code: def _critic_learn(
      self, obs, action, reward, next_obs, terminal, critic_lr, model_id
  ):
      next_action = self.target_models[model_id].policy(next_obs)
      next_Q = self.target_models[model_id].value(next_obs, next_action)
  
      terminal = layers.cast(terminal, dtype="float32")
      target_Q = reward + (1.0 - terminal) * self.gamma * next_Q
      target_Q.stop_gradient = True
  
      Q = self.models[model_id].value(obs, action)
      cost = layers.square_error_cost(Q, target_Q)
      cost = layers.reduce_mean(cost)
      optimizer = fluid.optimizer.AdamOptimizer(critic_lr)
      optimizer.minimize(cost)
      return cost
  
file_path: PARL/examples/NeurIPS2018-AI-for-Prosthetics-Challenge/multi_head_ddpg.py
imports: import parl
  from parl import layers
  from copy import deepcopy
  from paddle import fluid
label_desc: Use image node_id 6 for calling the MultiHeadDDPG obj's underlying member method code with example usage: obj._critic_learn(obs, action, reward, next_obs, terminal, critic_lr, model_id) and returns: cost