code: def learn(self, agents):
      self.global_train_step += 1
  
      # only update parameter every 100 steps
      if self.global_train_step % 100 != 0:
          return 0.0
  
      if self.rpm.size() <= self.min_memory_size:
          return 0.0
  
      batch_obs_n = []
      batch_act_n = []
      batch_obs_new_n = []
  
      rpm_sample_index = self.rpm.make_index(self.batch_size)
      for i in range(self.n):
          batch_obs, batch_act, _, batch_obs_new, _ = agents[
              i
          ].rpm.sample_batch_by_index(rpm_sample_index)
          batch_obs_n.append(batch_obs)
          batch_act_n.append(batch_act)
          batch_obs_new_n.append(batch_obs_new)
      _, _, batch_rew, _, batch_isOver = self.rpm.sample_batch_by_index(
          rpm_sample_index
      )
  
      # compute target q
      target_q = 0.0
      target_act_next_n = []
      for i in range(self.n):
          feed = {"obs": batch_obs_new_n[i]}
          target_act_next = agents[i].fluid_executor.run(
              agents[i].next_a_program,
              feed=feed,
              fetch_list=[agents[i].next_action],
          )[0]
          target_act_next_n.append(target_act_next)
      feed_obs = {
          "obs" + str(i): batch_obs_new_n[i] for i in range(self.n)
      }
      feed_act = {
          "act" + str(i): target_act_next_n[i] for i in range(self.n)
      }
      feed = feed_obs.copy()
      feed.update(feed_act)  # merge two dict
      target_q_next = self.fluid_executor.run(
          self.next_q_program, feed=feed, fetch_list=[self.next_Q]
      )[0]
      target_q += (
          batch_rew
          + self.alg.gamma * (1.0 - batch_isOver) * target_q_next
      )
  
      feed_obs = {"obs" + str(i): batch_obs_n[i] for i in range(self.n)}
      feed_act = {"act" + str(i): batch_act_n[i] for i in range(self.n)}
      target_q = target_q.astype("float32")
      feed = feed_obs.copy()
      feed.update(feed_act)
      feed["target_q"] = target_q
      critic_cost = self.fluid_executor.run(
          self.learn_program, feed=feed, fetch_list=[self.critic_cost]
      )[0]
  
      self.alg.sync_target()
      return critic_cost
  
file_path: PARL/benchmark/fluid/MADDPG/simple_agent.py
imports: import numpy
  import parl
  from parl import layers
  from paddle import fluid
  from parl.utils import ReplayMemory
  from parl.utils import machine_info, get_gpu_count
label_desc: Use image node_id 4 for calling the MAAgent obj's underlying member method code with example usage: obj.learn(agents) and returns: critic_cost, int, int