lit_sb3.callbacks.stop

source module lit_sb3.callbacks.stop

Classes

• StopTrainingOnRewardThreshold — Stop the training once a threshold in episodic reward.

• StopTrainingOnMaxEpisodes — Stop the training once a maximum number of episodes are played.

• StopTrainingOnNoModelImprovement — Stop the training early if there is no new best model (new best mean reward) after more than N consecutive.

source class StopTrainingOnRewardThreshold(reward_threshold: float, verbose: int = 0)

Bases : BaseCallback

Stop the training once a threshold in episodic reward.

has been reached (i.e. when the model is good enough).

It must be used with the EvalCallback.

Parameters

• reward_threshold : float — Minimum expected reward per episode to stop training.

• verbose : int — Verbosity level: 0 for no output, 1 for indicating when training ended because episodic reward threshold reached

Attributes

• state_key : str — Identifier for the state of the callback.

source class StopTrainingOnMaxEpisodes(max_episodes: int, verbose: int = 0)

Bases : BaseCallback

Stop the training once a maximum number of episodes are played.

For multiple environments presumes that, the desired behavior is that the agent trains on each env for max_episodes and in total for max_episodes * n_envs episodes.

Parameters

• max_episodes : int — Maximum number of episodes to stop training.

• verbose : int — Verbosity level: 0 for no output, 1 for indicating information about when training ended by reaching max_episodes

Attributes

• state_key : str — Identifier for the state of the callback.

source class StopTrainingOnNoModelImprovement(max_no_improvement_evals: int, min_evals: int = 0, verbose: int = 0)

Bases : BaseCallback

Stop the training early if there is no new best model (new best mean reward) after more than N consecutive.

evaluations.

It is possible to define a minimum number of evaluations before start to count evaluations without improvement.

It must be used with the EvalCallback.

Parameters

• max_no_improvement_evals : int — Maximum number of consecutive evaluations without a new best model.

• min_evals : int — Number of evaluations before start to count evaluations without improvements.

• verbose : int — Verbosity level: 0 for no output, 1 for indicating when training ended because no new best model

Attributes

• state_key : str — Identifier for the state of the callback.