class_docstr: Sample DQN agent to visualize Q-values and rewards.
class_docstr_tok: ["Sample","DQN","agent","to","visualize","Q-values","and","rewards","."]
code: def _select_action(self):
      action = super(MyDQNAgent, self)._select_action()
      q_vals = self._sess.run(
          self._net_outputs.q_values, {self.state_ph: self.state}
      )[0]
      for i in range(len(q_vals)):
          self.q_values[i].append(q_vals[i])
      return action
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import os
  from absl import logging
  from dopamine.agents.dqn import dqn_agent
  from dopamine.agents.rainbow import rainbow_agent
  from dopamine.discrete_domains import atari_lib
  from dopamine.discrete_domains import iteration_statistics
  from dopamine.discrete_domains import run_experiment
  from dopamine.utils import agent_visualizer
  from dopamine.utils import atari_plotter
  from dopamine.utils import bar_plotter
  from dopamine.utils import line_plotter
  import gin
  import numpy
  import tensorflow
  import tf_slim
label_desc: Use image node_id 3 for calling the MyDQNAgent obj's underlying member method code with example usage: obj._select_action() and returns: action