class_docstr: A3C: Asynchronous Advantage Actor-Critic.
  
  See http://arxiv.org/abs/1602.01783
  
  Args:
      model (A3CModel): Model to train
      optimizer (torch.optim.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.
      max_grad_norm (float or None): Maximum L2 norm of the gradient used for
          gradient clipping. If set to None, the gradient is not clipped.
      recurrent (bool): If set to True, `model` is assumed to implement
          `pfrl.nn.StatelessRecurrent`.
      batch_states (callable): method which makes a batch of observations.
          default is `pfrl.utils.batch_states.batch_states`
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code: def sync_parameters(self):
      copy_param.copy_param(
          target_link=self.model, source_link=self.shared_model
      )
  
imports: import copy
  from logging import getLogger
  import torch
  import torch.nn.functional
  import pfrl
  from pfrl import agent
  from pfrl.utils import clip_l2_grad_norm_, copy_param
  from pfrl.utils.batch_states import batch_states
  from pfrl.utils.mode_of_distribution import mode_of_distribution
  from pfrl.utils.recurrent import one_step_forward, pack_and_forward
label_desc: Use image node_id 2 for calling the A3C obj's underlying member method code with example usage: obj.sync_parameters() without return types