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 _compute_q_values(self, time_steps, actions, training=False):
      network_observation = time_steps.observation
  
      if self._observation_and_action_constraint_splitter is not None:
          (
              network_observation,
              _,
          ) = self._observation_and_action_constraint_splitter(
              network_observation
          )
  
      q_values, _ = self._q_network(
          network_observation,
          step_type=time_steps.step_type,
          training=training,
      )
      # Handle action_spec.shape=(), and shape=(1,) by using the multi_dim_actions
      # param. Note: assumes len(tf.nest.flatten(action_spec)) == 1.
      action_spec = cast(
          tensor_spec.BoundedTensorSpec, self._action_spec
      )
      multi_dim_actions = action_spec.shape.rank > 0
      # support for dueling networks
      a_values = (
          q_values[0] if isinstance(q_values, tuple) else q_values
      )
      return common.index_with_actions(
          a_values,
          tf.cast(actions, dtype=tf.int32),
          multi_dim_actions=multi_dim_actions,
      )
  
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 10 for calling the DqnAgent obj's underlying member method code with example usage: obj._compute_q_values(time_steps, actions, training) and returns: common