def __init__(
self,
room=None, # Specify either room or room type
room_type='empty', # Choose from ['empty', 'wall', 'rooms']
potential_type="euclidean", # Choose from ['none' (no shaping) ,'shaped' (shortest distance between COMs), 'euclidean' (euclidean distance between states)]
base_reward='positive', # Choose from ['positive'(0,1), 'negative' (-1,0)]
shaped=False,
speed=1,
*args,
**kwargs):
Serializable.quick_init(self, locals())
self.use_images = False
room_defaults = dict(
empty=room_world.rooms.Room('pm', 1.2, 1.2),
wall=room_world.rooms.RoomWithWall('pm', 1.2, 1.2),
rooms=room_world.rooms.FourRoom('pm', 1.2, 1.2),
)
if room is None:
room = room_defaults[room_type]
super().__init__(room=room,
potential_type=potential_type,
shaped=shaped,
base_reward=base_reward,
*args,
**kwargs)
new_frame_skip = int(np.ceil(self.frame_skip * speed))
self.modifier = self.frame_skip * speed / new_frame_skip
self.frame_skip = new_frame_skip
def __init__(
self,
room=None, # Specify either room or room type
room_type='empty', # Choose from ['empty', 'wall', 'rooms']
potential_type="euclidean", # Choose from ['none' (no shaping) ,'shaped' (shortest distance between COMs), 'euclidean' (euclidean distance between states)]
shaped=False,
*args,
**kwargs):
Serializable.quick_init(self, locals())
self.use_images = False
room_defaults = dict(
empty=room_world.rooms.Room('pm', 1.2, 1.2),
wall=room_world.rooms.RoomWithWall('pm', 1.2, 1.2),
rooms=room_world.rooms.FourRoom('pm', 1.2, 1.2),
long=room_world.rooms.Room('pm', 0.3, 7.2),
)
if room is None:
room = room_defaults[room_type]
super().__init__(room=room,
potential_type=potential_type,
shaped=shaped,
base_reward='com',
*args,
**kwargs)
def __init__(
self,
env,
obs_keys=None,
goal_keys=None,
append_goal_to_obs=True,
):
Serializable.quick_init(self, locals())
self.env = env
if obs_keys is None:
obs_keys = ['observation']
if goal_keys is None:
goal_keys = ['desired_goal']
if append_goal_to_obs:
obs_keys += goal_keys
for k in obs_keys:
assert k in self.env.observation_space.spaces
assert isinstance(self.env.observation_space, Dict)
self.obs_keys = obs_keys
self.goal_keys = goal_keys
# TODO: handle nested dict
self.observation_space = Box(
np.hstack([
self.env.observation_space.spaces[k].low
for k in obs_keys
]),
np.hstack([
self.env.observation_space.spaces[k].high
for k in obs_keys
]),
)
self.action_space = self.env.action_space
def __init__(self, env, initial_configurations, domain='obj_pos'):
assert not env.random_init,\
"Please use this wrapper when setting environment's random_init as False!!"
Serializable.quick_init(self, locals())
super().__init__(env)
self._domain = domain
self._initial_configs = initial_configurations
self._current_idx = 0
self._n_initial_configs = len(self._initial_configs)
def __init__(self, **kwargs):
Serializable.quick_init(self, locals())
sawyer_reaching.SawyerReachXYZEnv.__init__(self, **kwargs)
self.observation_space = Dict([
('observation', self.observation_space),
('desired_goal', self.goal_space),
('achieved_goal', self.goal_space),
('state_observation', self.observation_space),
('state_desired_goal', self.goal_space),
('state_achieved_goal', self.goal_space),
])
def __init__(self, **kwargs):
Serializable.quick_init(self, locals())
super().__init__(self, **kwargs)
self.observation_space = Dict([
('observation', self.observation_space),
('desired_goal', self.goal_space),
('achieved_goal', self.goal_space),
('state_observation', self.observation_space),
('state_desired_goal', self.goal_space),
('state_achieved_goal', self.goal_space),
])
self.reset()
def __init__(self,
base='pm',
length=1.2,
width=1.2,
start=None,
target=None):
Serializable.quick_init(self, locals())
if start is None:
start = np.array((-length / 6, -width / 4))
if target is None:
target = np.array((-width / 6, width / 4))
super().__init__(base, length, width, start, target)
def __init__(self,
door_open_epsilon=2,
**kwargs
):
self.door_open_epsilon=door_open_epsilon
Serializable.quick_init(self, locals())
sawyer_door.SawyerDoorEnv.__init__(self, **kwargs)
self.observation_space = Dict([
('observation', self.observation_space),
('desired_goal', self.goal_space),
('achieved_goal', self.goal_space),
('state_observation', self.observation_space),
('state_desired_goal', self.goal_space),
('state_achieved_goal', self.goal_space),
])
self.set_mode('eval')
def __init__(
self,
env,
obs_means=None,
obs_stds=None,
obs_to_normalize_keys=['observation'],
):
# self._wrapped_env needs to be called first because
# Serializable.quick_init calls getattr, on this class. And the
# implementation of getattr (see below) calls self._wrapped_env.
# Without setting this first, the call to self._wrapped_env would call
# getattr again (since it's not set yet) and therefore loop forever.
# Or else serialization gets delegated to the wrapped_env. Serialize
# this env separately from the wrapped_env.
self._wrapped_env = env
Serializable.quick_init(self, locals())
ProxyEnv.__init__(self, env)
self._should_normalize = not (obs_means is None and obs_stds is None)
num_obs_types = len(obs_to_normalize_keys)
if self._should_normalize:
if obs_means is None:
obs_means = dict()
for key in self.obs_to_normalize_keys:
obs_means[key] = np.zeros_like(
env.observation_space[key].low)
else:
obs_means = dict()
for key in self.obs_to_normalize_keys:
obs_means[key] = np.array(obs_means[key])
if obs_stds is None:
obs_stds = dict()
for key in self.obs_to_normalize_keys:
obs_stds[key] = np.zeros_like(
env.observation_space[key].low)
else:
obs_stds = dict()
for key in self.obs_to_normalize_keys:
obs_stds[key] = np.array(obs_stds[key])
self._obs_means = obs_means
self._obs_stds = obs_stds
ub = np.ones(self._wrapped_env.action_space.shape)
self.action_space = Box(-1 * ub, ub)
self.obs_to_normalize_keys = obs_to_normalize_keys
def __init__(self,
# Room
room,
# Start and Goal
start_config="all",
goal_config="all",
# Reward
potential_type='',
shaped=False,
base_reward='',
# State and Goal Representations
use_state_images=False,
use_goal_images=False,
image_resolution=64,
# Time Limits
max_path_length=None,
*args, **kwargs
):
# Initialize
Serializable.quick_init(self, locals())
MultitaskEnv.__init__(self)
# Environment Configuration
self._room = room
model = self.room.get_mjcmodel()
self.possible_positions = self.room.XY(n=50)
with model.asfile() as f:
MujocoEnv.__init__(self, f.name, frame_skip=self.FRAME_SKIP)
# Initialization
self.start_config = start_config
self.baseline_start = self.room.get_start()
self.start = np.zeros_like(self.baseline_start)
self.goal_config = goal_config
self.baseline_goal = self.room.get_target()
self.goal = np.zeros_like(self.baseline_goal)
# Time Limit
self.curr_path_length = 0
if max_path_length is None:
self.max_path_length = self.MAX_PATH_LENGTH
else:
self.max_path_length = max_path_length
# Reward Functions
self.potential_type = potential_type
self.shaped = shaped
self.base_reward = base_reward
# Action Space
bounds = self.model.actuator_ctrlrange.copy()
self.action_space = Box(low=bounds[:, 0], high=bounds[:, 1])
self.use_state_images = use_state_images
self.use_goal_images = use_goal_images
self.image_resolution = image_resolution
# Observation Space
example_state_obs = self._get_env_obs()
if self.use_state_images:
example_obs = self.get_image(self.image_resolution, self.image_resolution, camera_name='topview')
else:
example_obs = example_state_obs
state_obs_shape = example_obs.shape
obs_shape = example_obs.shape
self.obs_space = Box(-1 * np.ones(obs_shape),np.ones(obs_shape))
self.state_obs_space = Box(-1 * np.ones(state_obs_shape), np.ones(state_obs_shape))
# Goal Space
example_state_goal = self._get_env_achieved_goal(example_state_obs)
if self.use_goal_images:
example_goal = self.get_image(self.image_resolution, self.image_resolution, camera_name='topview')
else:
example_goal = example_state_goal
state_goal_shape = example_state_goal.shape
goal_shape = example_goal.shape
self.goal_space = Box(-1 * np.ones(goal_shape), np.ones(goal_shape))
self.state_goal_space = Box(-1 * np.ones(state_goal_shape), np.ones(state_goal_shape))
# Final Setup
self.observation_space = Dict([
('observation', self.obs_space),
('desired_goal', self.goal_space),
('achieved_goal', self.goal_space),
('state_observation', self.state_obs_space),
('state_desired_goal', self.state_goal_space),
('state_achieved_goal', self.state_goal_space),
])
self.reset()
def __setstate__(self, d):
Serializable.__setstate__(self, d)
self._obs_means = d["_obs_means"]
self._obs_stds = d["_obs_stds"]
def __getstate__(self):
d = Serializable.__getstate__(self)
# Add these explicitly in case they were modified
d["_obs_means"] = self._obs_means
d["_obs_stds"] = self._obs_stds
return d
def __init__(self, env):
Serializable.quick_init(self, locals())
self.env = env
self._observation_space = convert_gym_space(env.observation_space)
self._action_space = convert_gym_space(env.action_space)
self._horizon = 500
