class_docstr: Implementation of QR-DQN (Quantile Regression Deep Q-Network)
  
  See https://arxiv.org/abs/1710.10044 for details
class_docstr_tok: ["Implementation","of","QR-DQN","(","Quantile","Regression","Deep","Q-Network",")","See","https",":","\/\/arxiv.org\/abs\/1710.10044","for","details"]
code: def configure_optimizers(self):
      optimizers = []
      target_params = list(self.q_network_target.parameters())
      source_params = list(self.q_network.parameters())
  
      optimizers.append(
          self.q_network_optimizer.make_optimizer_scheduler(
              self.q_network.parameters()
          )
      )
  
      if self.calc_cpe_in_training:
          (
              cpe_target_params,
              cpe_source_params,
              cpe_optimizers,
          ) = self._configure_cpe_optimizers()
          target_params += cpe_target_params
          source_params += cpe_source_params
          optimizers += cpe_optimizers
  
      optimizers.append(
          SoftUpdate.make_optimizer_scheduler(
              target_params, source_params, tau=self.tau
          )
      )
  
      return optimizers
  
file_path: ReAgent/reagent/training/qrdqn_trainer.py
imports: import logging
  from typing import List, Tuple
  import reagent.core.types
  import torch
  from reagent.core.configuration import resolve_defaults
  from reagent.core.dataclasses import field
  from reagent.core.parameters import EvaluationParameters, RLParameters
  from reagent.optimizer import SoftUpdate
  from reagent.optimizer.union import Optimizer__Union
  from reagent.training.dqn_trainer_base import DQNTrainerBaseLightning
label_desc: Use image node_id 2 for calling the QRDQNTrainer obj's underlying member method code with example usage: obj.configure_optimizers() and returns: optimizers