class_docstr: An implementation of the DQN agent.
class_docstr_tok: ["An","implementation","of","the","DQN","agent","."]
docstr: Signals the end of the episode to the agent.
  
  We store the observation of the current time step, which is the last
  observation of the episode.
  
  Args:
    reward: float, the last reward from the environment.
docstr_tok: ["Signals","the","end","of","the","episode","to","the","agent",".","We","store","the","observation","of","the","current","time","step",",","which","is","the","last","observation","of","the","episode",".","Args",":","reward",":","float",",","the","last","reward","from","the","environment","."]
code: def end_episode(self, reward):
      """Signals the end of the episode to the agent.
  
      We store the observation of the current time step, which is the last
      observation of the episode.
  
      Args:
        reward: float, the last reward from the environment.
      """
      if not self.eval_mode:
          self._store_transition(
              self._observation, self.action, reward, True
          )
  
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 10 for calling the DQNAgent obj's underlying member method code with example usage: obj.end_episode(reward) without return types