ray.rllib.callbacks.callbacks.RLlibCallback.on_evaluate_end#

RLlibCallback.on_evaluate_end(*, algorithm: Algorithm, metrics_logger: MetricsLogger | None = None, evaluation_metrics: dict, **kwargs) → None[source]#

Runs when the evaluation is done.

Runs at the end of Algorithm.evaluate().

Parameters:

  • algorithm – Reference to the algorithm instance.

  • metrics_logger – The MetricsLogger object inside the Algorithm. Can be used to log custom metrics after the most recent evaluation round.

  • evaluation_metrics – Results dict to be returned from algorithm.evaluate(). You can mutate this object to add additional metrics.

  • kwargs – Forward compatibility placeholder.