docstr: Define the loss function and create an optimizer to minize the loss.
          
docstr_tok: ["Define","the","loss","function","and","create","an","optimizer","to","minize","the","loss","."]
code: def learn(self, obs, action, reward, next_obs, terminal):
      """Define the loss function and create an optimizer to minize the loss."""
      self._current_steps += 1
      critic_loss = self._critic_learn(
          obs, action, reward, next_obs, terminal
      )
      actor_loss = self._actor_learn(obs, action)
  
      self.sync_target()
      return critic_loss, actor_loss
  
file_path: PARL/parl/algorithms/torch/cql.py
imports: import parl
  import torch
  from torch.distributions import Normal
  import torch.nn.functional
  from parl.utils.utils import check_model_method
  from copy import deepcopy
  import numpy
label_desc: Use image node_id 4 for calling the CQL obj's underlying member method code with example usage: obj.learn(obs, action, reward, next_obs, terminal) and returns: critic_loss,  actor_loss