def __init__(self, env_name, policy_cls, actor_id, batch_size, logdir):
env = create_env(env_name)
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_name, config):
Algorithm.__init__(self, env_name, config)
self.env = create_env(env_name)
self.policy = LSTMPolicy(self.env.observation_space.shape,
self.env.action_space.n, 0)
self.agents = [
Runner.remote(env_name, i) for i in range(config["num_workers"])
]
self.parameters = self.policy.get_weights()
self.iteration = 0
def __init__(self, env_name, actor_id, logdir="/tmp/ray/a3c/", start=True):
env = create_env(env_name)
self.id = actor_id
num_actions = env.action_space.n
self.policy = LSTMPolicy(env.observation_space.shape, num_actions,
actor_id)
self.runner = RunnerThread(env, self.policy, 20)
self.env = env
self.logdir = logdir
if start:
self.start()
def __init__(self, env_name, config, upload_dir=None):
config.update({"alg": "A3C"})
Algorithm.__init__(self, env_name, config, upload_dir=upload_dir)
self.env = create_env(env_name)
self.policy = LSTMPolicy(self.env.observation_space.shape,
self.env.action_space.n, 0)
self.agents = [
Runner.remote(env_name, i, self.logdir)
for i in range(config["num_workers"])
]
self.parameters = self.policy.get_weights()
self.iteration = 0
def __init__(self,
env_name,
config,
policy_cls=SharedModelLSTM,
upload_dir=None):
config.update({"alg": "A3C"})
Agent.__init__(self, env_name, config, upload_dir=upload_dir)
self.env = create_env(env_name)
self.policy = policy_cls(self.env.observation_space.shape,
self.env.action_space)
self.agents = [
Runner.remote(env_name, policy_cls, i, config["batch_size"],
self.logdir) for i in range(config["num_workers"])
]
self.parameters = self.policy.get_weights()