def _init(self):
policy_params = {"action_noise_std": 0.01}
env = self.env_creator(self.config["env_config"])
preprocessor = ModelCatalog.get_preprocessor(self.registry, env)
self.sess = utils.make_session(single_threaded=False)
self.policy = policies.GenericPolicy(self.registry, self.sess,
env.action_space, preprocessor,
self.config["observation_filter"],
**policy_params)
self.optimizer = optimizers.Adam(self.policy, self.config["stepsize"])
# Create the shared noise table.
print("Creating shared noise table.")
noise_id = create_shared_noise.remote(self.config["noise_size"])
self.noise = SharedNoiseTable(ray.get(noise_id))
# Create the actors.
print("Creating actors.")
self.workers = [
Worker.remote(self.registry, self.config, policy_params,
self.env_creator, noise_id)
for _ in range(self.config["num_workers"])
]
self.episodes_so_far = 0
self.timesteps_so_far = 0
self.tstart = time.time()
def _init(self):
policy_params = {"ac_noise_std": 0.01}
env = self.env_creator()
preprocessor = ModelCatalog.get_preprocessor(
env.spec.id, env.observation_space.shape)
preprocessor_shape = preprocessor.transform_shape(
env.observation_space.shape)
self.sess = utils.make_session(single_threaded=False)
self.policy = policies.GenericPolicy(env.observation_space,
env.action_space, preprocessor,
**policy_params)
tf_util.initialize()
self.optimizer = optimizers.Adam(self.policy, self.config["stepsize"])
self.ob_stat = utils.RunningStat(preprocessor_shape, eps=1e-2)
# Create the shared noise table.
print("Creating shared noise table.")
noise_id = create_shared_noise.remote()
self.noise = SharedNoiseTable(ray.get(noise_id))
# Create the actors.
print("Creating actors.")
self.workers = [
Worker.remote(self.config, policy_params, self.env_creator,
noise_id) for _ in range(self.config["num_workers"])
]
self.episodes_so_far = 0
self.timesteps_so_far = 0
self.tstart = time.time()