original_docstring: Runs one iteration of agent/environment interaction.
  
      An iteration involves running several episodes until a certain number of
      steps are obtained. This method differs from the `_run_one_iteration` method
      in the base `Runner` class in that it only runs the train phase.
  
      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. This method differs from the `_run_one_iteration` method
  in the base `Runner` class in that it only runs the train phase.
docstring_tokens: Runs one iteration of agent / environment interaction . An iteration involves running several episodes until a certain number of steps are obtained . This method differs from the ` _run_one_iteration ` method in the base ` Runner ` class in that it only runs the train phase .
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. This method differs from the `_run_one_iteration` method
      in the base `Runner` class in that it only runs the train phase.
  
      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()
      num_episodes_train, average_reward_train = self._run_train_phase(
          statistics)
  
      self._save_tensorboard_summaries(iteration, num_episodes_train,
                                       average_reward_train)
      return statistics.data_lists
code_tokens: def _run_one_iteration ( self , iteration ) : statistics = iteration_statistics . IterationStatistics ( ) num_episodes_train , average_reward_train = self . _run_train_phase ( statistics ) self . _save_tensorboard_summaries ( iteration , num_episodes_train , average_reward_train ) 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. This method differs from the `_run_one_iteration` method
      in the base `Runner` class in that it only runs the train phase.
  
      Args:
        iteration: int, current iteration number, used as a global_step for saving
          Tensorboard summaries.
  
      Returns:
        A dict containing summary statistics for this iteration.
      """