path: root/examples/pybullet/gym/pybullet_envs/examples/enjoy_TF_HumanoidBulletEnv_v0_2017may.py

#add parent dir to find package. Only needed for source code build, pip install doesn't need it.
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)

import gym
import numpy as np
import pybullet_envs
import time

def relu(x):
    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])

    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()
    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.)

            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([
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weights_dense2_w = np.array([
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weights_dense2_b = np.array([ +0.1576, -0.1010, +0.0138, +0.3265, +0.2288, +0.0764, +0.0671, -0.1334, +0.1474, +0.1138, +0.0768, +0.1304, +0.2356, +0.0043, +0.0230, +0.1405, -0.0490, +0.2157, +0.1432, +0.1021, +0.0087, +0.1419, +0.0887, +0.1315, +0.1993, +0.1391, +0.2385, +0.0697, +0.1521, +0.1446, +0.1235, +0.2313, +0.1099, +0.1952, +0.1449, +0.2652, +0.0128, +0.2281, +0.1987, +0.1900, +0.0668, +0.1516, +0.0543, +0.0443, +0.1702, -0.0012, +0.1350, -0.0182, +0.1091, +0.1445, +0.1419, -0.0253, -0.0059, +0.0150, +0.1739, +0.0979, +0.1677, +0.0557, +0.1365, +0.1161, +0.2681, +0.1524, +0.1655, +0.1234, +0.1366, +0.2115, +0.1751, +0.0743, +0.1009, +0.3255, +0.0978, +0.3483, +0.3591, -0.0855, +0.2378, -0.0121, +0.2396, +0.0827, +0.1282, +0.2056, +0.2524, +0.1829, +0.0006, +0.0432, +0.1458, +0.1960, +0.2966, +0.1491, +0.0097, +0.0552, +0.1618, +0.0341, +0.1177, +0.0545, +0.2253, +0.1174, -0.0110, +0.0619, +0.0641, +0.1169, +0.1377, -0.0361, +0.3361, +0.0248, -0.0144, +0.2120, +0.2515, +0.1977, +0.1501, +0.1138, +0.1061, +0.1561, -0.0356, +0.1378, +0.1289, +0.0579, +0.1030, +0.1400, +0.1058, +0.1320, -0.0224, +0.0156, +0.0664, -0.0583, +0.1267, +0.3404, +0.2669, +0.0034])

weights_final_w = np.array([
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])

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])

if __name__=="__main__":
    main()