original_docstring: Runs one iteration of agent/environment interaction.
  
      An iteration involves running several episodes until a certain number of
      steps are obtained. The interleaving of train/eval phases implemented here
      are to match the implementation of (Mnih et al., 2015).
  
      Args:
        iteration: int, current iteration number, used as a global_step for saving
          Tensorboard summaries.
  
      Returns:
        A dict containing summary statistics for this iteration.
      
docstring: Runs one iteration of agent/environment interaction.
  An iteration involves running several episodes until a certain number of
  steps are obtained. The interleaving of train/eval phases implemented here
  are to match the implementation of (Mnih et al., 2015).
docstring_tokens: Runs one iteration of agent / environment interaction . An iteration involves running several episodes until a certain number of steps are obtained . The interleaving of train / eval phases implemented here are to match the implementation of ( Mnih et al . 2015 ) .
code: def _run_one_iteration(self, iteration):
      """Runs one iteration of agent/environment interaction.
  
      An iteration involves running several episodes until a certain number of
      steps are obtained. The interleaving of train/eval phases implemented here
      are to match the implementation of (Mnih et al., 2015).
  
      Args:
        iteration: int, current iteration number, used as a global_step for saving
          Tensorboard summaries.
  
      Returns:
        A dict containing summary statistics for this iteration.
      """
      statistics = iteration_statistics.IterationStatistics()
      tf.logging.info('Starting iteration %d', iteration)
      num_episodes_train, average_reward_train = self._run_train_phase(
          statistics)
      num_episodes_eval, average_reward_eval = self._run_eval_phase(
          statistics)
  
      self._save_tensorboard_summaries(iteration, num_episodes_train,
                                       average_reward_train, num_episodes_eval,
                                       average_reward_eval)
      return statistics.data_lists
code_tokens: def _run_one_iteration ( self , iteration ) : statistics = iteration_statistics . IterationStatistics ( ) tf . logging . info ( 'Starting iteration %d' , iteration ) num_episodes_train , average_reward_train = self . _run_train_phase ( statistics ) num_episodes_eval , average_reward_eval = self . _run_eval_phase ( statistics ) self . _save_tensorboard_summaries ( iteration , num_episodes_train , average_reward_train , num_episodes_eval , average_reward_eval ) return statistics . data_lists
short_docstring: Runs one iteration of agent/environment interaction.
short_docstring_tokens: Runs one iteration of agent / environment interaction .
comment: """Runs one iteration of agent/environment interaction.
  
      An iteration involves running several episodes until a certain number of
      steps are obtained. The interleaving of train/eval phases implemented here
      are to match the implementation of (Mnih et al., 2015).
  
      Args:
        iteration: int, current iteration number, used as a global_step for saving
          Tensorboard summaries.
  
      Returns:
        A dict containing summary statistics for this iteration.
      """