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