docstr: Args:
      obs_np: a numpy float32 array of shape ([B] + observation_space).
              Format of image input should be NCHW format.
      actions_np: a numpy int64 array of shape [B]
      behaviour_logits_np: a numpy float32 array of shape [B, act_dim]
      rewards_np: a numpy float32 array of shape [B]
      dones_np: a numpy bool array of shape [B]
      lr: float scalar of learning rate.
      entropy_coeff: float scalar of entropy coefficient.
code: def learn(
      self,
      obs_np,
      actions_np,
      behaviour_logits_np,
      rewards_np,
      dones_np,
      lr,
      entropy_coeff,
  ):
      """
      Args:
          obs_np: a numpy float32 array of shape ([B] + observation_space).
                  Format of image input should be NCHW format.
          actions_np: a numpy int64 array of shape [B]
          behaviour_logits_np: a numpy float32 array of shape [B, act_dim]
          rewards_np: a numpy float32 array of shape [B]
          dones_np: a numpy bool array of shape [B]
          lr: float scalar of learning rate.
          entropy_coeff: float scalar of entropy coefficient.
      """
  
      obs = paddle.to_tensor(obs_np, dtype="float32")
      actions = paddle.to_tensor(actions_np, dtype="int64")
      behaviour_logits = paddle.to_tensor(
          behaviour_logits_np, dtype="float32"
      )
      rewards = paddle.to_tensor(rewards_np, dtype="float32")
      dones = paddle.to_tensor(dones_np, dtype="bool")
  
      vtrace_loss, kl = self.alg.learn(
          obs,
          actions,
          behaviour_logits,
          rewards,
          dones,
          lr,
          entropy_coeff,
      )
  
      total_loss = vtrace_loss.total_loss.cpu().numpy()
      pi_loss = vtrace_loss.pi_loss.cpu().numpy()
      vf_loss = vtrace_loss.vf_loss.cpu().numpy()
      entropy = vtrace_loss.entropy.cpu().numpy()
      kl = kl.cpu().numpy()
  
      return total_loss, pi_loss, vf_loss, entropy, kl
  
file_path: PARL/examples/IMPALA/atari_agent.py
imports: import numpy
  import parl
  from parl.utils.scheduler import PiecewiseScheduler, LinearDecayScheduler
  import paddle
label_desc: Use image node_id 3 for calling the AtariAgent obj's underlying member method code with example usage: obj.learn(obs_np, actions_np, behaviour_logits_np, rewards_np, dones_np, lr, entropy_coeff) and returns: total_loss,  pi_loss,  vf_loss,  entropy,  kl