ray.rllib.callbacks.callbacks.RLlibCallback.on_episode_start#

RLlibCallback.on_episode_start(*, episode: SingleAgentEpisode | MultiAgentEpisode | EpisodeV2, env_runner: EnvRunner | None = None, metrics_logger: MetricsLogger | None = None, env: gymnasium.Env | None = None, env_index: int, rl_module: RLModule | None = None, worker: EnvRunner | None = None, base_env: BaseEnv | None = None, policies: Dict[str, Policy] | None = None, **kwargs) → None[source]#

Callback run right after an Episode has been started.

This method gets called after a SingleAgentEpisode or MultiAgentEpisode instance has been reset with a call to env.reset() by the EnvRunner.

  1. Single-/MultiAgentEpisode created: on_episode_created() is called.

  2. Respective sub-environment (gym.Env) is reset().

  3. Single-/MultiAgentEpisode starts: This callback is called.

  4. Stepping through sub-environment/episode commences.

Parameters:

  • episode – The just started (after env.reset()) SingleAgentEpisode or MultiAgentEpisode object.

  • env_runner – Reference to the EnvRunner running the env and episode.

  • metrics_logger – The MetricsLogger object inside the env_runner. Can be used to log custom metrics during env/episode stepping.

  • env – The gym.Env or gym.vector.Env object running the started episode.

  • env_index – The index of the sub-environment that is about to be reset (within the vector of sub-environments of the BaseEnv).

  • rl_module – The RLModule used to compute actions for stepping the env. In a single-agent setup, this is a (single-agent) RLModule, in a multi- agent setup, this will be a MultiRLModule.

  • kwargs – Forward compatibility placeholder.