EarlyStopping

class torchrl.trainers.EarlyStopping(*, monitor: NestedKey = 'r_evaluation', mode: Literal['min', 'max'] = 'max', min_delta: float = 0.0, patience: int = 100000, wait_for: int = 1000000, check_finite: bool = True)

Early stopping hook for Trainer.

This hook monitors a scalar metric and stops training when that metric does not improve according to a configured criterion.

By default, the hook monitors "r_evaluation".

Parameters:

• monitor (NestedKey, optional) – Metric name to monitor. Defaults to "r_evaluation".

• mode (Literal["min", "max"], optional) – One of "min" or "max". In "max" mode, larger metric values are considered better. Defaults to "max".

• min_delta (float, optional) – Minimum absolute improvement required to qualify as better. Defaults to 0.0.

• patience (int, optional) – Maximum number of non-improving frames allowed before stopping. Defaults to 100_000.

• wait_for (int, optional) – Number of initial frames to ignore before checking the stopping criterion. Defaults to 1_000_000.

• check_finite (bool, optional) – If True, non-finite metric values (NaN or inf) trigger early stopping. Defaults to True.

Examples

>>> LogScalar(("next", "reward"), "r_training").register(trainer)
>>> EarlyStopping(monitor="r_training", patience=10_000).register(trainer)

register(trainer: Trainer, name: str = 'early_stopping') → None

Registers the hook in the trainer at a default location.

Parameters:

• trainer (Trainer) – the trainer where the hook must be registered.

• name (str) – the name of the hook.

Note

To register the hook at another location than the default, use register_op().