text: import gym
  from typing import Optional, Dict
  
  import ray
  from ray.rllib.agents.ppo.ppo_torch_policy import ValueNetworkMixin
  from ray.rllib.evaluation.episode import MultiAgentEpisode
  from ray.rllib.evaluation.postprocessing import compute_gae_for_sample_batch, \
      Postprocessing
  from ray.rllib.models.action_dist import ActionDistribution
  from ray.rllib.models.modelv2 import ModelV2
  from ray.rllib.policy.policy import Policy
  from ray.rllib.policy.policy_template import build_policy_class
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.utils.annotations import Deprecated
  from ray.rllib.utils.framework import try_import_torch
  from ray.rllib.utils.torch_ops import apply_grad_clipping, sequence_mask
  from ray.rllib.utils.typing import TrainerConfigDict, TensorType, \
      PolicyID, LocalOptimizer
  
  torch, nn = try_import_torch()
  
  
  @Deprecated(
      old="rllib.agents.a3c.a3c_torch_policy.add_advantages",
      new="rllib.evaluation.postprocessing.compute_gae_for_sample_batch",
      error=False)
  def add_advantages(
          policy: Policy,
          sample_batch: SampleBatch,
          other_agent_batches: Optional[Dict[PolicyID, SampleBatch]] = None,
          episode: Optional[MultiAgentEpisode] = None) -> SampleBatch:
  
      return compute_gae_for_sample_batch(policy, sample_batch,
                                          other_agent_batches, episode)
  
  
  def actor_critic_loss(policy: Policy, model: ModelV2,
                        dist_class: ActionDistribution,
                        train_batch: SampleBatch) -> TensorType:
      logits, _ = model.from_batch(train_batch)
      values = model.value_function()
  
      if policy.is_recurrent():
          B = len(train_batch[SampleBatch.SEQ_LENS])
          max_seq_len = logits.shape[0] // B
          mask_orig = sequence_mask(train_batch[SampleBatch.SEQ_LENS],
                                    max_seq_len)
          valid_mask = torch.reshape(mask_orig, [-1])
      else:
          valid_mask = torch.ones_like(values, dtype=torch.bool)
  
      dist = dist_class(logits, model)
      log_probs = dist.logp(train_batch[SampleBatch.ACTIONS]).reshape(-1)
      pi_err = -torch.sum(
          torch.masked_select(log_probs * train_batch[Postprocessing.ADVANTAGES],
                              valid_mask))
  
      # Compute a value function loss.
      if policy.config["use_critic"]:
          value_err = 0.5 * torch.sum(
              torch.pow(
                  torch.masked_select(
                      values.reshape(-1) -
                      train_batch[Postprocessing.VALUE_TARGETS], valid_mask),
                  2.0))
      # Ignore the value function.
      else:
          value_err = 0.0
  
      entropy = torch.sum(torch.masked_select(dist.entropy(), valid_mask))
  
      total_loss = (pi_err + value_err * policy.config["vf_loss_coeff"] -
                    entropy * policy.config["entropy_coeff"])
  
      policy.entropy = entropy
      policy.pi_err = pi_err
      policy.value_err = value_err
  
      return total_loss
  
  
  def loss_and_entropy_stats(policy: Policy,
                             train_batch: SampleBatch) -> Dict[str, TensorType]:
      return {
          "policy_entropy": policy.entropy,
          "policy_loss": policy.pi_err,
          "vf_loss": policy.value_err,
      }
  
  
  def model_value_predictions(
          policy: Policy, input_dict: Dict[str, TensorType], state_batches,
          model: ModelV2,
          action_dist: ActionDistribution) -> Dict[str, TensorType]:
      return {SampleBatch.VF_PREDS: model.value_function()}
  
  
  def torch_optimizer(policy: Policy,
                      config: TrainerConfigDict) -> LocalOptimizer:
      return torch.optim.Adam(policy.model.parameters(), lr=config["lr"])
  
  
  def setup_mixins(policy: Policy, obs_space: gym.spaces.Space,
                   action_space: gym.spaces.Space,
                   config: TrainerConfigDict) -> None:
      """Call all mixin classes' constructors before PPOPolicy initialization.
  
      Args:
          policy (Policy): The Policy object.
          obs_space (gym.spaces.Space): The Policy's observation space.
          action_space (gym.spaces.Space): The Policy's action space.
          config (TrainerConfigDict): The Policy's config.
      """
      ValueNetworkMixin.__init__(policy, obs_space, action_space, config)
  
  
  A3CTorchPolicy = build_policy_class(
      name="A3CTorchPolicy",
      framework="torch",
      get_default_config=lambda: ray.rllib.agents.a3c.a3c.DEFAULT_CONFIG,
      loss_fn=actor_critic_loss,
      stats_fn=loss_and_entropy_stats,
      postprocess_fn=compute_gae_for_sample_batch,
      extra_action_out_fn=model_value_predictions,
      extra_grad_process_fn=apply_grad_clipping,
      optimizer_fn=torch_optimizer,
      before_loss_init=setup_mixins,
      mixins=[ValueNetworkMixin],
  )
  