trainer

Source code: tianshou/highlevel/trainer.py

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class TrainerCallbacks(epoch_callback_train: TrainerEpochCallbackTrain | None = None, epoch_callback_test: TrainerEpochCallbackTest | None = None, stop_callback: TrainerStopCallback | None = None)

Container for callbacks used during training.

epoch_callback_test: TrainerEpochCallbackTest | None = None

epoch_callback_train: TrainerEpochCallbackTrain | None = None

stop_callback: TrainerStopCallback | None = None

class TrainerEpochCallbackTest

Callback which is called at the beginning of each epoch.

abstract callback(epoch: int, env_step: int | None, context: TrainingContext) → None

get_trainer_fn(context: TrainingContext) → Callable[[int, int | None], None]

class TrainerEpochCallbackTrain

Callback which is called at the beginning of each epoch.

abstract callback(epoch: int, env_step: int, context: TrainingContext) → None

get_trainer_fn(context: TrainingContext) → Callable[[int, int], None]

class TrainerStopCallback

Callback indicating whether training should stop.

get_trainer_fn(context: TrainingContext) → Callable[[float], bool]

abstract should_stop(mean_rewards: float, context: TrainingContext) → bool

Determines whether training should stop.

Parameters:

• mean_rewards – the average undiscounted returns of the testing result

• context – the training context

Returns:

True if the goal has been reached and training should stop, False otherwise

class TrainingContext(policy: TPolicy, envs: Environments, logger: BaseLogger)