我们从Python开源项目中，提取了以下11个代码示例，用于说明如何使用gym.monitoring()。
def test_only_complete_episodes_written(): with helpers.tempdir() as temp: env = gym.make('CartPole-v0') env.monitor.start(temp, video_callable=False) env.reset() d = False while not d: _, _, d, _ = env.step(env.action_space.sample()) env.reset() env.step(env.action_space.sample()) env.monitor.close() # Only 1 episode should be written results = monitoring.load_results(temp) assert len(results['episode_lengths']) == 1, "Found {} episodes written; expecting 1".format(len(results['episode_lengths']))
def __init__(self, env_name, record_video=True, video_schedule=None, log_dir=None, record_log=True): if log_dir is None: if logger.get_snapshot_dir() is None: logger.log("Warning: skipping Gym environment monitoring since snapshot_dir not configured.") else: log_dir = os.path.join(logger.get_snapshot_dir(), "gym_log") Serializable.quick_init(self, locals()) env = gym.envs.make(env_name) self.env = env self.env_id = env.spec.id monitor.logger.setLevel(logging.WARNING) assert not (not record_log and record_video) if log_dir is None or record_log is False: self.monitoring = False else: if not record_video: video_schedule = NoVideoSchedule() else: if video_schedule is None: video_schedule = CappedCubicVideoSchedule() self.env.monitor.start(log_dir, video_schedule, force=True) # add 'force=True' if want overwrite dirs self.monitoring = True self._observation_space = convert_gym_space(env.observation_space) self._action_space = convert_gym_space(env.action_space) self._horizon = env.spec.timestep_limit self._log_dir = log_dir
def terminate(self): if self.monitoring: self.env.monitor.close() if self._log_dir is not None: print(""" *************************** Training finished! You can upload results to OpenAI Gym by running the following command: python scripts/submit_gym.py %s *************************** """ % self._log_dir)
def __init__(self, env, type="origin"): self.env = env self.type = type self.video_schedule = None if not pms.record_movie: self.video_schedule = NoVideoSchedule() else: if self.video_schedule is not None: self.video_schedule = CappedCubicVideoSchedule() self.env.monitor.start("log/trpo" ,self.video_schedule, force=True) self.monitoring = True
def test_video_callable_false_does_not_record(): with helpers.tempdir() as temp: env = gym.make('CartPole-v0') env.monitor.start(temp, video_callable=False) env.reset() env.monitor.close() results = monitoring.load_results(temp) assert len(results['videos']) == 0
def test_video_callable_records_videos(): with helpers.tempdir() as temp: env = gym.make('CartPole-v0') env.monitor.start(temp) env.reset() env.monitor.close() results = monitoring.load_results(temp) assert len(results['videos']) == 1, "Videos: {}".format(results['videos'])
def __init__(self, env_name, record_video=True, video_schedule=None, log_dir=None, record_log=True, force_reset=False): if log_dir is None: if logger.get_snapshot_dir() is None: logger.log("Warning: skipping Gym environment monitoring since snapshot_dir not configured.") else: log_dir = os.path.join(logger.get_snapshot_dir(), "gym_log") Serializable.quick_init(self, locals()) env = gym.envs.make(env_name) self.env = env self.env_id = env.spec.id monitor_manager.logger.setLevel(logging.WARNING) assert not (not record_log and record_video) if log_dir is None or record_log is False: self.monitoring = False else: if not record_video: video_schedule = NoVideoSchedule() else: if video_schedule is None: video_schedule = CappedCubicVideoSchedule() self.env = gym.wrappers.Monitor(self.env, log_dir, video_callable=video_schedule, force=True) self.monitoring = True self._observation_space = convert_gym_space(env.observation_space) self._action_space = convert_gym_space(env.action_space) self._horizon = env.spec.timestep_limit self._log_dir = log_dir self._force_reset = force_reset
def reset(self, **kwargs): if self._force_reset and self.monitoring: recorder = self.env._monitor.stats_recorder if recorder is not None: recorder.done = True return self.env.reset()
def terminate(self): if self.monitoring: self.env._close() if self._log_dir is not None: print(""" *************************** Training finished! You can upload results to OpenAI Gym by running the following command: python scripts/submit_gym.py %s *************************** """ % self._log_dir)
def _start(self, directory, video_callable=None, force=False, resume=False, write_upon_reset=False, uid=None, mode=None): """Start monitoring. Args: directory (str): A per-training run directory where to record stats. video_callable (Optional[function, False]): function that takes in the index of the episode and outputs a boolean, indicating whether we should record a video on this episode. The default (for video_callable is None) is to take perfect cubes, capped at 1000. False disables video recording. force (bool): Clear out existing training data from this directory (by deleting every file prefixed with "openaigym."). resume (bool): Retain the training data already in this directory, which will be merged with our new data write_upon_reset (bool): Write the manifest file on each reset. (This is currently a JSON file, so writing it is somewhat expensive.) uid (Optional[str]): A unique id used as part of the suffix for the file. By default, uses os.getpid(). mode (['evaluation', 'training']): Whether this is an evaluation or training episode. """ if self.env.spec is None: logger.warning("Trying to monitor an environment which has no 'spec' set. This usually means you did not create it via 'gym.make', and is recommended only for advanced users.") env_id = '(unknown)' else: env_id = self.env.spec.id if not os.path.exists(directory): logger.info('Creating monitor directory %s', directory) if six.PY3: os.makedirs(directory, exist_ok=True) else: os.makedirs(directory) if video_callable is None: video_callable = capped_cubic_video_schedule elif video_callable == False: video_callable = disable_videos elif not callable(video_callable): raise error.Error('You must provide a function, None, or False for video_callable, not {}: {}'.format(type(video_callable), video_callable)) self.video_callable = video_callable # Check on whether we need to clear anything if force: clear_monitor_files(directory) elif not resume: training_manifests = detect_training_manifests(directory) if len(training_manifests) > 0: raise error.Error('''Trying to write to monitor directory {} with existing monitor files: {}. You should use a unique directory for each training run, or use 'force=True' to automatically clear previous monitor files.'''.format(directory, ', '.join(training_manifests[:5]))) self._monitor_id = monitor_closer.register(self) self.enabled = True self.directory = os.path.abspath(directory) # We use the 'openai-gym' prefix to determine if a file is # ours self.file_prefix = FILE_PREFIX self.file_infix = '{}.{}'.format(self._monitor_id, uid if uid else os.getpid()) self.stats_recorder = stats_recorder.StatsRecorder(directory, '{}.episode_batch.{}'.format(self.file_prefix, self.file_infix), autoreset=self.env_semantics_autoreset, env_id=env_id) if not os.path.exists(directory): os.mkdir(directory) self.write_upon_reset = write_upon_reset if mode is not None: self._set_mode(mode)
