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