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: Sets the current state of the FQE Model.
  Args:
      state: A state dict returned by `get_state()`.
docstr_tok: ["Sets","the","current","state","of","the","FQE","Model",".","Args",":","state",":","A","state","dict","returned","by","`","get_state","(",")","`","."]
code: def set_state(self, state: Dict[str, Any]) -> None:
      """Sets the current state of the FQE Model.
      Args:
          state: A state dict returned by `get_state()`.
      """
      self.n_iters = state["n_iters"]
      self.lr = state["lr"]
      self.min_loss_threshold = state["min_loss_threshold"]
      self.clip_grad_norm = state["clip_grad_norm"]
      self.minibatch_size = state["minibatch_size"]
      self.polyak_coef = state["polyak_coef"]
      self.gamma = state["gamma"]
      self.policy.set_state(state["policy_state"])
      self.q_model.load_state_dict(state["q_model_state"])
      self.target_q_model.load_state_dict(state["target_q_model_state"])
  
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 8 for calling the FQETorchModel obj's underlying member method code with example usage: obj.set_state(state) without return types