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
docstr: Predict an action when given an observation, a greedy action will be returned.
  
  Args:
      obs (np.float32): shape of (3, 84, 84) or (1, 3, 84, 84), current observation
  
  Returns:
      act(int): action
code: def predict(self, obs):
      """Predict an action when given an observation, a greedy action will be returned.
  
      Args:
          obs (np.float32): shape of (3, 84, 84) or (1, 3, 84, 84), current observation
  
      Returns:
          act(int): action
      """
      if (
          obs.ndim == 3
      ):  # if obs is 3 dimensional, we need to expand it to have batch_size = 1
          obs = np.expand_dims(obs, axis=0)
  
      obs = paddle.to_tensor(obs, dtype="float32")
      pred_q = self.alg.predict(obs).detach().numpy().squeeze()
  
      best_actions = np.where(pred_q == pred_q.max())[0]
      act = np.random.choice(best_actions)
      return act
  
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 3 for calling the AtariAgent obj's underlying member method code with example usage: obj.predict(obs) and returns: act