code: def __init__(
      self,
      algorithm,
      obs_dim,
      act_dim,
      e_greed=0.1,
      e_greed_decrement=0,
  ):
      assert isinstance(obs_dim, int)
      assert isinstance(act_dim, int)
      self.obs_dim = obs_dim
      self.act_dim = act_dim
      super(Agent, self).__init__(algorithm)
  
      self.global_step = 0
      self.update_target_steps = (
          200  # 每隔200个training steps再把model的参数复制到target_model中
      )
  
      self.e_greed = e_greed  # 有一定概率随机选取动作，探索
      self.e_greed_decrement = e_greed_decrement
  
file_path: PARL/examples/tutorials/lesson3/dqn/agent.py
imports: import numpy
  import paddle.fluid
  import parl
  from parl import layers
label_desc: Use image node_id 1 to create a new Agent object from inherited base classes: parl with example: obj = Agent(algorithm, obs_dim, act_dim, e_greed, e_greed_decrement)