class_docstr: An implementation of the DQN agent.
class_docstr_tok: ["An","implementation","of","the","DQN","agent","."]
docstr: Stores an experienced transition.
  
  Executes a tf session and executes replay buffer ops in order to store the
  following tuple in the replay buffer:
    (last_observation, action, reward, is_terminal).
  
  Pedantically speaking, this does not actually store an entire transition
  since the next state is recorded on the following time step.
  
  Args:
    last_observation: numpy array, last observation.
    action: int, the action taken.
    reward: float, the reward.
    is_terminal: bool, indicating if the current state is a terminal state.
docstr_tok: ["Stores","an","experienced","transition",".","Executes","a","tf","session","and","executes","replay","buffer","ops","in","order","to","store","the","following","tuple","in","the","replay","buffer",":","(","last_observation",",","action",",","reward",",","is_terminal",")",".","Pedantically","speaking",",","this","does","not","actually","store","an","entire","transition","since","the","next","state","is","recorded","on","the","following","time","step",".","Args",":","last_observation",":","numpy","array",",","last","observation",".","action",":","int",",","the","action","taken",".","reward",":","float",",","the","reward",".","is_terminal",":","bool",",","indicating","if","the","current","state","is","a","terminal","state","."]
code: def _store_transition(
      self, last_observation, action, reward, is_terminal
  ):
      """Stores an experienced transition.
  
      Executes a tf session and executes replay buffer ops in order to store the
      following tuple in the replay buffer:
        (last_observation, action, reward, is_terminal).
  
      Pedantically speaking, this does not actually store an entire transition
      since the next state is recorded on the following time step.
  
      Args:
        last_observation: numpy array, last observation.
        action: int, the action taken.
        reward: float, the reward.
        is_terminal: bool, indicating if the current state is a terminal state.
      """
      self._replay.add(last_observation, action, reward, is_terminal)
  
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 14 for calling the DQNAgent obj's underlying member method code with example usage: obj._store_transition(last_observation, action, reward, is_terminal) without return types