docstr: Train both a reward model to be used by the doubly-robust estimator, and some model to be used for BCQ
  
  :param epochs: The total number of epochs to use for training a reward model
  :return: None
docstr_tok: ["Train","both","a","reward","model","to","be","used","by","the","doubly-robust","estimator",",","and","some","model","to","be","used","for","BCQ",":","param","epochs",":","The","total","number","of","epochs","to","use","for","training","a","reward","model",":","return",":","None"]
code: def improve_reward_model(self, epochs: int):
      """
      Train both a reward model to be used by the doubly-robust estimator, and some model to be used for BCQ
  
      :param epochs: The total number of epochs to use for training a reward model
      :return: None
      """
  
      # we'll be assuming that these gets drawn from the reward model parameters
      batch_size = self.ap.network_wrappers["reward_model"].batch_size
      network_keys = self.ap.network_wrappers[
          "reward_model"
      ].input_embedders_parameters.keys()
  
      # if using a NN to decide which actions to drop, we'll train the NN here
      if isinstance(
          self.ap.algorithm.action_drop_method_parameters,
          NNImitationModelParameters,
      ):
          total_epochs = max(
              epochs,
              self.ap.algorithm.action_drop_method_parameters.imitation_model_num_epochs,
          )
      else:
          total_epochs = epochs
  
      for epoch in range(total_epochs):
          # this is fitted from the training dataset
          reward_model_loss = 0
          imitation_model_loss = 0
          total_transitions_processed = 0
          for i, batch in enumerate(
              self.call_memory(
                  "get_shuffled_training_data_generator", batch_size
              )
          ):
              batch = Batch(batch)
  
              # reward model
              if epoch < epochs:
                  reward_model_loss += self.get_reward_model_loss(batch)
  
              # imitation model
              if (
                  isinstance(
                      self.ap.algorithm.action_drop_method_parameters,
                      NNImitationModelParameters,
                  )
                  and epoch
                  < self.ap.algorithm.action_drop_method_parameters.imitation_model_num_epochs
              ):
                  target_actions = np.zeros(
                      (batch.size, len(self.spaces.action.actions))
                  )
                  target_actions[range(batch.size), batch.actions()] = 1
                  imitation_model_loss += self.networks[
                      "imitation_model"
                  ].train_and_sync_networks(
                      batch.states(network_keys), target_actions
                  )[
                      0
                  ]
  
              total_transitions_processed += batch.size
  
          log = OrderedDict()
          log["Epoch"] = epoch
  
          if reward_model_loss:
              log["Reward Model Loss"] = (
                  reward_model_loss / total_transitions_processed
              )
          if imitation_model_loss:
              log["Imitation Model Loss"] = (
                  imitation_model_loss / total_transitions_processed
              )
  
          screen.log_dict(log, prefix="Training Batch RL Models")
  
      # if using a kNN based model, we'll initialize and build it here.
      # initialization cannot be moved to the constructor as we don't have the agent's spaces initialized yet.
      if isinstance(
          self.ap.algorithm.action_drop_method_parameters, KNNParameters
      ):
          knn_size = (
              self.ap.algorithm.action_drop_method_parameters.knn_size
          )
          if (
              self.ap.algorithm.action_drop_method_parameters.use_state_embedding_instead_of_state
          ):
              self.knn_trees = [
                  AnnoyDictionary(
                      dict_size=knn_size,
                      key_width=int(
                          self.networks["reward_model"]
                          .online_network.state_embedding[0]
                          .shape[-1]
                      ),
                      batch_size=knn_size,
                  )
                  for _ in range(len(self.spaces.action.actions))
              ]
          else:
              self.knn_trees = [
                  AnnoyDictionary(
                      dict_size=knn_size,
                      key_width=self.spaces.state["observation"].shape[
                          0
                      ],
                      batch_size=knn_size,
                  )
                  for _ in range(len(self.spaces.action.actions))
              ]
  
          for i, knn_tree in enumerate(self.knn_trees):
              state_embeddings = self.embedding(
                  [
                      transition.state
                      for transition in self.memory.transitions
                      if transition.action == i
                  ]
              )
              knn_tree.add(
                  keys=state_embeddings,
                  values=np.expand_dims(
                      np.zeros(state_embeddings.shape[0]), axis=1
                  ),
              )
  
          for knn_tree in self.knn_trees:
              knn_tree._rebuild_index()
  
          self.average_dist = [
              [
                  dist[0]
                  for dist in knn_tree._get_k_nearest_neighbors_indices(
                      keys=self.embedding(
                          [
                              transition.state
                              for transition in self.memory.transitions
                          ]
                      ),
                      k=1,
                  )[0]
              ]
              for knn_tree in self.knn_trees
          ]
          self.average_dist = sum(
              [x for l in self.average_dist for x in l]
          )  # flatten and sum
          self.average_dist /= len(self.memory.transitions)
  
imports: from collections import OrderedDict
  from copy import deepcopy
  from typing import Union, List, Dict
  import numpy
  from rl_coach.agents.dqn_agent import DQNAgentParameters, DQNAlgorithmParameters, DQNAgent
  from rl_coach.base_parameters import Parameters
  from rl_coach.core_types import EnvironmentSteps, Batch, StateType
  from rl_coach.graph_managers.batch_rl_graph_manager import BatchRLGraphManager
  from rl_coach.logger import screen
  from rl_coach.memories.non_episodic.differentiable_neural_dictionary import AnnoyDictionary
  from rl_coach.schedules import LinearSchedule
label_desc: Use image node_id 3 for calling the DDQNBCQAgent obj's underlying member method code with example usage: obj.improve_reward_model(epochs) without return types