class_docstr: An implementation of the DQN agent.
class_docstr_tok: ["An","implementation","of","the","DQN","agent","."]
docstr: Creates the replay buffer used by the agent.
  
  Args:
    use_staging: bool, if True, uses a staging area to prefetch data for
      faster training.
  
  Returns:
    A WrapperReplayBuffer object.
docstr_tok: ["Creates","the","replay","buffer","used","by","the","agent",".","Args",":","use_staging",":","bool",",","if","True",",","uses","a","staging","area","to","prefetch","data","for","faster","training",".","Returns",":","A","WrapperReplayBuffer","object","."]
code: def _build_replay_buffer(self, use_staging):
      """Creates the replay buffer used by the agent.
  
      Args:
        use_staging: bool, if True, uses a staging area to prefetch data for
          faster training.
  
      Returns:
        A WrapperReplayBuffer object.
      """
      return circular_replay_buffer.WrappedReplayBuffer(
          observation_shape=self.observation_shape,
          stack_size=self.stack_size,
          use_staging=use_staging,
          update_horizon=self.update_horizon,
          gamma=self.gamma,
          observation_dtype=self.observation_dtype.as_numpy_dtype,
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import math
  import os
  import random
  from absl import logging
  from dopamine.discrete_domains import atari_lib
  from dopamine.replay_memory import circular_replay_buffer
  import gin.tf
  import numpy
  import tensorflow
label_desc: Use image node_id 4 for calling the DQNAgent obj's underlying member method code with example usage: obj._build_replay_buffer(use_staging) and returns: circular_replay_buffer