Method run

acme/environment_loop.py:144–180

Perform the run loop. Run the environment loop either for `num_episodes` episodes or for at least `num_steps` steps (the last episode is always run until completion, so the total number of steps may be slightly more than `num_steps`). At least one of these two arguments has to be No

(self,
          num_episodes: Optional[int] = None,
          num_steps: Optional[int] = None)

    return result

  def run(self,
          num_episodes: Optional[int] = None,
          num_steps: Optional[int] = None):
    """Perform the run loop.

    Run the environment loop either for `num_episodes` episodes or for at
    least `num_steps` steps (the last episode is always run until completion,
    so the total number of steps may be slightly more than `num_steps`).
    At least one of these two arguments has to be None.

    Upon termination of an episode a new episode will be started. If the number
    of episodes and the number of steps are not given then this will interact
    with the environment infinitely.

    Args:
      num_episodes: number of episodes to run the loop for.
      num_steps: minimal number of steps to run the loop for.

    Raises:
      ValueError: If both 'num_episodes' and 'num_steps' are not None.
    """

    if not (num_episodes is None or num_steps is None):
      raise ValueError('Either "num_episodes" or "num_steps" should be None.')

    def should_terminate(episode_count: int, step_count: int) -> bool:
      return ((num_episodes is not None and episode_count >= num_episodes) or
              (num_steps is not None and step_count >= num_steps))

    episode_count, step_count = 0, 0
    with signals.runtime_terminator():
      while not should_terminate(episode_count, step_count):
        result = self.run_episode()
        episode_count += 1
        step_count += result['episode_length']
        # Log the given episode results.
        self._logger.write(result)

# Placeholder for an EnvironmentLoop alias