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"]
docstr: Returns the current state of the FQE Model.
docstr_tok: ["Returns","the","current","state","of","the","FQE","Model","."]
code: def get_state(self) -> Dict[str, Any]:
      """Returns the current state of the FQE Model."""
      return {
          "policy_state": self.policy.get_state(),
          "model_config": self.model_config,
          "n_iters": self.n_iters,
          "lr": self.lr,
          "min_loss_threshold": self.min_loss_threshold,
          "clip_grad_norm": self.clip_grad_norm,
          "minibatch_size": self.minibatch_size,
          "polyak_coef": self.polyak_coef,
          "gamma": self.gamma,
          "q_model_state": self.q_model.state_dict(),
          "target_q_model_state": self.target_q_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 7 for calling the FQETorchModel obj's underlying member method code with example usage: obj.get_state() and returns: dict