46 lines
1.4 KiB
Python
46 lines
1.4 KiB
Python
"""A protocol that all policies should follow.
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This provides a mechanism for type-hinting and isinstance checks without requiring the policies classes
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subclass a base class.
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The protocol structure, method signatures, and docstrings should be used by developers as a reference for
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how to implement new policies.
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"""
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from typing import Protocol, runtime_checkable
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from torch import Tensor
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@runtime_checkable
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class Policy(Protocol):
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"""The required interface for implementing a policy."""
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name: str
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def reset(self):
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"""To be called whenever the environment is reset.
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Does things like clearing caches.
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"""
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def forward(self, batch: dict[str, Tensor]) -> dict:
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"""Run the batch through the model and compute the loss for training or validation.
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Returns a dictionary with "loss" and maybe other information.
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"""
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def select_action(self, batch: dict[str, Tensor]):
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"""Return one action to run in the environment (potentially in batch mode).
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When the model uses a history of observations, or outputs a sequence of actions, this method deals
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with caching.
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"""
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def update(self, batch):
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"""Does compute_loss then an optimization step.
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TODO(alexander-soare): We will move the optimization step back into the training loop, so this will
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disappear.
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"""
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