Evaluator which tests Meta-RL algorithms on test environments.
- class MetaEvaluator(*, test_task_sampler, n_exploration_eps=10, n_test_tasks=None, n_test_episodes=1, prefix='MetaTest', test_task_names=None, worker_class=DefaultWorker, worker_args=None)¶
Evaluates Meta-RL algorithms on test environments.
test_task_sampler (TaskSampler) – Sampler for test tasks. To demonstrate the effectiveness of a meta-learning method, these should be different from the training tasks.
n_test_tasks (int or None) – Number of test tasks to sample each time evaluation is performed. Note that tasks are sampled “without replacement”. If None, is set to test_task_sampler.n_tasks.
n_exploration_eps (int) – Number of episodes to gather from the exploration policy before requesting the meta algorithm to produce an adapted policy.
n_test_episodes (int) – Number of episodes to use for each adapted policy. The adapted policy should forget previous episodes when .reset() is called.
prefix (str) – Prefix to use when logging. Defaults to MetaTest. For example, this results in logging the key ‘MetaTest/SuccessRate’. If not set to MetaTest, it should probably be set to MetaTrain.
worker_class (type) – Type of worker the Sampler should use.
worker_args (dict or None) – Additional arguments that should be passed to the worker.