class_docstr: A3C: Asynchronous Advantage Actor-Critic.
  
  See http://arxiv.org/abs/1602.01783
  
  Args:
      model (A3CModel): Model to train
      optimizer (chainer.Optimizer): optimizer used to train the model
      t_max (int): The model is updated after every t_max local steps
      gamma (float): Discount factor [0,1]
      beta (float): Weight coefficient for the entropy regularizaiton term.
      process_idx (int): Index of the process.
      phi (callable): Feature extractor function
      pi_loss_coef (float): Weight coefficient for the loss of the policy
      v_loss_coef (float): Weight coefficient for the loss of the value
          function
      act_deterministically (bool): If set true, choose most probable actions
          in act method.
      batch_states (callable): method which makes a batch of observations.
          default is `chainerrl.misc.batch_states.batch_states`
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code: def shared_attributes(self):
      return ("shared_model", "optimizer")
  
imports: import copy
  from logging import getLogger
  import chainer
  from chainer import functions
  import numpy
  from chainerrl import agent
  from chainerrl.misc import async_
  from chainerrl.misc.batch_states import batch_states
  from chainerrl.misc import copy_param
  from chainerrl.recurrent import Recurrent
  from chainerrl.recurrent import RecurrentChainMixin
  from chainerrl.recurrent import state_kept
label_desc: Use image node_id 3 for calling the A3C obj's underlying member method code with example usage: obj.shared_attributes() and returns: str,  str