class_docstr: Pytorch implementation of the Fitted Q-Evaluation (FQE) model from
  https://arxiv.org/abs/1911.06854
class_docstr_tok: ["Pytorch","implementation","of","the","Fitted","Q-Evaluation","(","FQE",")","model","from","https",":","\/\/arxiv.org\/abs\/1911.06854"]
code: def update_target(self, polyak_coef=None):
      # Update_target will be called periodically to copy Q network to
      # target Q network, using (soft) polyak_coef-synching.
      polyak_coef = polyak_coef or self.polyak_coef
      model_state_dict = self.q_model.state_dict()
      # Support partial (soft) synching.
      # If polyak_coef == 1.0: Full sync from Q-model to target Q-model.
      target_state_dict = self.target_q_model.state_dict()
      model_state_dict = {
          k: polyak_coef * model_state_dict[k] + (1 - polyak_coef) * v
          for k, v in target_state_dict.items()
      }
  
      self.target_q_model.load_state_dict(model_state_dict)
  
imports: from typing import Dict, Any
  from ray.rllib.models.utils import get_initializer
  from ray.rllib.policy import Policy
  from ray.rllib.models.catalog import ModelCatalog
  from ray.rllib.models.torch.torch_modelv2 import TorchModelV2
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.utils.annotations import DeveloperAPI
  from ray.rllib.utils.framework import try_import_torch
  from ray.rllib.utils.annotations import is_overridden
  from ray.rllib.utils.typing import ModelConfigDict, TensorType
  from gymnasium.spaces import Discrete
label_desc: Use image node_id 5 for calling the FQETorchModel obj's underlying member method code with example usage: obj.update_target(polyak_coef) without return types