def __init__(self,
domain_name,
task_name,
horizon,
gamma,
task_kwargs=None,
dt=.01,
width_screen=480,
height_screen=480,
camera_id=0):
"""
Constructor.
Args:
domain_name (str): name of the environment;
task_name (str): name of the task of the environment;
horizon (int): the horizon;
gamma (float): the discount factor;
task_kwargs (dict, None): parameters of the task;
dt (float, .01): duration of a control step;
width_screen (int, 480): width of the screen;
height_screen (int, 480): height of the screen;
camera_id (int, 0): position of camera to render the environment;
"""
# MDP creation
if task_kwargs is None:
task_kwargs = dict()
task_kwargs[
'time_limit'] = np.inf # Hack to ignore dm_control time limit.
self.env = suite.load(domain_name, task_name, task_kwargs=task_kwargs)
# MDP properties
action_space = self._convert_action_space(self.env.action_spec())
observation_space = self._convert_observation_space(
self.env.observation_spec())
mdp_info = MDPInfo(observation_space, action_space, gamma, horizon)
self._viewer = ImageViewer((width_screen, height_screen), dt)
self._camera_id = camera_id
super().__init__(mdp_info)
def __init__(self,
domain_name,
task_name,
horizon=None,
gamma=0.99,
task_kwargs=None,
dt=.01,
width_screen=480,
height_screen=480,
camera_id=0,
use_pixels=False,
pixels_width=64,
pixels_height=64):
"""
Constructor.
Args:
domain_name (str): name of the environment;
task_name (str): name of the task of the environment;
horizon (int): the horizon;
gamma (float): the discount factor;
task_kwargs (dict, None): parameters of the task;
dt (float, .01): duration of a control step;
width_screen (int, 480): width of the screen;
height_screen (int, 480): height of the screen;
camera_id (int, 0): position of camera to render the environment;
use_pixels (bool, False): if True, pixel observations are used
rather than the state vector;
pixels_width (int, 64): width of the pixel observation;
pixels_height (int, 64): height of the pixel observation;
"""
# MDP creation
self.env = suite.load(domain_name, task_name, task_kwargs=task_kwargs)
if use_pixels:
self.env = pixels.Wrapper(self.env,
render_kwargs={
'width': pixels_width,
'height': pixels_height
})
# get the default horizon
if horizon is None:
horizon = self.env._step_limit
# Hack to ignore dm_control time limit.
self.env._step_limit = np.inf
if use_pixels:
self._convert_observation_space = self._convert_observation_space_pixels
self._convert_observation = self._convert_observation_pixels
else:
self._convert_observation_space = self._convert_observation_space_vector
self._convert_observation = self._convert_observation_vector
# MDP properties
action_space = self._convert_action_space(self.env.action_spec())
observation_space = self._convert_observation_space(
self.env.observation_spec())
mdp_info = MDPInfo(observation_space, action_space, gamma, horizon)
self._viewer = ImageViewer((width_screen, height_screen), dt)
self._camera_id = camera_id
super().__init__(mdp_info)
self._state = None
def __init__(self,
config_file,
horizon=None,
gamma=0.99,
is_discrete=False,
width=None,
height=None,
debug_gui=False,
verbose=False):
"""
Constructor.
Args:
config_file (str): path to the YAML file specifying the task
(see igibson/examples/configs/ and igibson/test/);
horizon (int, None): the horizon;
gamma (float, 0.99): the discount factor;
is_discrete (bool, False): if True, actions are automatically
discretized by iGibson's `set_up_discrete_action_space`.
Please note that not all robots support discrete actions.
width (int, None): width of the pixel observation. If None, the
value specified in the config file is used;
height (int, None): height of the pixel observation. If None, the
value specified in the config file is used;
debug_gui (bool, False): if True, activate the iGibson in GUI mode,
showing the pybullet rendering and the robot camera.
verbose (bool, False): if False, it disable iGibson default messages.
"""
if not verbose:
logging.disable(logging.CRITICAL +
1) # Disable iGibson log messages
# MDP creation
self._not_pybullet = False
self._first = True
config = parse_config(config_file)
config['is_discrete'] = is_discrete
if horizon is not None:
config['max_step'] = horizon
else:
horizon = config['max_step']
config['max_step'] = horizon + 1 # Hack to ignore gym time limit
if width is not None:
config['image_width'] = width
if height is not None:
config['image_height'] = height
env = iGibsonEnv(config_file=config,
mode='gui' if debug_gui else 'headless')
env = iGibsonWrapper(env)
self.env = env
self._img_size = env.observation_space.shape[0:2]
# MDP properties
action_space = self.env.action_space
observation_space = Box(low=0.,
high=255.,
shape=(3, self._img_size[1],
self._img_size[0]))
mdp_info = MDPInfo(observation_space, action_space, gamma, horizon)
if isinstance(action_space, Discrete):
self._convert_action = lambda a: a[0]
else:
self._convert_action = lambda a: a
self._viewer = ImageViewer((self._img_size[1], self._img_size[0]),
1 / 60)
self._image = None
Environment.__init__(self, mdp_info)
def __init__(self,
wrapper,
config_file,
base_config_file=None,
horizon=None,
gamma=0.99,
width=None,
height=None):
"""
Constructor. For more details on how to pass YAML configuration files,
please see <MUSHROOM_RL PATH>/examples/habitat/README.md
Args:
wrapper (str): wrapper for converting observations and actions
(e.g., HabitatRearrangeWrapper);
config_file (str): path to the YAML file specifying the RL task
configuration (see <HABITAT_LAB PATH>/habitat_baselines/configs/);
base_config_file (str, None): path to an optional YAML file, used
as 'BASE_TASK_CONFIG_PATH' in the first YAML
(see <HABITAT_LAB PATH>/configs/);
horizon (int, None): the horizon;
gamma (float, 0.99): the discount factor;
width (int, None): width of the pixel observation. If None, the
value specified in the config file is used.
height (int, None): height of the pixel observation. If None, the
value specified in the config file is used.
"""
# MDP creation
self._not_pybullet = False
self._first = True
if base_config_file is None:
base_config_file = config_file
config = get_config(config_paths=config_file,
opts=['BASE_TASK_CONFIG_PATH', base_config_file])
config.defrost()
if horizon is None:
horizon = config.TASK_CONFIG.ENVIRONMENT.MAX_EPISODE_STEPS # Get the default horizon
config.TASK_CONFIG.ENVIRONMENT.MAX_EPISODE_STEPS = horizon + 1 # Hack to ignore gym time limit
# Overwrite all RGB width / height used for the TASK (not SIMULATOR)
for k in config['TASK_CONFIG']['SIMULATOR']:
if 'rgb' in k.lower():
if height is not None:
config['TASK_CONFIG']['SIMULATOR'][k]['HEIGHT'] = height
if width is not None:
config['TASK_CONFIG']['SIMULATOR'][k]['WIDTH'] = width
config.freeze()
env_class = get_env_class(config.ENV_NAME)
env = make_env_fn(env_class=env_class, config=config)
env = globals()[wrapper](env)
self.env = env
self._img_size = env.observation_space.shape[0:2]
# MDP properties
action_space = self.env.action_space
observation_space = Box(low=0.,
high=255.,
shape=(3, self._img_size[1],
self._img_size[0]))
mdp_info = MDPInfo(observation_space, action_space, gamma, horizon)
if isinstance(action_space, Discrete):
self._convert_action = lambda a: a[0]
else:
self._convert_action = lambda a: a
self._viewer = ImageViewer((self._img_size[1], self._img_size[0]),
1 / 10)
Environment.__init__(self, mdp_info)