random

Source code: tianshou/policy/random.py

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class MARLRandomTrainingStats(*, train_time: float = 0.0, smoothed_loss: dict = <factory>)

Bases: TrainingStats

class MARLRandomPolicy(*, action_space: Space, observation_space: Space | None = None, action_scaling: bool = False, action_bound_method: Literal['clip', 'tanh'] | None = 'clip', lr_scheduler: LRScheduler | MultipleLRSchedulers | None = None)

Bases: BasePolicy[TMARLRandomTrainingStats]

A random agent used in multi-agent learning.

It randomly chooses an action from the legal action.

Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(batch: ObsBatchProtocol, state: dict | BatchProtocol | ndarray | None = None, **kwargs: Any) → ActBatchProtocol

Compute the random action over the given batch data.

The input should contain a mask in batch.obs, with “True” to be available and “False” to be unavailable. For example, batch.obs.mask == np.array([[False, True, False]]) means with batch size 1, action “1” is available but action “0” and “2” are unavailable.

Returns:

A Batch with “act” key, containing the random action.

See also

Please refer to forward() for more detailed explanation.

learn(batch: RolloutBatchProtocol, *args: Any, **kwargs: Any) → TMARLRandomTrainingStats

Since a random agent learns nothing, it returns an empty dict.