MultiEvaluator¶
-
class
maze.train.trainers.common.evaluators.multi_evaluator.
MultiEvaluator
(evaluators: List[maze.train.trainers.common.evaluators.evaluator.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.
- Parameters
evaluators – Evaluators to run.
-
evaluate
(policy: maze.core.agent.torch_policy.TorchPolicy) → None¶ (overrides
Evaluator
)Evaluate given policy using the given evaluators.
- param policy
Policy to evaluate