2cc34fb72b
Closes #914 Additional changes: - Deprecate python below 11 - Remove 3rd party and throughput tests. This simplifies install and test pipeline - Remove gym compatibility and shimmy - Format with 3.11 conventions. In particular, add `zip(..., strict=True/False)` where possible Since the additional tests and gym were complicating the CI pipeline (flaky and dist-dependent), it didn't make sense to work on fixing the current tests in this PR to then just delete them in the next one. So this PR changes the build and removes these tests at the same time.
47 lines
1.6 KiB
Python
47 lines
1.6 KiB
Python
from typing import Any, 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: dict | BatchProtocol | np.ndarray | None = 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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