7036073649
This PR focus on some definition change of trainer to make it more friendly to use and be consistent with typical usage in research papers, typically change `collect-per-step` to `step-per-collect`, add `update-per-step` / `episode-per-collect` accordingly, and modify the documentation.
43 lines
1.4 KiB
Python
43 lines
1.4 KiB
Python
import numpy as np
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from typing import Any, Dict, Union, Optional
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from tianshou.data import Batch
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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: Batch,
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state: Optional[Union[dict, Batch, np.ndarray]] = None,
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**kwargs: Any,
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) -> Batch:
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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
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logits = np.random.rand(*mask.shape)
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logits[~mask] = -np.inf
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return Batch(act=logits.argmax(axis=-1))
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def learn(self, batch: Batch, **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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