habitat.core.environments.RLTaskEnv class

Methods

def close(self) -> None

def current_episode(self, all_info: bool = False) -> dataset.BaseEpisode

Returns the current episode of the environment.

def get_done(self, observations)

def get_info(self, observations)

def get_reward(self, observations)

def get_reward_range(self)

def render(self, mode: str = 'rgb') -> numpy.ndarray

def reset(self, *args, return_info: bool = False, **kwargs) -> typing.Union[numpy.ndarray, typing.Dict[str, numpy.ndarray], typing.Tuple[typing.Union[numpy.ndarray, typing.Dict[str, numpy.ndarray]], typing.Dict]]

def seed(self, seed: typing.Optional[int] = None) -> None

def step(self, *args, **kwargs) -> typing.Tuple[typing.Union[numpy.ndarray, typing.Dict[str, numpy.ndarray]], float, bool, dict]

Special methods

def __enter__(self)

def __exit__(self, exc_type, exc_val, exc_tb)

def __init__(self, config: DictConfig, dataset: typing.Optional[dataset.Dataset] = None)

def __str__(self)

Properties

config: DictConfig get

episodes: typing.List[dataset.Episode] get set

habitat_env: env.Env get

np_random: gym.utils.seeding.RandomNumberGenerator get set

Initializes the np_random field if not done already.

unwrapped: gym.core.Env get

Completely unwrap this env.

Data

metadata = {'render_modes': []}

reward_range = (-inf, inf)

spec = None

Method documentation

Property documentation