high=1, size=1) - ]) - - # make length of vector between min and max_target_placement_radius - self.target_pos = self.target_pos \ - / np.linalg.norm(self.target_pos) \ - * self.np_random.uniform(low=self.min_target_placement_radius, - high=self.max_target_placement_radius, size=1) - - self.object_pos = np.concatenate([ - self.np_random.uniform(low=-1, high=1, size=1), - self.np_random.uniform(low=-1, high=1, size=1) - ]) - - # make length of vector between min and max_object_to_target_distance - self.object_pos = self.object_pos \ - / np.linalg.norm(self.object_pos - self.target_pos) \ - * self.np_random.uniform(low=self.min_object_to_target_distance, - high=self.max_object_to_target_distance, size=1) - - # set position of objects - self.zero_offset = np.array([0.45, 0.55]) - self.jdict["target_x"].reset_current_position(self.target_pos[0] - self.zero_offset[0], 0) - self.jdict["target_y"].reset_current_position(self.target_pos[1] - self.zero_offset[1], 0) - self.jdict["object_x"].reset_current_position(self.object_pos[0] - self.zero_offset[0], 0) - self.jdict["object_y"].reset_current_position(self.object_pos[1] - self.zero_offset[1], 0) - - # randomize all joints TODO: Will this work or do we have to constrain this resetting in some way? - self.shoulder_pan_joint.reset_current_position(self.np_random.uniform(low=-3.14, high=3.14), 0) - self.shoulder_lift_joint.reset_current_position(self.np_random.uniform(low=-3.14, high=3.14), 0) - self.upper_arm_roll_joint.reset_current_position(self.np_random.uniform(low=-3.14, high=3.14), 0) - self.elbow_flex_joint.reset_current_position(self.np_random.uniform(low=-3.14, high=3.14), 0) - self.forearm_roll_joint.reset_current_position(self.np_random.uniform(low=-3.14, high=3.14), 0) - self.wrist_flex_joint.reset_current_position(self.np_random.uniform(low=-3.14, high=3.14), 0) - self.wrist_roll_joint.reset_current_position(self.np_random.uniform(low=-3.14, high=3.14), 0) - - def apply_action(self, a): - assert (np.isfinite(a).all()) - self.shoulder_pan_joint.set_motor_torque(0.05 * float(np.clip(a[0], -1, +1))) - self.shoulder_lift_joint.set_motor_torque(0.05 * float(np.clip(a[1], -1, +1))) - self.upper_arm_roll_joint.set_motor_torque(0.05 * float(np.clip(a[2], -1, +1))) - self.elbow_flex_joint.set_motor_torque(0.05 * float(np.clip(a[3], -1, +1))) - self.forearm_roll_joint.set_motor_torque(0.05 * float(np.clip(a[4], -1, +1))) - self.wrist_flex_joint.set_motor_torque(0.05 * float(np.clip(a[5], -1, +1))) - self.wrist_roll_joint.set_motor_torque(0.05 * float(np.clip(a[6], -1, +1))) - - def calc_state(self): - self.to_target_vec = self.target_pos - self.object_pos - return np.concatenate([ - np.array([j.current_position() for j in self.ordered_joints]).flatten(), # all positions - np.array([j.current_relative_position() for j in self.ordered_joints]).flatten(), # all speeds - self.to_target_vec, - self.fingertip.pose().xyz(), - self.object.pose().xyz(), - self.target.pose().xyz(), - ]) + min_target_placement_radius = 0.5 + max_target_placement_radius = 0.8 + min_object_to_target_distance = 0.1 + max_object_to_target_distance = 0.4 + + def __init__(self): + MJCFBasedRobot.__init__(self, 'pusher.xml', 'body0', action_dim=7, obs_dim=55) + + def robot_specific_reset(self, bullet_client): + # parts + self.fingertip = self.parts["fingertip"] + self.target = self.parts["target"] + self.object = self.parts["object"] + + # joints + self.shoulder_pan_joint = self.jdict["shoulder_pan_joint"] + self.shoulder_lift_joint = self.jdict["shoulder_lift_joint"] + self.upper_arm_roll_joint = self.jdict["upper_arm_roll_joint"] + self.elbow_flex_joint = self.jdict["elbow_flex_joint"] + self.forearm_roll_joint = self.jdict["forearm_roll_joint"] + self.wrist_flex_joint = self.jdict["wrist_flex_joint"] + self.wrist_roll_joint = self.jdict["wrist_roll_joint"] + + self.target_pos = np.concatenate([ + self.np_random.uniform(low=-1, high=1, size=1), + self.np_random.uniform(low=-1, high=1, size=1) + ]) + + # make length of vector between min and max_target_placement_radius + self.target_pos = self.target_pos \ + / np.linalg.norm(self.target_pos) \ + * self.np_random.uniform(low=self.min_target_placement_radius, + high=self.max_target_placement_radius, size=1) + + self.object_pos = np.concatenate([ + self.np_random.uniform(low=-1, high=1, size=1), + self.np_random.uniform(low=-1, high=1, size=1) + ]) + + # make length of vector between min and max_object_to_target_distance + self.object_pos = self.object_pos \ + / np.linalg.norm(self.object_pos - self.target_pos) \ + * self.np_random.uniform(low=self.min_object_to_target_distance, + high=self.max_object_to_target_distance, size=1) + + # set position of objects + self.zero_offset = np.array([0.45, 0.55]) + self.jdict["target_x"].reset_current_position(self.target_pos[0] - self.zero_offset[0], 0) + self.jdict["target_y"].reset_current_position(self.target_pos[1] - self.zero_offset[1], 0) + self.jdict["object_x"].reset_current_position(self.object_pos[0] - self.zero_offset[0], 0) + self.jdict["object_y"].reset_current_position(self.object_pos[1] - self.zero_offset[1], 0) + + # randomize all joints TODO: Will this work or do we have to constrain this resetting in some way? + self.shoulder_pan_joint.reset_current_position(self.np_random.uniform(low=-3.14, high=3.14), 0) + self.shoulder_lift_joint.reset_current_position(self.np_random.uniform(low=-3.14, high=3.14), + 0) + self.upper_arm_roll_joint.reset_current_position(self.np_random.uniform(low=-3.14, high=3.14), + 0) + self.elbow_flex_joint.reset_current_position(self.np_random.uniform(low=-3.14, high=3.14), 0) + self.forearm_roll_joint.reset_current_position(self.np_random.uniform(low=-3.14, high=3.14), 0) + self.wrist_flex_joint.reset_current_position(self.np_random.uniform(low=-3.14, high=3.14), 0) + self.wrist_roll_joint.reset_current_position(self.np_random.uniform(low=-3.14, high=3.14), 0) + + def apply_action(self, a): + assert (np.isfinite(a).all()) + self.shoulder_pan_joint.set_motor_torque(0.05 * float(np.clip(a[0], -1, +1))) + self.shoulder_lift_joint.set_motor_torque(0.05 * float(np.clip(a[1], -1, +1))) + self.upper_arm_roll_joint.set_motor_torque(0.05 * float(np.clip(a[2], -1, +1))) + self.elbow_flex_joint.set_motor_torque(0.05 * float(np.clip(a[3], -1, +1))) + self.forearm_roll_joint.set_motor_torque(0.05 * float(np.clip(a[4], -1, +1))) + self.wrist_flex_joint.set_motor_torque(0.05 * float(np.clip(a[5], -1, +1))) + self.wrist_roll_joint.set_motor_torque(0.05 * float(np.clip(a[6], -1, +1))) + + def calc_state(self): + self.to_target_vec = self.target_pos - self.object_pos + return np.concatenate([ + np.array([j.current_position() for j in self.ordered_joints]).flatten(), # all positions + np.array([j.current_relative_position() for j in self.ordered_joints + ]).flatten(), # all speeds + self.to_target_vec, + self.fingertip.pose().xyz(), + self.object.pose().xyz(), + self.target.pose().xyz(), + ]) class Striker(MJCFBasedRobot): - min_target_placement_radius = 0.1 - max_target_placement_radius = 0.8 - min_object_placement_radius = 0.1 - max_object_placement_radius = 0.8 - - def __init__(self): - MJCFBasedRobot.__init__(self, 'striker.xml', 'body0', action_dim=7, obs_dim=55) - - def robot_specific_reset(self, bullet_client): - # parts - self.fingertip = self.parts["fingertip"] - self.target = self.parts["target"] - self.object = self.parts["object"] - - # joints - self.shoulder_pan_joint = self.jdict["shoulder_pan_joint"] - self.shoulder_lift_joint = self.jdict["shoulder_lift_joint"] - self.upper_arm_roll_joint = self.jdict["upper_arm_roll_joint"] - self.elbow_flex_joint = self.jdict["elbow_flex_joint"] - self.forearm_roll_joint = self.jdict["forearm_roll_joint"] - self.wrist_flex_joint = self.jdict["wrist_flex_joint"] - self.wrist_roll_joint = self.jdict["wrist_roll_joint"] - - self._min_strike_dist = np.inf - self._striked = False - self._strike_pos = None - - # reset position and speed of manipulator - # TODO: Will this work or do we have to constrain this resetting in some way? - self.shoulder_pan_joint.reset_current_position(self.np_random.uniform(low=-3.14, high=3.14), 0) - self.shoulder_lift_joint.reset_current_position(self.np_random.uniform(low=-3.14, high=3.14), 0) - self.upper_arm_roll_joint.reset_current_position(self.np_random.uniform(low=-3.14, high=3.14), 0) - self.elbow_flex_joint.reset_current_position(self.np_random.uniform(low=-3.14, high=3.14), 0) - self.forearm_roll_joint.reset_current_position(self.np_random.uniform(low=-3.14, high=3.14), 0) - self.wrist_flex_joint.reset_current_position(self.np_random.uniform(low=-3.14, high=3.14), 0) - self.wrist_roll_joint.reset_current_position(self.np_random.uniform(low=-3.14, high=3.14), 0) - - self.zero_offset = np.array([0.45, 0.55, 0]) - self.object_pos = np.concatenate([ - self.np_random.uniform(low=-1, high=1, size=1), - self.np_random.uniform(low=-1, high=1, size=1) - ]) - - # make length of vector between min and max_object_placement_radius - self.object_pos = self.object_pos \ - / np.linalg.norm(self.object_pos) \ - * self.np_random.uniform(low=self.min_object_placement_radius, - high=self.max_object_placement_radius, size=1) - - # reset object position - self.jdict["object_x"].reset_current_position(self.object_pos[0] - self.zero_offset[0], 0) - self.jdict["object_y"].reset_current_position(self.object_pos[1] - self.zero_offset[1], 0) - - self.target_pos = np.concatenate([ - self.np_random.uniform(low=-1, high=1, size=1), - self.np_random.uniform(low=-1, high=1, size=1), - self.np_random.uniform(low=-1, high=1, size=1) - ]) - - # make length of vector between min and max_target_placement_radius - self.target_pos = self.target_pos \ - / np.linalg.norm(self.target_pos) \ - * self.np_random.uniform(low=self.min_target_placement_radius, - high=self.max_target_placement_radius, size=1) - - self.target.reset_pose(self.target_pos - self.zero_offset, np.array([0, 0, 0, 1])) - - def apply_action(self, a): - assert (np.isfinite(a).all()) - self.shoulder_pan_joint.set_motor_torque(0.05 * float(np.clip(a[0], -1, +1))) - self.shoulder_lift_joint.set_motor_torque(0.05 * float(np.clip(a[1], -1, +1))) - self.upper_arm_roll_joint.set_motor_torque(0.05 * float(np.clip(a[2], -1, +1))) - self.elbow_flex_joint.set_motor_torque(0.05 * float(np.clip(a[3], -1, +1))) - self.forearm_roll_joint.set_motor_torque(0.05 * float(np.clip(a[4], -1, +1))) - self.wrist_flex_joint.set_motor_torque(0.05 * float(np.clip(a[5], -1, +1))) - self.wrist_roll_joint.set_motor_torque(0.05 * float(np.clip(a[6], -1, +1))) - - def calc_state(self): - self.to_target_vec = self.target_pos - self.object_pos - return np.concatenate([ - np.array([j.current_position() for j in self.ordered_joints]).flatten(), # all positions - np.array([j.current_relative_position() for j in self.ordered_joints]).flatten(), # all speeds - self.to_target_vec, - self.fingertip.pose().xyz(), - self.object.pose().xyz(), - self.target.pose().xyz(), - ]) + min_target_placement_radius = 0.1 + max_target_placement_radius = 0.8 + min_object_placement_radius = 0.1 + max_object_placement_radius = 0.8 + + def __init__(self): + MJCFBasedRobot.__init__(self, 'striker.xml', 'body0', action_dim=7, obs_dim=55) + + def robot_specific_reset(self, bullet_client): + # parts + self.fingertip = self.parts["fingertip"] + self.target = self.parts["target"] + self.object = self.parts["object"] + + # joints + self.shoulder_pan_joint = self.jdict["shoulder_pan_joint"] + self.shoulder_lift_joint = self.jdict["shoulder_lift_joint"] + self.upper_arm_roll_joint = self.jdict["upper_arm_roll_joint"] + self.elbow_flex_joint = self.jdict["elbow_flex_joint"] + self.forearm_roll_joint = self.jdict["forearm_roll_joint"] + self.wrist_flex_joint = self.jdict["wrist_flex_joint"] + self.wrist_roll_joint = self.jdict["wrist_roll_joint"] + + self._min_strike_dist = np.inf + self._striked = False + self._strike_pos = None + + # reset position and speed of manipulator + # TODO: Will this work or do we have to constrain this resetting in some way? + self.shoulder_pan_joint.reset_current_position(self.np_random.uniform(low=-3.14, high=3.14), 0) + self.shoulder_lift_joint.reset_current_position(self.np_random.uniform(low=-3.14, high=3.14), + 0) + self.upper_arm_roll_joint.reset_current_position(self.np_random.uniform(low=-3.14, high=3.14), + 0) + self.elbow_flex_joint.reset_current_position(self.np_random.uniform(low=-3.14, high=3.14), 0) + self.forearm_roll_joint.reset_current_position(self.np_random.uniform(low=-3.14, high=3.14), 0) + self.wrist_flex_joint.reset_current_position(self.np_random.uniform(low=-3.14, high=3.14), 0) + self.wrist_roll_joint.reset_current_position(self.np_random.uniform(low=-3.14, high=3.14), 0) + + self.zero_offset = np.array([0.45, 0.55, 0]) + self.object_pos = np.concatenate([ + self.np_random.uniform(low=-1, high=1, size=1), + self.np_random.uniform(low=-1, high=1, size=1) + ]) + + # make length of vector between min and max_object_placement_radius + self.object_pos = self.object_pos \ + / np.linalg.norm(self.object_pos) \ + * self.np_random.uniform(low=self.min_object_placement_radius, + high=self.max_object_placement_radius, size=1) + + # reset object position + self.jdict["object_x"].reset_current_position(self.object_pos[0] - self.zero_offset[0], 0) + self.jdict["object_y"].reset_current_position(self.object_pos[1] - self.zero_offset[1], 0) + + self.target_pos = np.concatenate([ + self.np_random.uniform(low=-1, high=1, size=1), + self.np_random.uniform(low=-1, high=1, size=1), + self.np_random.uniform(low=-1, high=1, size=1) + ]) + + # make length of vector between min and max_target_placement_radius + self.target_pos = self.target_pos \ + / np.linalg.norm(self.target_pos) \ + * self.np_random.uniform(low=self.min_target_placement_radius, + high=self.max_target_placement_radius, size=1) + + self.target.reset_pose(self.target_pos - self.zero_offset, np.array([0, 0, 0, 1])) + + def apply_action(self, a): + assert (np.isfinite(a).all()) + self.shoulder_pan_joint.set_motor_torque(0.05 * float(np.clip(a[0], -1, +1))) + self.shoulder_lift_joint.set_motor_torque(0.05 * float(np.clip(a[1], -1, +1))) + self.upper_arm_roll_joint.set_motor_torque(0.05 * float(np.clip(a[2], -1, +1))) + self.elbow_flex_joint.set_motor_torque(0.05 * float(np.clip(a[3], -1, +1))) + self.forearm_roll_joint.set_motor_torque(0.05 * float(np.clip(a[4], -1, +1))) + self.wrist_flex_joint.set_motor_torque(0.05 * float(np.clip(a[5], -1, +1))) + self.wrist_roll_joint.set_motor_torque(0.05 * float(np.clip(a[6], -1, +1))) + + def calc_state(self): + self.to_target_vec = self.target_pos - self.object_pos + return np.concatenate([ + np.array([j.current_position() for j in self.ordered_joints]).flatten(), # all positions + np.array([j.current_relative_position() for j in self.ordered_joints + ]).flatten(), # all speeds + self.to_target_vec, + self.fingertip.pose().xyz(), + self.object.pose().xyz(), + self.target.pose().xyz(), + ]) class Thrower(MJCFBasedRobot): - min_target_placement_radius = 0.1 - max_target_placement_radius = 0.8 - min_object_placement_radius = 0.1 - max_object_placement_radius = 0.8 - - def __init__(self): - MJCFBasedRobot.__init__(self, 'thrower.xml', 'body0', action_dim=7, obs_dim=48) - - def robot_specific_reset(self, bullet_client): - # parts - self.fingertip = self.parts["fingertip"] - self.target = self.parts["target"] - self.object = self.parts["object"] - - # joints - self.shoulder_pan_joint = self.jdict["shoulder_pan_joint"] - self.shoulder_lift_joint = self.jdict["shoulder_lift_joint"] - self.upper_arm_roll_joint = self.jdict["upper_arm_roll_joint"] - self.elbow_flex_joint = self.jdict["elbow_flex_joint"] - self.forearm_roll_joint = self.jdict["forearm_roll_joint"] - self.wrist_flex_joint = self.jdict["wrist_flex_joint"] - self.wrist_roll_joint = self.jdict["wrist_roll_joint"] - - self._object_hit_ground = False - self._object_hit_location = None - - # reset position and speed of manipulator - # TODO: Will this work or do we have to constrain this resetting in some way? - self.shoulder_pan_joint.reset_current_position(self.np_random.uniform(low=-3.14, high=3.14), 0) - self.shoulder_lift_joint.reset_current_position(self.np_random.uniform(low=-3.14, high=3.14), 0) - self.upper_arm_roll_joint.reset_current_position(self.np_random.uniform(low=-3.14, high=3.14), 0) - self.elbow_flex_joint.reset_current_position(self.np_random.uniform(low=-3.14, high=3.14), 0) - self.forearm_roll_joint.reset_current_position(self.np_random.uniform(low=-3.14, high=3.14), 0) - self.wrist_flex_joint.reset_current_position(self.np_random.uniform(low=-3.14, high=3.14), 0) - self.wrist_roll_joint.reset_current_position(self.np_random.uniform(low=-3.14, high=3.14), 0) - - self.zero_offset = np.array([0.45, 0.55, 0]) - self.object_pos = np.concatenate([ - self.np_random.uniform(low=-1, high=1, size=1), - self.np_random.uniform(low=-1, high=1, size=1), - self.np_random.uniform(low=-1, high=1, size=1) - ]) - - # make length of vector between min and max_object_placement_radius - self.object_pos = self.object_pos \ - / np.linalg.norm(self.object_pos) \ - * self.np_random.uniform(low=self.min_object_placement_radius, - high=self.max_object_placement_radius, size=1) - - # reset object position - self.parts["object"].reset_pose(self.object_pos - self.zero_offset, np.array([0, 0, 0, 1])) - - self.target_pos = np.concatenate([ - self.np_random.uniform(low=-1, high=1, size=1), - self.np_random.uniform(low=-1, high=1, size=1), - self.np_random.uniform(low=-1, high=1, size=1) - ]) - - # make length of vector between min and max_target_placement_radius - self.target_pos = self.target_pos \ - / np.linalg.norm(self.target_pos) \ - * self.np_random.uniform(low=self.min_target_placement_radius, - high=self.max_target_placement_radius, size=1) - - self.parts["target"].reset_pose(self.target_pos - self.zero_offset, np.array([0, 0, 0, 1])) - - def apply_action(self, a): - assert (np.isfinite(a).all()) - self.shoulder_pan_joint.set_motor_torque(0.05 * float(np.clip(a[0], -1, +1))) - self.shoulder_lift_joint.set_motor_torque(0.05 * float(np.clip(a[1], -1, +1))) - self.upper_arm_roll_joint.set_motor_torque(0.05 * float(np.clip(a[2], -1, +1))) - self.elbow_flex_joint.set_motor_torque(0.05 * float(np.clip(a[3], -1, +1))) - self.forearm_roll_joint.set_motor_torque(0.05 * float(np.clip(a[4], -1, +1))) - self.wrist_flex_joint.set_motor_torque(0.05 * float(np.clip(a[5], -1, +1))) - self.wrist_roll_joint.set_motor_torque(0.05 * float(np.clip(a[6], -1, +1))) - - def calc_state(self): - self.to_target_vec = self.target_pos - self.object_pos - return np.concatenate([ - np.array([j.current_position() for j in self.ordered_joints]).flatten(), # all positions - np.array([j.current_relative_position() for j in self.ordered_joints]).flatten(), # all speeds - self.to_target_vec, - self.fingertip.pose().xyz(), - self.object.pose().xyz(), - self.target.pose().xyz(), - ]) + min_target_placement_radius = 0.1 + max_target_placement_radius = 0.8 + min_object_placement_radius = 0.1 + max_object_placement_radius = 0.8 + + def __init__(self): + MJCFBasedRobot.__init__(self, 'thrower.xml', 'body0', action_dim=7, obs_dim=48) + + def robot_specific_reset(self, bullet_client): + # parts + self.fingertip = self.parts["fingertip"] + self.target = self.parts["target"] + self.object = self.parts["object"] + + # joints + self.shoulder_pan_joint = self.jdict["shoulder_pan_joint"] + self.shoulder_lift_joint = self.jdict["shoulder_lift_joint"] + self.upper_arm_roll_joint = self.jdict["upper_arm_roll_joint"] + self.elbow_flex_joint = self.jdict["elbow_flex_joint"] + self.forearm_roll_joint = self.jdict["forearm_roll_joint"] + self.wrist_flex_joint = self.jdict["wrist_flex_joint"] + self.wrist_roll_joint = self.jdict["wrist_roll_joint"] + + self._object_hit_ground = False + self._object_hit_location = None + + # reset position and speed of manipulator + # TODO: Will this work or do we have to constrain this resetting in some way? + self.shoulder_pan_joint.reset_current_position(self.np_random.uniform(low=-3.14, high=3.14), 0) + self.shoulder_lift_joint.reset_current_position(self.np_random.uniform(low=-3.14, high=3.14), + 0) + self.upper_arm_roll_joint.reset_current_position(self.np_random.uniform(low=-3.14, high=3.14), + 0) + self.elbow_flex_joint.reset_current_position(self.np_random.uniform(low=-3.14, high=3.14), 0) + self.forearm_roll_joint.reset_current_position(self.np_random.uniform(low=-3.14, high=3.14), 0) + self.wrist_flex_joint.reset_current_position(self.np_random.uniform(low=-3.14, high=3.14), 0) + self.wrist_roll_joint.reset_current_position(self.np_random.uniform(low=-3.14, high=3.14), 0) + + self.zero_offset = np.array([0.45, 0.55, 0]) + self.object_pos = np.concatenate([ + self.np_random.uniform(low=-1, high=1, size=1), + self.np_random.uniform(low=-1, high=1, size=1), + self.np_random.uniform(low=-1, high=1, size=1) + ]) + + # make length of vector between min and max_object_placement_radius + self.object_pos = self.object_pos \ + / np.linalg.norm(self.object_pos) \ + * self.np_random.uniform(low=self.min_object_placement_radius, + high=self.max_object_placement_radius, size=1) + + # reset object position + self.parts["object"].reset_pose(self.object_pos - self.zero_offset, np.array([0, 0, 0, 1])) + + self.target_pos = np.concatenate([ + self.np_random.uniform(low=-1, high=1, size=1), + self.np_random.uniform(low=-1, high=1, size=1), + self.np_random.uniform(low=-1, high=1, size=1) + ]) + + # make length of vector between min and max_target_placement_radius + self.target_pos = self.target_pos \ + / np.linalg.norm(self.target_pos) \ + * self.np_random.uniform(low=self.min_target_placement_radius, + high=self.max_target_placement_radius, size=1) + + self.parts["target"].reset_pose(self.target_pos - self.zero_offset, np.array([0, 0, 0, 1])) + + def apply_action(self, a): + assert (np.isfinite(a).all()) + self.shoulder_pan_joint.set_motor_torque(0.05 * float(np.clip(a[0], -1, +1))) + self.shoulder_lift_joint.set_motor_torque(0.05 * float(np.clip(a[1], -1, +1))) + self.upper_arm_roll_joint.set_motor_torque(0.05 * float(np.clip(a[2], -1, +1))) + self.elbow_flex_joint.set_motor_torque(0.05 * float(np.clip(a[3], -1, +1))) + self.forearm_roll_joint.set_motor_torque(0.05 * float(np.clip(a[4], -1, +1))) + self.wrist_flex_joint.set_motor_torque(0.05 * float(np.clip(a[5], -1, +1))) + self.wrist_roll_joint.set_motor_torque(0.05 * float(np.clip(a[6], -1, +1))) + + def calc_state(self): + self.to_target_vec = self.target_pos - self.object_pos + return np.concatenate([ + np.array([j.current_position() for j in self.ordered_joints]).flatten(), # all positions + np.array([j.current_relative_position() for j in self.ordered_joints + ]).flatten(), # all speeds + self.to_target_vec, + self.fingertip.pose().xyz(), + self.object.pose().xyz(), + self.target.pose().xyz(), + ]) diff --git a/examples/pybullet/gym/pybullet_envs/robot_pendula.py b/examples/pybullet/gym/pybullet_envs/robot_pendula.py index a90728128..924d84292 100644 --- a/examples/pybullet/gym/pybullet_envs/robot_pendula.py +++ b/examples/pybullet/gym/pybullet_envs/robot_pendula.py @@ -1,86 +1,92 @@ from robot_bases import MJCFBasedRobot import numpy as np + class InvertedPendulum(MJCFBasedRobot): - swingup = False - def __init__(self): - MJCFBasedRobot.__init__(self, 'inverted_pendulum.xml', 'cart', action_dim=1, obs_dim=5) - - def robot_specific_reset(self, bullet_client): - self._p = bullet_client - self.pole = self.parts["pole"] - self.slider = self.jdict["slider"] - self.j1 = self.jdict["hinge"] - u = self.np_random.uniform(low=-.1, high=.1) - self.j1.reset_current_position( u if not self.swingup else 3.1415+u , 0) - self.j1.set_motor_torque(0) - - def apply_action(self, a): - assert( np.isfinite(a).all() ) - if not np.isfinite(a).all(): - print("a is inf") - a[0] = 0 - self.slider.set_motor_torque( 100*float(np.clip(a[0], -1, +1)) ) - - def calc_state(self): - self.theta, theta_dot = self.j1.current_position() - x, vx = self.slider.current_position() - assert( np.isfinite(x) ) - - if not np.isfinite(x): - print("x is inf") - x = 0 - - if not np.isfinite(vx): - print("vx is inf") - vx = 0 - - if not np.isfinite(self.theta): - print("theta is inf") - self.theta = 0 - - if not np.isfinite(theta_dot): - print("theta_dot is inf") - theta_dot = 0 - - return np.array([ - x, vx, - np.cos(self.theta), np.sin(self.theta), theta_dot - ]) + swingup = False + + def __init__(self): + MJCFBasedRobot.__init__(self, 'inverted_pendulum.xml', 'cart', action_dim=1, obs_dim=5) + + def robot_specific_reset(self, bullet_client): + self._p = bullet_client + self.pole = self.parts["pole"] + self.slider = self.jdict["slider"] + self.j1 = self.jdict["hinge"] + u = self.np_random.uniform(low=-.1, high=.1) + self.j1.reset_current_position(u if not self.swingup else 3.1415 + u, 0) + self.j1.set_motor_torque(0) + + def apply_action(self, a): + assert (np.isfinite(a).all()) + if not np.isfinite(a).all(): + print("a is inf") + a[0] = 0 + self.slider.set_motor_torque(100 * float(np.clip(a[0], -1, +1))) + + def calc_state(self): + self.theta, theta_dot = self.j1.current_position() + x, vx = self.slider.current_position() + assert (np.isfinite(x)) + + if not np.isfinite(x): + print("x is inf") + x = 0 + + if not np.isfinite(vx): + print("vx is inf") + vx = 0 + + if not np.isfinite(self.theta): + print("theta is inf") + self.theta = 0 + + if not np.isfinite(theta_dot): + print("theta_dot is inf") + theta_dot = 0 + + return np.array([x, vx, np.cos(self.theta), np.sin(self.theta), theta_dot]) + class InvertedPendulumSwingup(InvertedPendulum): - swingup = True + swingup = True class InvertedDoublePendulum(MJCFBasedRobot): - def __init__(self): - MJCFBasedRobot.__init__(self, 'inverted_double_pendulum.xml', 'cart', action_dim=1, obs_dim=9) - - def robot_specific_reset(self, bullet_client): - self._p = bullet_client - self.pole2 = self.parts["pole2"] - self.slider = self.jdict["slider"] - self.j1 = self.jdict["hinge"] - self.j2 = self.jdict["hinge2"] - u = self.np_random.uniform(low=-.1, high=.1, size=[2]) - self.j1.reset_current_position(float(u[0]), 0) - self.j2.reset_current_position(float(u[1]), 0) - self.j1.set_motor_torque(0) - self.j2.set_motor_torque(0) - - def apply_action(self, a): - assert( np.isfinite(a).all() ) - self.slider.set_motor_torque( 200*float(np.clip(a[0], -1, +1)) ) - - def calc_state(self): - theta, theta_dot = self.j1.current_position() - gamma, gamma_dot = self.j2.current_position() - x, vx = self.slider.current_position() - self.pos_x, _, self.pos_y = self.pole2.pose().xyz() - assert( np.isfinite(x) ) - return np.array([ - x, vx, - self.pos_x, - np.cos(theta), np.sin(theta), theta_dot, - np.cos(gamma), np.sin(gamma), gamma_dot, - ]) + + def __init__(self): + MJCFBasedRobot.__init__(self, 'inverted_double_pendulum.xml', 'cart', action_dim=1, obs_dim=9) + + def robot_specific_reset(self, bullet_client): + self._p = bullet_client + self.pole2 = self.parts["pole2"] + self.slider = self.jdict["slider"] + self.j1 = self.jdict["hinge"] + self.j2 = self.jdict["hinge2"] + u = self.np_random.uniform(low=-.1, high=.1, size=[2]) + self.j1.reset_current_position(float(u[0]), 0) + self.j2.reset_current_position(float(u[1]), 0) + self.j1.set_motor_torque(0) + self.j2.set_motor_torque(0) + + def apply_action(self, a): + assert (np.isfinite(a).all()) + self.slider.set_motor_torque(200 * float(np.clip(a[0], -1, +1))) + + def calc_state(self): + theta, theta_dot = self.j1.current_position() + gamma, gamma_dot = self.j2.current_position() + x, vx = self.slider.current_position() + self.pos_x, _, self.pos_y = self.pole2.pose().xyz() + assert (np.isfinite(x)) + return np.array([ + x, + vx, + self.pos_x, + np.cos(theta), + np.sin(theta), + theta_dot, + np.cos(gamma), + np.sin(gamma), + gamma_dot, + ]) diff --git a/examples/pybullet/gym/pybullet_envs/scene_abstract.py b/examples/pybullet/gym/pybullet_envs/scene_abstract.py index b50dd524b..f4bbfae0c 100644 --- a/examples/pybullet/gym/pybullet_envs/scene_abstract.py +++ b/examples/pybullet/gym/pybullet_envs/scene_abstract.py @@ -1,78 +1,79 @@ import sys, os sys.path.append(os.path.dirname(__file__)) -import pybullet +import pybullet import gym class Scene: - "A base class for single- and multiplayer scenes" + "A base class for single- and multiplayer scenes" - def __init__(self, bullet_client, gravity, timestep, frame_skip): - self._p = bullet_client - self.np_random, seed = gym.utils.seeding.np_random(None) - self.timestep = timestep - self.frame_skip = frame_skip + def __init__(self, bullet_client, gravity, timestep, frame_skip): + self._p = bullet_client + self.np_random, seed = gym.utils.seeding.np_random(None) + self.timestep = timestep + self.frame_skip = frame_skip - self.dt = self.timestep * self.frame_skip - self.cpp_world = World(self._p, gravity, timestep, frame_skip) + self.dt = self.timestep * self.frame_skip + self.cpp_world = World(self._p, gravity, timestep, frame_skip) - self.test_window_still_open = True # or never opened - self.human_render_detected = False # if user wants render("human"), we open test window + self.test_window_still_open = True # or never opened + self.human_render_detected = False # if user wants render("human"), we open test window - self.multiplayer_robots = {} + self.multiplayer_robots = {} - def test_window(self): - "Call this function every frame, to see what's going on. Not necessary in learning." - self.human_render_detected = True - return self.test_window_still_open + def test_window(self): + "Call this function every frame, to see what's going on. Not necessary in learning." + self.human_render_detected = True + return self.test_window_still_open - def actor_introduce(self, robot): - "Usually after scene reset" - if not self.multiplayer: return - self.multiplayer_robots[robot.player_n] = robot + def actor_introduce(self, robot): + "Usually after scene reset" + if not self.multiplayer: return + self.multiplayer_robots[robot.player_n] = robot - def actor_is_active(self, robot): - """ + def actor_is_active(self, robot): + """ Used by robots to see if they are free to exclusiveley put their HUD on the test window. Later can be used for click-focus robots. """ - return not self.multiplayer + return not self.multiplayer - def episode_restart(self, bullet_client): - "This function gets overridden by specific scene, to reset specific objects into their start positions" - self.cpp_world.clean_everything() - #self.cpp_world.test_window_history_reset() + def episode_restart(self, bullet_client): + "This function gets overridden by specific scene, to reset specific objects into their start positions" + self.cpp_world.clean_everything() + #self.cpp_world.test_window_history_reset() - def global_step(self): - """ + def global_step(self): + """ The idea is: apply motor torques for all robots, then call global_step(), then collect observations from robots using step() with the same action. """ - self.cpp_world.step(self.frame_skip) + self.cpp_world.step(self.frame_skip) -class SingleRobotEmptyScene(Scene): - multiplayer = False # this class is used "as is" for InvertedPendulum, Reacher -class World: +class SingleRobotEmptyScene(Scene): + multiplayer = False # this class is used "as is" for InvertedPendulum, Reacher - def __init__(self, bullet_client, gravity, timestep, frame_skip): - self._p = bullet_client - self.gravity = gravity - self.timestep = timestep - self.frame_skip = frame_skip - self.numSolverIterations = 5 - self.clean_everything() - - - def clean_everything(self): - #p.resetSimulation() - self._p.setGravity(0, 0, -self.gravity) - self._p.setDefaultContactERP(0.9) - #print("self.numSolverIterations=",self.numSolverIterations) - self._p.setPhysicsEngineParameter(fixedTimeStep=self.timestep*self.frame_skip, numSolverIterations=self.numSolverIterations, numSubSteps=self.frame_skip) - - def step(self, frame_skip): - self._p.stepSimulation() +class World: + def __init__(self, bullet_client, gravity, timestep, frame_skip): + self._p = bullet_client + self.gravity = gravity + self.timestep = timestep + self.frame_skip = frame_skip + self.numSolverIterations = 5 + self.clean_everything() + + def clean_everything(self): + #p.resetSimulation() + self._p.setGravity(0, 0, -self.gravity) + self._p.setDefaultContactERP(0.9) + #print("self.numSolverIterations=",self.numSolverIterations) + self._p.setPhysicsEngineParameter(fixedTimeStep=self.timestep * self.frame_skip, + numSolverIterations=self.numSolverIterations, + numSubSteps=self.frame_skip) + + def step(self, frame_skip): + self._p.stepSimulation() diff --git a/examples/pybullet/gym/pybullet_envs/scene_stadium.py b/examples/pybullet/gym/pybullet_envs/scene_stadium.py index c105983db..0ab34f202 100644 --- a/examples/pybullet/gym/pybullet_envs/scene_stadium.py +++ b/examples/pybullet/gym/pybullet_envs/scene_stadium.py @@ -1,52 +1,54 @@ -import os, inspect +import os, inspect currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe()))) parentdir = os.path.dirname(currentdir) -os.sys.path.insert(0,parentdir) +os.sys.path.insert(0, parentdir) import pybullet_data - from pybullet_envs.scene_abstract import Scene -import pybullet +import pybullet class StadiumScene(Scene): - zero_at_running_strip_start_line = True # if False, center of coordinates (0,0,0) will be at the middle of the stadium - stadium_halflen = 105*0.25 # FOOBALL_FIELD_HALFLEN - stadium_halfwidth = 50*0.25 # FOOBALL_FIELD_HALFWID - stadiumLoaded=0 - - def episode_restart(self, bullet_client): - self._p = bullet_client - Scene.episode_restart(self, bullet_client) # contains cpp_world.clean_everything() - if (self.stadiumLoaded==0): - self.stadiumLoaded=1 - - # stadium_pose = cpp_household.Pose() - # if self.zero_at_running_strip_start_line: - # stadium_pose.set_xyz(27, 21, 0) # see RUN_STARTLINE, RUN_RAD constants - - filename = os.path.join(pybullet_data.getDataPath(),"plane_stadium.sdf") - self.ground_plane_mjcf=self._p.loadSDF(filename) - #filename = os.path.join(pybullet_data.getDataPath(),"stadium_no_collision.sdf") - #self.ground_plane_mjcf = self._p.loadSDF(filename) - # - for i in self.ground_plane_mjcf: - self._p.changeDynamics(i,-1,lateralFriction=0.8, restitution=0.5) - self._p.changeVisualShape(i,-1,rgbaColor=[1,1,1,0.8]) - self._p.configureDebugVisualizer(pybullet.COV_ENABLE_PLANAR_REFLECTION,1) - - # for j in range(p.getNumJoints(i)): - # self._p.changeDynamics(i,j,lateralFriction=0) - #despite the name (stadium_no_collision), it DID have collision, so don't add duplicate ground - + zero_at_running_strip_start_line = True # if False, center of coordinates (0,0,0) will be at the middle of the stadium + stadium_halflen = 105 * 0.25 # FOOBALL_FIELD_HALFLEN + stadium_halfwidth = 50 * 0.25 # FOOBALL_FIELD_HALFWID + stadiumLoaded = 0 + + def episode_restart(self, bullet_client): + self._p = bullet_client + Scene.episode_restart(self, bullet_client) # contains cpp_world.clean_everything() + if (self.stadiumLoaded == 0): + self.stadiumLoaded = 1 + + # stadium_pose = cpp_household.Pose() + # if self.zero_at_running_strip_start_line: + # stadium_pose.set_xyz(27, 21, 0) # see RUN_STARTLINE, RUN_RAD constants + + filename = os.path.join(pybullet_data.getDataPath(), "plane_stadium.sdf") + self.ground_plane_mjcf = self._p.loadSDF(filename) + #filename = os.path.join(pybullet_data.getDataPath(),"stadium_no_collision.sdf") + #self.ground_plane_mjcf = self._p.loadSDF(filename) + # + for i in self.ground_plane_mjcf: + self._p.changeDynamics(i, -1, lateralFriction=0.8, restitution=0.5) + self._p.changeVisualShape(i, -1, rgbaColor=[1, 1, 1, 0.8]) + self._p.configureDebugVisualizer(pybullet.COV_ENABLE_PLANAR_REFLECTION, 1) + + # for j in range(p.getNumJoints(i)): + # self._p.changeDynamics(i,j,lateralFriction=0) + #despite the name (stadium_no_collision), it DID have collision, so don't add duplicate ground + + class SinglePlayerStadiumScene(StadiumScene): - "This scene created by environment, to work in a way as if there was no concept of scene visible to user." - multiplayer = False + "This scene created by environment, to work in a way as if there was no concept of scene visible to user." + multiplayer = False + class MultiplayerStadiumScene(StadiumScene): - multiplayer = True - players_count = 3 - def actor_introduce(self, robot): - StadiumScene.actor_introduce(self, robot) - i = robot.player_n - 1 # 0 1 2 => -1 0 +1 - robot.move_robot(0, i, 0) + multiplayer = True + players_count = 3 + + def actor_introduce(self, robot): + StadiumScene.actor_introduce(self, robot) + i = robot.player_n - 1 # 0 1 2 => -1 0 +1 + robot.move_robot(0, i, 0) |