import torch
from torch import nn
import torch.nn.functional as F
from tianshou.data import Batch
from tianshou.policy import PGPolicy
[docs]class A2CPolicy(PGPolicy):
"""Implementation of Synchronous Advantage Actor-Critic. arXiv:1602.01783
:param torch.nn.Module actor: the actor network following the rules in
:class:`~tianshou.policy.BasePolicy`. (s -> logits)
:param torch.nn.Module critic: the critic network. (s -> V(s))
:param torch.optim.Optimizer optim: the optimizer for actor and critic
network.
:param torch.distributions.Distribution dist_fn: for computing the action,
defaults to ``torch.distributions.Categorical``.
:param float discount_factor: in [0, 1], defaults to 0.99.
:param float vf_coef: weight for value loss, defaults to 0.5.
:param float ent_coef: weight for entropy loss, defaults to 0.01.
:param float max_grad_norm: clipping gradients in back propagation,
defaults to ``None``.
"""
def __init__(self, actor, critic, optim,
dist_fn=torch.distributions.Categorical,
discount_factor=0.99, vf_coef=.5, ent_coef=.01,
max_grad_norm=None, **kwargs):
super().__init__(None, optim, dist_fn, discount_factor)
self.actor = actor
self.critic = critic
self._w_vf = vf_coef
self._w_ent = ent_coef
self._grad_norm = max_grad_norm
[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.actor(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, actor_losses, vf_losses, ent_losses = [], [], [], []
for _ in range(repeat):
for b in batch.split(batch_size):
self.optim.zero_grad()
result = self(b)
dist = result.dist
v = self.critic(b.obs)
a = torch.tensor(b.act, device=dist.logits.device)
r = torch.tensor(b.returns, device=dist.logits.device)
a_loss = -(dist.log_prob(a) * (r - v).detach()).mean()
vf_loss = F.mse_loss(r[:, None], v)
ent_loss = dist.entropy().mean()
loss = a_loss + self._w_vf * vf_loss - self._w_ent * ent_loss
loss.backward()
if self._grad_norm:
nn.utils.clip_grad_norm_(
self.model.parameters(), max_norm=self._grad_norm)
self.optim.step()
actor_losses.append(a_loss.item())
vf_losses.append(vf_loss.item())
ent_losses.append(ent_loss.item())
losses.append(loss.item())
return {
'loss': losses,
'loss/actor': actor_losses,
'loss/vf': vf_losses,
'loss/ent': ent_losses,
}