Python Wrapper.__init__方法代码示例

# 需要导入模块: from gym.core import Wrapper [as 别名]
# 或者: from gym.core.Wrapper import __init__ [as 别名]
def __init__(self, env, filename, allow_early_resets=False):
        Wrapper.__init__(self, env=env)
        self.tstart = time.time()
        if filename is None:
            self.f = None
            self.logger = None
        else:
            if not filename.endswith(Monitor.EXT):
                filename = filename + "." + Monitor.EXT
            self.f = open(filename, "wt")
            self.logger = JSONLogger(self.f)
            self.logger.writekvs({"t_start": self.tstart, "gym_version": gym.__version__,
                "env_id": env.spec.id if env.spec else 'Unknown'})
        self.allow_early_resets = allow_early_resets
        self.rewards = None
        self.needs_reset = True
        self.episode_rewards = []
        self.episode_lengths = []
        self.total_steps = 0
        self.current_metadata = {} # extra info that gets injected into each log entry
        # Useful for metalearning where we're modifying the environment externally
        # But want our logs to know about these modifications 

# 需要导入模块: from gym.core import Wrapper [as 别名]
# 或者: from gym.core.Wrapper import __init__ [as 别名]
def __init__(self, env, filename, allow_early_resets=False, reset_keywords=(), info_keywords=()):
        Wrapper.__init__(self, env=env)
        self.tstart = time.time()
        self.results_writer = ResultsWriter(
            filename,
            header={"t_start": time.time(), 'env_id' : env.spec and env.spec.id},
            extra_keys=reset_keywords + info_keywords
        )
        self.reset_keywords = reset_keywords
        self.info_keywords = info_keywords
        self.allow_early_resets = allow_early_resets
        self.rewards = None
        self.needs_reset = True
        self.episode_rewards = []
        self.episode_lengths = []
        self.episode_times = []
        self.total_steps = 0
        self.current_reset_info = {} # extra info about the current episode, that was passed in during reset() 

# 需要导入模块: from gym.core import Wrapper [as 别名]
# 或者: from gym.core.Wrapper import __init__ [as 别名]
def __init__(self, env, filename, allow_early_resets=False, reset_keywords=(), info_keywords=()):
        Wrapper.__init__(self, env=env)
        self.tstart = time.time()
        if filename:
            self.results_writer = ResultsWriter(filename,
                header={"t_start": time.time(), 'env_id' : env.spec and env.spec.id},
                extra_keys=reset_keywords + info_keywords
            )
        else:
            self.results_writer = None
        self.reset_keywords = reset_keywords
        self.info_keywords = info_keywords
        self.allow_early_resets = allow_early_resets
        self.rewards = None
        self.needs_reset = True
        self.episode_rewards = []
        self.episode_lengths = []
        self.episode_times = []
        self.total_steps = 0
        self.current_reset_info = {} # extra info about the current episode, that was passed in during reset() 

# 需要导入模块: from gym.core import Wrapper [as 别名]
# 或者: from gym.core.Wrapper import __init__ [as 别名]
def __init__(self, env):
        """Adds two qunatities to info returned by every step:

            num_steps: int
                Number of steps takes so far
            rewards: [float]
                All the cumulative rewards for the episodes completed so far.
        """
        super().__init__(env)
        # current episode state
        self._current_reward = None
        self._num_steps = None
        # temporary monitor state that we do not save
        self._time_offset = None
        self._total_steps = None
        # monitor state
        self._episode_rewards = []
        self._episode_lengths = []
        self._episode_end_times = [] 

# 需要导入模块: from gym.core import Wrapper [as 别名]
# 或者: from gym.core.Wrapper import __init__ [as 别名]
def __init__(self, env, filename, allow_early_resets=False, reset_keywords=(), info_keywords=()):
        Wrapper.__init__(self, env=env)
        self.tstart = time.time()
        if filename is None:
            self.f = None
            self.logger = None
        else:
            if not filename.endswith(Monitor.EXT):
                if osp.isdir(filename):
                    filename = osp.join(filename, Monitor.EXT)
                else:
                    filename = filename + "." + Monitor.EXT
            self.f = open(filename, "wt")
            self.f.write('#%s\n'%json.dumps({"t_start": self.tstart, 'env_id' : env.spec and env.spec.id}))
            self.logger = csv.DictWriter(self.f, fieldnames=('r', 'l', 't')+reset_keywords+info_keywords)
            self.logger.writeheader()
            self.f.flush()

        self.reset_keywords = reset_keywords
        self.info_keywords = info_keywords
        self.allow_early_resets = allow_early_resets
        self.rewards = None
        self.needs_reset = True
        self.episode_rewards = []
        self.episode_lengths = []
        self.episode_times = []
        self.total_steps = 0
        self.current_reset_info = {} # extra info about the current episode, that was passed in during reset() 

# 需要导入模块: from gym.core import Wrapper [as 别名]
# 或者: from gym.core.Wrapper import __init__ [as 别名]
def __init__(self, env, filename, allow_early_resets=False, reset_keywords=()):
        Wrapper.__init__(self, env=env)
        self.tstart = time.time()
        if filename is None:
            self.f = None
            self.logger = None
        else:
            if not filename.endswith(Monitor.EXT):
                if osp.isdir(filename):
                    filename = osp.join(filename, Monitor.EXT)
                else:
                    filename = filename + "." + Monitor.EXT
            self.f = open(filename, "wt")
            self.f.write('#%s\n'%json.dumps({"t_start": self.tstart, 'env_id' : env.spec and env.spec.id}))
            self.logger = csv.DictWriter(self.f, fieldnames=('r', 'l', 't')+reset_keywords)
            self.logger.writeheader()

        self.reset_keywords = reset_keywords
        self.allow_early_resets = allow_early_resets
        self.rewards = None
        self.needs_reset = True
        self.episode_rewards = []
        self.episode_lengths = []
        self.episode_times = []
        self.total_steps = 0
        self.current_reset_info = {} # extra info about the current episode, that was passed in during reset() 

# 需要导入模块: from gym.core import Wrapper [as 别名]
# 或者: from gym.core.Wrapper import __init__ [as 别名]
def __init__(self, env, filename, allow_early_resets=False, cpu=None):
        Wrapper.__init__(self, env=env)
        self.tstart = time.time()
        self.filename = filename
        if filename is None:
            self.f = None
            self.logger = None
        else:
            if not filename.endswith(Monitor.EXT):
                filename_local = filename + "." + Monitor.EXT_LOCAL
                filename = filename + "." + Monitor.EXT
            else:
                filename_local = filename[:-13] + "." + Monitor.EXT_LOCAL
            self.f = open(filename, "wt")
            self.f_local = open(filename_local, "wt")
            self.logger = JSONLogger(self.f, filename=filename)
            self.logger_local = JSONLogger(self.f_local, override=True)
            self.logger.writekvs({"t_start": self.tstart, "gym_version": gym.__version__,
                                  "env_id": env.spec.id if env.spec else 'Unknown'})
            self.logger_local.writekvs({"t_start": self.tstart, "gym_version": gym.__version__,
                                        "env_id": env.spec.id if env.spec else 'Unknown'})
        self.allow_early_resets = allow_early_resets
        self.rewards = None
        self.needs_reset = True
        self.episode_rewards = []
        self.episode_lengths = []
        self.total_steps = 0
        self.current_metadata = {}  # extra info that gets injected into each log entry
        # Useful for metalearning where we're modifying the environment externally
        # But want our logs to know about these modifications

        self.cpu = cpu 

# 需要导入模块: from gym.core import Wrapper [as 别名]
# 或者: from gym.core.Wrapper import __init__ [as 别名]
def __init__(self, env_fns):
        self.envs = [fn() for fn in env_fns]
        env = self.envs[0]
        self.action_space = env.action_space
        self.observation_space = env.observation_space
        self.ts = np.zeros(len(self.envs), dtype='int')