import numpy as np
from typing import Any, Dict, Union, Optional
from tianshou.data import Batch
from tianshou.policy import BasePolicy
[docs]class RandomPolicy(BasePolicy):
"""A random agent used in multi-agent learning.
It randomly chooses an action from the legal action.
"""
[docs] def forward(
self,
batch: Batch,
state: Optional[Union[dict, Batch, np.ndarray]] = None,
**kwargs: Any,
) -> Batch:
"""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.
:return: A :class:`~tianshou.data.Batch` with "act" key, containing
the random action.
.. seealso::
Please refer to :meth:`~tianshou.policy.BasePolicy.forward` for
more detailed explanation.
"""
mask = batch.obs.mask
logits = np.random.rand(*mask.shape)
logits[~mask] = -np.inf
return Batch(act=logits.argmax(axis=-1))
[docs] def learn(self, batch: Batch, **kwargs: Any) -> Dict[str, float]:
"""Since a random agent learn nothing, it returns an empty dict."""
return {}