108 lines
3.7 KiB
Python
108 lines
3.7 KiB
Python
import torch
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import numpy as np
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from tianshou.data import Batch
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from tianshou.policy import BasePolicy
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class PGPolicy(BasePolicy):
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"""Implementation of Vanilla Policy Gradient.
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:param torch.nn.Module model: a model following the rules in
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:class:`~tianshou.policy.BasePolicy`. (s -> logits)
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:param torch.optim.Optimizer optim: a torch.optim for optimizing the model.
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:param torch.distributions.Distribution dist_fn: for computing the action.
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:param float discount_factor: in [0, 1].
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.. seealso::
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Please refer to :class:`~tianshou.policy.BasePolicy` for more detailed
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explanation.
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"""
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def __init__(self, model, optim, dist_fn=torch.distributions.Categorical,
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discount_factor=0.99, **kwargs):
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super().__init__(**kwargs)
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self.model = model
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self.optim = optim
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self.dist_fn = dist_fn
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self._eps = np.finfo(np.float32).eps.item()
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assert 0 <= discount_factor <= 1, 'discount factor should in [0, 1]'
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self._gamma = discount_factor
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def process_fn(self, batch, buffer, indice):
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r"""Compute the discounted returns for each frame:
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.. math::
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G_t = \sum_{i=t}^T \gamma^{i-t}r_i
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, where :math:`T` is the terminal time step, :math:`\gamma` is the
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discount factor, :math:`\gamma \in [0, 1]`.
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"""
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batch.returns = self._vanilla_returns(batch)
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# batch.returns = self._vectorized_returns(batch)
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return batch
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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 has 4 keys:
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* ``act`` the action.
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* ``logits`` the network's raw output.
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* ``dist`` the action distribution.
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* ``state`` the hidden state.
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.. seealso::
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Please refer to :meth:`~tianshou.policy.BasePolicy.__call__` for
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more detailed explanation.
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"""
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logits, h = self.model(batch.obs, state=state, info=batch.info)
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if isinstance(logits, tuple):
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dist = self.dist_fn(*logits)
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else:
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dist = self.dist_fn(logits)
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act = dist.sample()
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return Batch(logits=logits, act=act, state=h, dist=dist)
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def learn(self, batch, batch_size=None, repeat=1, **kwargs):
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losses = []
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r = batch.returns
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batch.returns = (r - r.mean()) / (r.std() + self._eps)
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for _ in range(repeat):
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for b in batch.split(batch_size):
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self.optim.zero_grad()
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dist = self(b).dist
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a = torch.tensor(b.act, device=dist.logits.device)
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r = torch.tensor(b.returns, device=dist.logits.device)
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loss = -(dist.log_prob(a) * r).sum()
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loss.backward()
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self.optim.step()
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losses.append(loss.item())
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return {'loss': losses}
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def _vanilla_returns(self, batch):
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returns = batch.rew[:]
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last = 0
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for i in range(len(returns) - 1, -1, -1):
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if not batch.done[i]:
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returns[i] += self._gamma * last
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last = returns[i]
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return returns
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def _vectorized_returns(self, batch):
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# according to my tests, it is slower than _vanilla_returns
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# import scipy.signal
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convolve = np.convolve
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# convolve = scipy.signal.convolve
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rew = batch.rew[::-1]
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batch_size = len(rew)
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gammas = self._gamma ** np.arange(batch_size)
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c = convolve(rew, gammas)[:batch_size]
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T = np.where(batch.done[::-1])[0]
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d = np.zeros_like(rew)
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d[T] += c[T] - rew[T]
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d[T[1:]] -= d[T[:-1]] * self._gamma ** np.diff(T)
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return (c - convolve(d, gammas)[:batch_size])[::-1]
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