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BaseAgent

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BaseAgent(
   env: VecEnv, eval_env: Optional[VecEnv] = None, tag: str = 'default', seed: int = 1,
   device: str = 'auto', pretraining: bool = False
)

Base class of the agent.

Args

  • env (VecEnv) : Vectorized environments for training.
  • eval_env (VecEnv) : Vectorized environments for evaluation.
  • tag (str) : An experiment tag.
  • seed (int) : Random seed for reproduction.
  • device (str) : Device (cpu, cuda, ...) on which the code should be run.
  • pretraining (bool) : Turn on pre-training model or not.

Returns

Base agent instance.

Methods:

.freeze

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.freeze(
   **kwargs
)

Freeze the agent and get ready for training.

.check

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.check()

Check the compatibility of selected modules.

.set

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.set(
   encoder: Optional[Encoder] = None, policy: Optional[Policy] = None,
   storage: Optional[Storage] = None, distribution: Optional[Distribution] = None,
   augmentation: Optional[Augmentation] = None,
   reward: Optional[IntrinsicRewardModule] = None
)

Set a module for the agent.

Args

  • encoder (Optional[Encoder]) : An encoder of rllte.xploit.encoder or a custom encoder.
  • policy (Optional[Policy]) : A policy of rllte.xploit.policy or a custom policy.
  • storage (Optional[Storage]) : A storage of rllte.xploit.storage or a custom storage.
  • distribution (Optional[Distribution]) : A distribution of rllte.xplore.distribution or a custom distribution.
  • augmentation (Optional[Augmentation]) : An augmentation of rllte.xplore.augmentation or a custom augmentation.
  • reward (Optional[IntrinsicRewardModule]) : A reward of rllte.xplore.reward or a custom reward.

Returns

None.

.mode

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.mode(
   training: bool = True
)

Set the training mode.

Args

  • training (bool) : True (training) or False (evaluation).

Returns

None.

.save

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.save()

Save the agent.

.update

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.update(
   *args, **kwargs
)

Update function of the agent.

.train

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.train(
   num_train_steps: int, init_model_path: Optional[str], log_interval: int,
   eval_interval: int, save_interval: int, num_eval_episodes: int, th_compile: bool
)

Training function.

Args

  • num_train_steps (int) : The number of training steps.
  • init_model_path (Optional[str]) : The path of the initial model.
  • log_interval (int) : The interval of logging.
  • eval_interval (int) : The interval of evaluation.
  • save_interval (int) : The interval of saving model.
  • num_eval_episodes (int) : The number of evaluation episodes.
  • th_compile (bool) : Whether to use th.compile or not.

Returns

None.

.eval

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.eval(
   num_eval_episodes: int
)

Evaluation function.

Args

  • num_eval_episodes (int) : The number of evaluation episodes.

Returns

The evaluation results.