class_docstr: A JAX implementation of the DQN agent.
class_docstr_tok: ["A","JAX","implementation","of","the","DQN","agent","."]
docstr: Creates the replay buffer used by the agent.
docstr_tok: ["Creates","the","replay","buffer","used","by","the","agent","."]
code: def _build_replay_buffer(self):
      """Creates the replay buffer used by the agent."""
      return circular_replay_buffer.OutOfGraphReplayBuffer(
          observation_shape=self.observation_shape,
          stack_size=self.stack_size,
          update_horizon=self.update_horizon,
          gamma=self.gamma,
          observation_dtype=self.observation_dtype,
      )
  
imports: import collections
  import functools
  import inspect
  import math
  import time
  from absl import logging
  from dopamine.agents.dqn import dqn_agent
  from dopamine.jax import losses
  from dopamine.jax import networks
  from dopamine.metrics import statistics_instance
  from dopamine.replay_memory import circular_replay_buffer
  from dopamine.replay_memory import prioritized_replay_buffer
  import gin
  import jax
  import jax.numpy
  import numpy
  import optax
  import tensorflow
label_desc: Use image node_id 3 for calling the JaxDQNAgent obj's underlying member method code with example usage: obj._build_replay_buffer() and returns: circular_replay_buffer