class_docstr: Agent of Atari env.
  
  Args:
      algorithm (`parl.Algorithm`): algorithm to be used in this agent.
      act_dim (int): action space dimension
      start_lr (float): starting learning rate
      total_step (int): total learning rate and epsilon decay steps
      update_target_step (int): target network update frequency
code: def __init__(
      self, algorithm, act_dim, start_lr, total_step, update_target_step
  ):
      super().__init__(algorithm)
      self.global_update_step = 0
      self.update_target_step = update_target_step
      self.act_dim = act_dim
      self.curr_ep = 1
      self.ep_end = 0.1
      self.lr_end = 0.00001
  
      self.ep_scheduler = LinearDecayScheduler(1, total_step)
      self.lr_scheduler = LinearDecayScheduler(start_lr, total_step)
  
file_path: PARL/examples/DQN_variant/atari_agent.py
imports: import parl
  import numpy
  import paddle
  from parl.utils.scheduler import LinearDecayScheduler
label_desc: Use image node_id 1 to create a new AtariAgent object from inherited base classes: parl with example: obj = AtariAgent(algorithm, act_dim, start_lr, total_step, update_target_step)