def __init__(self, env_creator, policy_cls, actor_id, batch_size,
preprocess_config, logdir):
env = create_and_wrap(env_creator, preprocess_config)
self.id = actor_id
# TODO(rliaw): should change this to be just env.observation_space
self.policy = policy_cls(env.observation_space.shape, env.action_space)
self.runner = RunnerThread(env, self.policy, batch_size)
self.env = env
self.logdir = logdir
self.start()
def __init__(self, env_creator, config, logdir):
self.env = env = create_and_wrap(env_creator, config["model"])
policy_cls = get_policy_cls(config)
# TODO(rliaw): should change this to be just env.observation_space
self.policy = policy_cls(env.observation_space.shape, env.action_space)
obs_filter = get_filter(config["observation_filter"],
env.observation_space.shape)
self.rew_filter = get_filter(config["reward_filter"], ())
self.sampler = AsyncSampler(env, self.policy, config["batch_size"],
obs_filter)
self.logdir = logdir
def _init(self):
self.env = create_and_wrap(self.env_creator, self.config["model"])
policy_cls = get_policy_cls(self.config)
self.policy = policy_cls(self.env.observation_space.shape,
self.env.action_space)
self.obs_filter = get_filter(self.config["observation_filter"],
self.env.observation_space.shape)
self.rew_filter = get_filter(self.config["reward_filter"], ())
self.agents = [
RemoteRunner.remote(self.env_creator, self.config, self.logdir)
for i in range(self.config["num_workers"])
]
self.parameters = self.policy.get_weights()
def _init(self):
self.env = create_and_wrap(self.env_creator, self.config["model"])
if self.config["use_lstm"]:
policy_cls = SharedModelLSTM
else:
policy_cls = SharedModel
self.policy = policy_cls(
self.env.observation_space.shape, self.env.action_space)
self.agents = [
RemoteRunner.remote(self.env_creator, policy_cls, i,
self.config["batch_size"],
self.config["model"], self.logdir)
for i in range(self.config["num_workers"])]
self.parameters = self.policy.get_weights()
