600f4bbd55
Preparation for #914 and #920 Changes formatting to ruff and black. Remove python 3.8 ## Additional Changes - Removed flake8 dependencies - Adjusted pre-commit. Now CI and Make use pre-commit, reducing the duplication of linting calls - Removed check-docstyle option (ruff is doing that) - Merged format and lint. In CI the format-lint step fails if any changes are done, so it fulfills the lint functionality. --------- Co-authored-by: Jiayi Weng <jiayi@openai.com>
47 lines
1.6 KiB
Python
47 lines
1.6 KiB
Python
from typing import Any, Optional, Union, cast
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import numpy as np
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from tianshou.data import Batch
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from tianshou.data.batch import BatchProtocol
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from tianshou.data.types import ActBatchProtocol, RolloutBatchProtocol
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from tianshou.policy import BasePolicy
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class RandomPolicy(BasePolicy):
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"""A random agent used in multi-agent learning.
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It randomly chooses an action from the legal action.
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"""
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def forward(
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self,
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batch: RolloutBatchProtocol,
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state: Optional[Union[dict, BatchProtocol, np.ndarray]] = None,
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**kwargs: Any,
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) -> ActBatchProtocol:
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"""Compute the random action over the given batch data.
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The input should contain a mask in batch.obs, with "True" to be
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available and "False" to be unavailable. For example,
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``batch.obs.mask == np.array([[False, True, False]])`` means with batch
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size 1, action "1" is available but action "0" and "2" are unavailable.
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:return: A :class:`~tianshou.data.Batch` with "act" key, containing
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the random action.
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.. seealso::
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Please refer to :meth:`~tianshou.policy.BasePolicy.forward` for
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more detailed explanation.
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"""
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mask = batch.obs.mask # type: ignore
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logits = np.random.rand(*mask.shape)
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logits[~mask] = -np.inf
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result = Batch(act=logits.argmax(axis=-1))
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return cast(ActBatchProtocol, result)
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def learn(self, batch: RolloutBatchProtocol, *args: Any, **kwargs: Any) -> dict[str, float]:
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"""Since a random agent learns nothing, it returns an empty dict."""
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return {}
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