class_docstr: PyTorch Policy class used with A3C.
class_docstr_tok: ["PyTorch","Policy","class","used","with","A3C","."]
code: def __init__(self, observation_space, action_space, config):
      TorchPolicyV2.__init__(
          self,
          observation_space,
          action_space,
          config,
          max_seq_len=config["model"]["max_seq_len"],
      )
      ValueNetworkMixin.__init__(self, config)
      LearningRateSchedule.__init__(
          self, config["lr"], config["lr_schedule"]
      )
      EntropyCoeffSchedule.__init__(
          self,
          config["entropy_coeff"],
          config["entropy_coeff_schedule"],
      )
  
      # TODO: Don't require users to call this manually.
      self._initialize_loss_from_dummy_batch()
  
imports: from typing import Dict, List, Optional, Type, Union
  from ray.rllib.evaluation.episode import Episode
  from ray.rllib.evaluation.postprocessing import Postprocessing, compute_gae_for_sample_batch
  from ray.rllib.models.modelv2 import ModelV2
  from ray.rllib.models.torch.torch_action_dist import TorchDistributionWrapper
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.policy.torch_mixins import EntropyCoeffSchedule, LearningRateSchedule, ValueNetworkMixin
  from ray.rllib.policy.torch_policy_v2 import TorchPolicyV2
  from ray.rllib.utils.annotations import override
  from ray.rllib.utils.framework import try_import_torch
  from ray.rllib.utils.numpy import convert_to_numpy
  from ray.rllib.utils.torch_utils import apply_grad_clipping, sequence_mask
  from ray.rllib.utils.typing import AgentID, TensorType
label_desc: Use image node_id 1 to create a new A3CTorchPolicy object from inherited base classes: ValueNetworkMixin, LearningRateSchedule, EntropyCoeffSchedule, TorchPolicyV2 with example: obj = A3CTorchPolicy(observation_space, action_space, config)