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import numpy as np
import pybullet_envs.deep_mimic.learning.agent_builder as AgentBuilder
import pybullet_envs.deep_mimic.learning.tf_util as TFUtil
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
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