code: def __init__(
      self,
      algorithm,
      agent_index=None,
      obs_dim_n=None,
      act_dim_n=None,
      batch_size=None,
  ):
      assert isinstance(agent_index, int)
      assert isinstance(obs_dim_n, list)
      assert isinstance(act_dim_n, list)
      assert isinstance(batch_size, int)
      self.agent_index = agent_index
      self.obs_dim_n = obs_dim_n
      self.act_dim_n = act_dim_n
      self.batch_size = batch_size
      self.n = len(act_dim_n)
  
      self.memory_size = int(1e5)
      self.min_memory_size = (
          batch_size * 25
      )  # batch_size * args.max_episode_len
      self.rpm = ReplayMemory(
          max_size=self.memory_size,
          obs_dim=self.obs_dim_n[agent_index],
          act_dim=self.act_dim_n[agent_index],
      )
      self.global_train_step = 0
  
      super(MAAgent, self).__init__(algorithm)
  
      # Attention: In the beginning, sync target model totally.
      self.alg.sync_target(decay=0)
  
file_path: PARL/examples/MADDPG/simple_agent.py
imports: import parl
  import paddle
  import numpy
  from parl.utils import ReplayMemory
label_desc: Use image node_id 1 to create a new MAAgent object from inherited base classes: parl with example: obj = MAAgent(algorithm, agent_index, obs_dim_n, act_dim_n, batch_size)