text: import torch
  import numpy as np
  from torch import nn
  import torch.nn.functional as F
  from typing import Dict, List, Union, Optional
  
  from tianshou.policy import PGPolicy
  from tianshou.data import Batch, ReplayBuffer, to_torch_as, to_numpy
  
  
  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``.
      :param float gae_lambda: in [0, 1], param for Generalized Advantage
          Estimation, defaults to 0.95.
  
      .. 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: torch.distributions.Distribution
                   = torch.distributions.Categorical,
                   discount_factor: float = 0.99,
                   vf_coef: float = .5,
                   ent_coef: float = .01,
                   max_grad_norm: Optional[float] = None,
                   gae_lambda: float = 0.95,
                   reward_normalization: bool = False,
                   **kwargs) -> None:
          super().__init__(None, optim, dist_fn, discount_factor, **kwargs)
          self.actor = actor
          self.critic = critic
          assert 0 <= gae_lambda <= 1, 'GAE lambda should be in [0, 1].'
          self._lambda = gae_lambda
          self._w_vf = vf_coef
          self._w_ent = ent_coef
          self._grad_norm = max_grad_norm
          self._batch = 64
          self._rew_norm = reward_normalization
  
      def process_fn(self, batch: Batch, buffer: ReplayBuffer,
                     indice: np.ndarray) -> Batch:
          if self._lambda in [0, 1]:
              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):
                  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)
  
      def forward(self, batch: Batch,
                  state: Optional[Union[dict, Batch, np.ndarray]] = None,
                  **kwargs) -> 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)
          act = dist.sample()
          return Batch(logits=logits, act=act, state=h, dist=dist)
  
      def learn(self, batch: Batch, batch_size: int, repeat: int,
                **kwargs) -> Dict[str, List[float]]:
          self._batch = batch_size
          r = batch.returns
          if self._rew_norm and not np.isclose(r.std(), 0):
              batch.returns = (r - r.mean()) / r.std()
          losses, actor_losses, vf_losses, ent_losses = [], [], [], []
          for _ in range(repeat):
              for b in batch.split(batch_size):
                  self.optim.zero_grad()
                  dist = self(b).dist
                  v = self.critic(b.obs).squeeze(-1)
                  a = to_torch_as(b.act, v)
                  r = to_torch_as(b.returns, v)
                  a_loss = -(dist.log_prob(a).reshape(v.shape) * (r - v).detach()
                             ).mean()
                  vf_loss = F.mse_loss(r, 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 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,
          }
  