77 lines
2.9 KiB
Python
77 lines
2.9 KiB
Python
from torch import nn
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from abc import ABC, abstractmethod
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class BasePolicy(ABC, nn.Module):
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"""Tianshou aims to modularizing RL algorithms. It comes into several
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classes of policies in Tianshou. All of the policy classes must inherit
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:class:`~tianshou.policy.BasePolicy`.
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A policy class typically has four parts:
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* :meth:`~tianshou.policy.BasePolicy.__init__`: initialize the policy, \
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including coping the target network and so on;
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* :meth:`~tianshou.policy.BasePolicy.__call__`: compute action with given \
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observation;
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* :meth:`~tianshou.policy.BasePolicy.process_fn`: pre-process data from \
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the replay buffer (this function can interact with replay buffer);
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* :meth:`~tianshou.policy.BasePolicy.learn`: update policy with a given \
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batch of data.
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Most of the policy needs a neural network to predict the action and an
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optimizer to optimize the policy. The rules of self-defined networks are:
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1. Input: observation ``obs`` (may be a ``numpy.ndarray`` or \
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``torch.Tensor``), hidden state ``state`` (for RNN usage), and other \
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information ``info`` provided by the environment.
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2. Output: some ``logits`` and the next hidden state ``state``. The logits\
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could be a tuple instead of a ``torch.Tensor``. It depends on how the \
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policy process the network output. For example, in PPO, the return of \
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the network might be ``(mu, sigma), state`` for Gaussian policy.
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Since :class:`~tianshou.policy.BasePolicy` inherits ``torch.nn.Module``,
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you can operate :class:`~tianshou.policy.BasePolicy` almost the same as
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``torch.nn.Module``, for instance, load and save the model:
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::
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torch.save(policy.state_dict(), 'policy.pth')
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policy.load_state_dict(torch.load('policy.pth'))
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"""
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def __init__(self, **kwargs):
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super().__init__()
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def process_fn(self, batch, buffer, indice):
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"""Pre-process the data from the provided replay buffer. Check out
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:ref:`policy_concept` for more information.
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"""
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return batch
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@abstractmethod
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def __call__(self, batch, state=None, **kwargs):
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"""Compute action over the given batch data.
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:return: A :class:`~tianshou.data.Batch` which MUST have the following\
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keys:
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* ``act`` an numpy.ndarray or a torch.Tensor, the action over \
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given batch data.
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* ``state`` a dict, an numpy.ndarray or a torch.Tensor, the \
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internal state of the policy, ``None`` as default.
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Other keys are user-defined. It depends on the algorithm. For example,
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::
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# some code
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return Batch(logits=..., act=..., state=None, dist=...)
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"""
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pass
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@abstractmethod
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def learn(self, batch, **kwargs):
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"""Update policy with a given batch of data.
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:return: A dict which includes loss and its corresponding label.
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"""
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pass
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