60 lines
1.9 KiB
Python
60 lines
1.9 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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We also expect all policies to subclass torch.nn.Module and PyTorchModelHubMixin.
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"""
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name: str
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def __init__(self, cfg, dataset_stats: dict[str, dict[str, Tensor]] | None = None):
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"""
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Args:
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cfg: Policy configuration class instance or None, in which case the default instantiation of the
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configuration class is used.
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dataset_stats: Dataset statistics to be used for normalization.
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"""
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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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@runtime_checkable
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class PolicyWithUpdate(Policy, Protocol):
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def update(self):
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"""An update method that is to be called after a training optimization step.
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Implements an additional updates the model parameters may need (for example, doing an EMA step for a
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target model, or incrementing an internal buffer).
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"""
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