code: def _critic_learn(self, obs, action, reward, next_obs, terminal):
      next_action = self.target_model.policy(next_obs)
      next_Q = self.target_model.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.model.value(obs, action)
      cost = layers.square_error_cost(Q, target_Q)
      cost = layers.reduce_mean(cost)
      optimizer = fluid.optimizer.AdamOptimizer(self.critic_lr)
      optimizer.minimize(cost)
      return cost
  
file_path: PARL/parl/algorithms/fluid/ddpg.py
imports: import warnings
  from parl.core.fluid import layers
  from copy import deepcopy
  from paddle import fluid
  from parl.core.fluid.algorithm import Algorithm
label_desc: Use image node_id 5 for calling the DDPG obj's underlying member method code with example usage: obj._critic_learn(obs, action, reward, next_obs, terminal) and returns: cost