Python logger.get_snapshot_dir方法代碼示例

# 需要導入模塊: from rllab.misc import logger [as 別名]
# 或者: from rllab.misc.logger import get_snapshot_dir [as 別名]
def logdir(algo=None, dirname=None):
    if dirname:
        rllablogger.set_snapshot_dir(dirname)
    dirname = rllablogger.get_snapshot_dir()
    rllablogger.add_tabular_output(os.path.join(dirname, 'progress.csv'))
    if algo:
        with open(os.path.join(dirname, 'params.json'), 'w') as f:
            params = extract_hyperparams(algo)
            json.dump(params, f)
    yield dirname
    rllablogger.remove_tabular_output(os.path.join(dirname, 'progress.csv')) 

# 需要導入模塊: from rllab.misc import logger [as 別名]
# 或者: from rllab.misc.logger import get_snapshot_dir [as 別名]
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

        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)
        logger.log("observation space: {}".format(self._observation_space))
        self._action_space = convert_gym_space(env.action_space)
        logger.log("action space: {}".format(self._action_space))
        self._horizon = env.spec.tags['wrapper_config.TimeLimit.max_episode_steps']
        self._log_dir = log_dir
        self._force_reset = force_reset 

# 需要導入模塊: from rllab.misc import logger [as 別名]
# 或者: from rllab.misc.logger import get_snapshot_dir [as 別名]
def rllab_logdir(algo=None, dirname=None):
    if dirname:
        rllablogger.set_snapshot_dir(dirname)
    dirname = rllablogger.get_snapshot_dir()
    rllablogger.add_tabular_output(os.path.join(dirname, 'progress.csv'))
    if algo:
        with open(os.path.join(dirname, 'params.json'), 'w') as f:
            params = extract_hyperparams(algo)
            json.dump(params, f)
    yield dirname
    rllablogger.remove_tabular_output(os.path.join(dirname, 'progress.csv')) 

# 需要導入模塊: from rllab.misc import logger [as 別名]
# 或者: from rllab.misc.logger import get_snapshot_dir [as 別名]
def __init__(self, path_dir=None):
        if path_dir is None:
            path_dir = get_snapshot_dir()
        self.path_dir = path_dir
        self.paths_reader = PathsReader(path_dir) 

# 需要導入模塊: from rllab.misc import logger [as 別名]
# 或者: from rllab.misc.logger import get_snapshot_dir [as 別名]
def __init__(self, env_name, record_video=False, video_schedule=None, log_dir=None, record_log=False,
                 force_reset=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)

        # HACK: Gets rid of the TimeLimit wrapper that sets 'done = True' when
        # the time limit specified for each environment has been passed and
        # therefore the environment is not Markovian (terminal condition depends
        # on time rather than state).
        env = env.env

        self.env = env
        self.env_id = env.spec.id

        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)
        logger.log("observation space: {}".format(self._observation_space))
        self._action_space = convert_gym_space(env.action_space)
        logger.log("action space: {}".format(self._action_space))
        self._horizon = env.spec.tags['wrapper_config.TimeLimit.max_episode_steps']
        self._log_dir = log_dir
        self._force_reset = force_reset 

# 需要導入模塊: from rllab.misc import logger [as 別名]
# 或者: from rllab.misc.logger import get_snapshot_dir [as 別名]
def __init__(self, env_name, wrappers=(), wrapper_args=(),
                 record_video=True, video_schedule=None, log_dir=None, record_log=True,
                 post_create_env_seed=None,
                 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)
        if post_create_env_seed is not None:
            env.set_env_seed(post_create_env_seed)
        for i, wrapper in enumerate(wrappers):
            if wrapper_args and len(wrapper_args) == len(wrappers):
                env = wrapper(env, **wrapper_args[i])
            else:
                env = wrapper(env)
        self.env = env
        self.env_id = env.spec.id

        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)
        logger.log("observation space: {}".format(self._observation_space))
        self._action_space = convert_gym_space(env.action_space)
        logger.log("action space: {}".format(self._action_space))
        self._horizon = env.spec.tags.get('wrapper_config.TimeLimit.max_episode_steps')
        self._log_dir = log_dir
        self._force_reset = force_reset 

# 需要導入模塊: from rllab.misc import logger [as 別名]
# 或者: from rllab.misc.logger import get_snapshot_dir [as 別名]
def setup(self, env, policy, start_itr):

        if not self.args.algo == 'thddpg':
            # Baseline
            if self.args.baseline_type == 'linear':
                baseline = LinearFeatureBaseline(env_spec=env.spec)
            elif self.args.baseline_type == 'zero':
                baseline = ZeroBaseline(env_spec=env.spec)
            else:
                raise NotImplementedError(self.args.baseline_type)

            if self.args.control == 'concurrent':
                baseline = [baseline for _ in range(len(env.agents))]
        # Logger
        default_log_dir = config.LOG_DIR
        if self.args.log_dir is None:
            log_dir = osp.join(default_log_dir, self.args.exp_name)
        else:
            log_dir = self.args.log_dir

        tabular_log_file = osp.join(log_dir, self.args.tabular_log_file)
        text_log_file = osp.join(log_dir, self.args.text_log_file)
        params_log_file = osp.join(log_dir, self.args.params_log_file)

        logger.log_parameters_lite(params_log_file, self.args)
        logger.add_text_output(text_log_file)
        logger.add_tabular_output(tabular_log_file)
        prev_snapshot_dir = logger.get_snapshot_dir()
        prev_mode = logger.get_snapshot_mode()
        logger.set_snapshot_dir(log_dir)
        logger.set_snapshot_mode(self.args.snapshot_mode)
        logger.set_log_tabular_only(self.args.log_tabular_only)
        logger.push_prefix("[%s] " % self.args.exp_name)

        if self.args.algo == 'tftrpo':
            algo = MATRPO(env=env, policy_or_policies=policy, baseline_or_baselines=baseline,
                          batch_size=self.args.batch_size, start_itr=start_itr,
                          max_path_length=self.args.max_path_length, n_itr=self.args.n_iter,
                          discount=self.args.discount, gae_lambda=self.args.gae_lambda,
                          step_size=self.args.step_size, optimizer=ConjugateGradientOptimizer(
                              hvp_approach=FiniteDifferenceHvp(base_eps=1e-5)) if
                          self.args.recurrent else None, ma_mode=self.args.control)
        elif self.args.algo == 'thddpg':
            qfunc = thContinuousMLPQFunction(env_spec=env.spec)
            if self.args.exp_strategy == 'ou':
                es = OUStrategy(env_spec=env.spec)
            elif self.args.exp_strategy == 'gauss':
                es = GaussianStrategy(env_spec=env.spec)
            else:
                raise NotImplementedError()

            algo = thDDPG(env=env, policy=policy, qf=qfunc, es=es, batch_size=self.args.batch_size,
                          max_path_length=self.args.max_path_length,
                          epoch_length=self.args.epoch_length,
                          min_pool_size=self.args.min_pool_size,
                          replay_pool_size=self.args.replay_pool_size, n_epochs=self.args.n_iter,
                          discount=self.args.discount, scale_reward=0.01,
                          qf_learning_rate=self.args.qfunc_lr,
                          policy_learning_rate=self.args.policy_lr,
                          eval_samples=self.args.eval_samples, mode=self.args.control)
        return algo