import torch
import numpy as np
from torch import nn
import torch.nn.functional as F
from typing import Any, Dict, List, Union, Optional, Callable
from tianshou.policy import PGPolicy
from tianshou.data import Batch, ReplayBuffer, to_torch_as, to_numpy
[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 dist_fn: distribution class for computing the action.
:type dist_fn: Callable[[], torch.distributions.Distribution]
: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.
:param float gae_lambda: in [0, 1], param for Generalized Advantage
Estimation, defaults to 0.95.
:param bool reward_normalization: normalize the reward to Normal(0, 1),
defaults to False.
:param int max_batchsize: the maximum size of the batch when computing GAE,
depends on the size of available memory and the memory cost of the
model; should be as large as possible within the memory constraint;
defaults to 256.
.. seealso::
Please refer to :class:`~tianshou.policy.BasePolicy` for more detailed
explanation.
"""
def __init__(
self,
actor: torch.nn.Module,
critic: torch.nn.Module,
optim: torch.optim.Optimizer,
dist_fn: Callable[[], torch.distributions.Distribution],
discount_factor: float = 0.99,
vf_coef: float = 0.5,
ent_coef: float = 0.01,
max_grad_norm: Optional[float] = None,
gae_lambda: float = 0.95,
reward_normalization: bool = False,
max_batchsize: int = 256,
**kwargs: Any
) -> None:
super().__init__(None, optim, dist_fn, discount_factor, **kwargs)
self.actor = actor
self.critic = critic
assert 0.0 <= gae_lambda <= 1.0, "GAE lambda should be in [0, 1]."
self._lambda = gae_lambda
self._weight_vf = vf_coef
self._weight_ent = ent_coef
self._grad_norm = max_grad_norm
self._batch = max_batchsize
self._rew_norm = reward_normalization
[docs] def process_fn(
self, batch: Batch, buffer: ReplayBuffer, indice: np.ndarray
) -> Batch:
if self._lambda in [0.0, 1.0]:
return self.compute_episodic_return(
batch, None, gamma=self._gamma, gae_lambda=self._lambda)
v_ = []
with torch.no_grad():
for b in batch.split(self._batch, shuffle=False, merge_last=True):
v_.append(to_numpy(self.critic(b.obs_next)))
v_ = np.concatenate(v_, axis=0)
return self.compute_episodic_return(
batch, v_, gamma=self._gamma, gae_lambda=self._lambda,
rew_norm=self._rew_norm)
[docs] def forward(
self,
batch: Batch,
state: Optional[Union[dict, Batch, np.ndarray]] = None,
**kwargs: Any
) -> Batch:
"""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.
.. seealso::
Please refer to :meth:`~tianshou.policy.BasePolicy.forward` for
more detailed explanation.
"""
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) # type: ignore
act = dist.sample()
return Batch(logits=logits, act=act, state=h, dist=dist)
[docs] def learn( # type: ignore
self, batch: Batch, batch_size: int, repeat: int, **kwargs: Any
) -> Dict[str, List[float]]:
losses, actor_losses, vf_losses, ent_losses = [], [], [], []
for _ in range(repeat):
for b in batch.split(batch_size, merge_last=True):
self.optim.zero_grad()
dist = self(b).dist
v = self.critic(b.obs).flatten()
a = to_torch_as(b.act, v)
r = to_torch_as(b.returns, v)
log_prob = dist.log_prob(a).reshape(len(r), -1).transpose(0, 1)
a_loss = -(log_prob * (r - v).detach()).mean()
vf_loss = F.mse_loss(r, v) # type: ignore
ent_loss = dist.entropy().mean()
loss = a_loss + self._weight_vf * vf_loss - \
self._weight_ent * ent_loss
loss.backward()
if self._grad_norm is not None:
nn.utils.clip_grad_norm_(
list(self.actor.parameters())
+ list(self.critic.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,
}