def _init(self, config, env_creator):
validate_config(config)
env_context = EnvContext(config["env_config"] or {}, worker_index=0)
env = env_creator(env_context)
self._policy_class = get_policy_class(config)
self.policy = self._policy_class(env.observation_space,
env.action_space, config)
self.optimizer = optimizers.SGD(self.policy, config["sgd_stepsize"])
self.rollouts_used = config["rollouts_used"]
self.num_rollouts = config["num_rollouts"]
self.report_length = config["report_length"]
# Create the shared noise table.
logger.info("Creating shared noise table.")
noise_id = create_shared_noise.remote(config["noise_size"])
self.noise = SharedNoiseTable(ray.get(noise_id))
# Create the actors.
logger.info("Creating actors.")
self.workers = [
Worker.remote(config, env_creator, noise_id, idx + 1)
for idx in range(config["num_workers"])
]
self.episodes_so_far = 0
self.reward_list = []
self.tstart = time.time()
def _init(self, config, env_creator):
super()._init(config, env_creator)
validate_config(config)
env_context = EnvContext(config["env_config"] or {}, worker_index=0)
env = env_creator(env_context)
policy_cls = get_ars_frac_policy_class(config)
self.policy = policy_cls(env.observation_space, env.action_space, config)