MultiEvaluator¶

class maze.train.trainers.common.evaluators.multi_evaluator.MultiEvaluator(evaluators: List[Evaluator])¶

Evaluates the given policy using multiple different evaluators (ran in sequence).

Useful when evaluating a policy in different scenarios. E.g., during behavioral cloning, we might want to evaluate the policy first on a validation dataset and then through an evaluation rollout.