class_docstr: A DQN Agent.
  
  Implements the DQN algorithm from
  
  "Human level control through deep reinforcement learning"
    Mnih et al., 2015
    https://deepmind.com/research/dqn/
  
  This agent also implements n-step updates. See "Rainbow: Combining
  Improvements in Deep Reinforcement Learning" by Hessel et al., 2017, for a
  discussion on its benefits: https://arxiv.org/abs/1710.02298
class_docstr_tok: ["A","DQN","Agent",".","Implements","the","DQN","algorithm","from","''","Human","level","control","through","deep","reinforcement","learning","''","Mnih","et","al.",",","2015","https",":","\/\/deepmind.com\/research\/dqn\/","This","agent","also","implements","n-step","updates",".","See","``","Rainbow",":","Combining","Improvements","in","Deep","Reinforcement","Learning","''","by","Hessel","et","al.",",","2017",",","for","a","discussion","on","its","benefits",":","https",":","\/\/arxiv.org\/abs\/1710.02298"]
code: def _initialize(self):
      common.soft_variables_update(
          self._q_network.variables,
          self._target_q_network.variables,
          tau=1.0,
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import collections
  from typing import Optional, Text, cast
  import gin
  import tensorflow
  from tf_agents.agents import data_converter
  from tf_agents.agents import tf_agent
  from tf_agents.networks import network
  from tf_agents.networks import utils
  from tf_agents.policies import boltzmann_policy
  from tf_agents.policies import epsilon_greedy_policy
  from tf_agents.policies import greedy_policy
  from tf_agents.policies import q_policy
  from tf_agents.specs import tensor_spec
  from tf_agents.trajectories import time_step
  from tf_agents.typing import types
  from tf_agents.utils import common
  from tf_agents.utils import eager_utils
  from tf_agents.utils import nest_utils
label_desc: Use image node_id 5 for calling the DqnAgent obj's underlying member method code with example usage: obj._initialize() without return types