code: def train_value_network(self, dataset, epochs):
      loss = []
      batch = Batch(dataset)
      network_keys = self.ap.network_wrappers[
          "critic"
      ].input_embedders_parameters.keys()
  
      # * Found not to have any impact *
      # add a timestep to the observation
      # current_states_with_timestep = self.concat_state_and_timestep(dataset)
  
      mix_fraction = self.ap.algorithm.value_targets_mix_fraction
      total_returns = batch.n_step_discounted_rewards(True)
      for j in range(epochs):
          curr_batch_size = batch.size
          if (
              self.networks["critic"].online_network.optimizer_type
              != "LBFGS"
          ):
              curr_batch_size = self.ap.network_wrappers[
                  "critic"
              ].batch_size
          for i in range(batch.size // curr_batch_size):
              # split to batches for first order optimization techniques
              current_states_batch = {
                  k: v[i * curr_batch_size : (i + 1) * curr_batch_size]
                  for k, v in batch.states(network_keys).items()
              }
              total_return_batch = total_returns[
                  i * curr_batch_size : (i + 1) * curr_batch_size
              ]
              old_policy_values = force_list(
                  self.networks["critic"]
                  .target_network.predict(current_states_batch)
                  .squeeze()
              )
              if (
                  self.networks["critic"].online_network.optimizer_type
                  != "LBFGS"
              ):
                  targets = total_return_batch
              else:
                  current_values = self.networks[
                      "critic"
                  ].online_network.predict(current_states_batch)
                  targets = (
                      current_values * (1 - mix_fraction)
                      + total_return_batch * mix_fraction
                  )
  
              inputs = copy.copy(current_states_batch)
              for input_index, input in enumerate(old_policy_values):
                  name = "output_0_{}".format(input_index)
                  if (
                      name
                      in self.networks["critic"].online_network.inputs
                  ):
                      inputs[name] = input
  
              value_loss = self.networks[
                  "critic"
              ].online_network.accumulate_gradients(inputs, targets)
  
              self.networks[
                  "critic"
              ].apply_gradients_to_online_network()
              if isinstance(
                  self.ap.task_parameters, DistributedTaskParameters
              ):
                  self.networks[
                      "critic"
                  ].apply_gradients_to_global_network()
              self.networks[
                  "critic"
              ].online_network.reset_accumulated_gradients()
  
              loss.append([value_loss[0]])
      loss = np.mean(loss, 0)
      return loss
  
imports: import copy
  from collections import OrderedDict
  from typing import Union
  import numpy
  from rl_coach.agents.actor_critic_agent import ActorCriticAgent
  from rl_coach.agents.policy_optimization_agent import PolicyGradientRescaler
  from rl_coach.architectures.embedder_parameters import InputEmbedderParameters
  from rl_coach.architectures.head_parameters import PPOHeadParameters, VHeadParameters
  from rl_coach.architectures.middleware_parameters import FCMiddlewareParameters
  from rl_coach.base_parameters import AlgorithmParameters, NetworkParameters, AgentParameters, DistributedTaskParameters
  from rl_coach.core_types import EnvironmentSteps, Batch
  from rl_coach.exploration_policies.additive_noise import AdditiveNoiseParameters
  from rl_coach.exploration_policies.categorical import CategoricalParameters
  from rl_coach.logger import screen
  from rl_coach.memories.episodic.episodic_experience_replay import EpisodicExperienceReplayParameters
  from rl_coach.spaces import DiscreteActionSpace, BoxActionSpace
  from rl_coach.utils import force_list
label_desc: Use image node_id 4 for calling the PPOAgent obj's underlying member method code with example usage: obj.train_value_network(dataset, epochs) and returns: loss