class_docstr: PyTorch policy class used with PPO.
class_docstr_tok: ["PyTorch","policy","class","used","with","PPO","."]
code: def __init__(self, observation_space, action_space, config):
      config = dict(
          ray.rllib.algorithms.ppo.ppo.PPOConfig().to_dict(), **config
      )
      validate_config(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"],
      )
      KLCoeffMixin.__init__(self, config)
  
      self._initialize_loss_from_dummy_batch()
  
imports: import logging
  from typing import Dict, List, Type, Union
  import ray
  from ray.rllib.algorithms.ppo.ppo_tf_policy import validate_config
  from ray.rllib.evaluation.postprocessing import Postprocessing, compute_gae_for_sample_batch
  from ray.rllib.models.action_dist import ActionDistribution
  from ray.rllib.models.modelv2 import ModelV2
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.policy.torch_mixins import EntropyCoeffSchedule, KLCoeffMixin, 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, explained_variance, sequence_mask, warn_if_infinite_kl_divergence
  from ray.rllib.utils.typing import TensorType
label_desc: Use image node_id 1 to create a new PPOTorchPolicy object from inherited base classes: ValueNetworkMixin, LearningRateSchedule, EntropyCoeffSchedule, KLCoeffMixin, TorchPolicyV2 with example: obj = PPOTorchPolicy(observation_space, action_space, config)