class_docstr: REST client to interact with an RLlib policy server.
docstr: Record the start of one or more episode(s).
  
  Args:
      episode_id (Optional[str]): Unique string id for the episode or
          None for it to be auto-assigned.
      training_enabled: Whether to use experiences for this
          episode to improve the policy.
  
  Returns:
      episode_id: Unique string id for the episode.
docstr_tok: ["Record","the","start","of","one","or","more","episode","(","s",")",".","Args",":","episode_id","(","Optional","[","str","]",")",":","Unique","string","id","for","the","episode","or","None","for","it","to","be","auto-assigned",".","training_enabled",":","Whether","to","use","experiences","for","this","episode","to","improve","the","policy",".","Returns",":","episode_id",":","Unique","string","id","for","the","episode","."]
code: def start_episode(
      self,
      episode_id: Optional[str] = None,
      training_enabled: bool = True,
  ) -> str:
      """Record the start of one or more episode(s).
  
      Args:
          episode_id (Optional[str]): Unique string id for the episode or
              None for it to be auto-assigned.
          training_enabled: Whether to use experiences for this
              episode to improve the policy.
  
      Returns:
          episode_id: Unique string id for the episode.
      """
  
      if self.local:
          self._update_local_policy()
          return self.env.start_episode(episode_id, training_enabled)
  
      return self._send(
          {
              "episode_id": episode_id,
              "command": Commands.START_EPISODE,
              "training_enabled": training_enabled,
          }
      )["episode_id"]
  
imports: import logging
  import threading
  import time
  from typing import Union, Optional
  from enum import Enum
  import ray.cloudpickle
  from ray.rllib.env.external_env import ExternalEnv
  from ray.rllib.env.external_multi_agent_env import ExternalMultiAgentEnv
  from ray.rllib.env.multi_agent_env import MultiAgentEnv
  from ray.rllib.policy.sample_batch import MultiAgentBatch
  from ray.rllib.utils.annotations import PublicAPI
  from ray.rllib.utils.typing import MultiAgentDict, EnvInfoDict, EnvObsType, EnvActionType
label_desc: Use image node_id 2 for calling the PolicyClient obj's underlying member method code with example usage: obj.start_episode(episode_id, training_enabled) and returns: self, self