def __init__(self,
task='hfield',
reset_every_episode=False,
reward=True,
max_timesteps=1000,
*args,
**kwargs):
Serializable.quick_init(self, locals())
self.cripple_mask = None
self.reset_every_episode = reset_every_episode
self.first = True
self.timesteps = 0
self.max_timesteps = max_timesteps
MujocoEnv.__init__(
self,
os.path.join(os.path.abspath(os.path.dirname(__file__)), "assets",
"half_cheetah_hfield.xml"))
task = None if task == 'None' else task
self._init_geom_rgba = self.model.geom_rgba.copy()
self._init_geom_contype = self.model.geom_contype.copy()
self._init_geom_size = self.model.geom_size.copy()
self._init_geom_pos = self.model.geom_pos.copy()
self.dt = self.model.opt.timestep
assert task in [None, 'hfield', 'hill', 'basin', 'steep', 'gentle']
self.task = task
self.x_walls = np.array([250, 260, 261, 270, 280, 285])
self.height_walls = np.array([0.2, 0.2, 0.2, 0.2, 0.2, 0.2])
self.height = 0.8
self.width = 15
def __init__(self,
task='force',
reset_every_episode=False,
fixed_goal=False):
Serializable.quick_init(self, locals())
self.reset_every_episode = reset_every_episode
self.first = True
self.fixed_goal = fixed_goal
MujocoEnv.__init__(
self,
os.path.join(os.path.abspath(os.path.dirname(__file__)), "assets",
"arm_7dof.xml"))
task = None if task == 'None' else task
self.cripple_mask = np.ones(self.action_space.shape)
self._init_geom_rgba = self.model.geom_rgba.copy()
self._init_geom_contype = self.model.geom_contype.copy()
self._init_geom_size = self.model.geom_size.copy()
self._init_body_pos = self.model.body_pos.copy()
self._init_body_masses = self.model.body_mass.copy()
self._init_geom_pos = self.model.geom_pos.copy()
self.dt = self.model.opt.timestep
assert task in [None, 'cripple', 'damping', 'mass', 'force']
self.task = task
def __init__(self,
task='damping',
reset_every_episode=False,
max_timesteps=1000):
Serializable.quick_init(self, locals())
self.reset_every_episode = reset_every_episode
self.first = True
self.timesteps = 0
self.max_timesteps = max_timesteps
MujocoEnv.__init__(
self,
os.path.join(os.path.abspath(os.path.dirname(__file__)), "assets",
"half_cheetah_blocks.xml"))
task = None if task == 'None' else task
self.cripple_mask = np.ones(self.action_space.shape)
self._init_geom_rgba = self.model.geom_rgba.copy()
self._init_geom_contype = self.model.geom_contype.copy()
self._init_geom_size = self.model.geom_size.copy()
self._init_geom_pos = self.model.geom_pos.copy()
self.dt = self.model.opt.timestep
assert task in [None, 'damping']
self.task = task
def reset(self, mode='train'):
self.prev_qpos = None
self.mode = mode
if mode == 'train':
self.reset_task(value=np.random.randint(1, self.action_dim - 1))
else:
self.reset_task(value=self.action_dim - 1)
return MujocoEnv.reset(self)
def __init__(self, task='cripple', reset_every_episode=False):
Serializable.quick_init(self, locals())
self.cripple_mask = None
self.first = True
self.task = 'cripple'
self.crippled_leg = 0
self.prev_torso = None
self.prev_qpos = None
self.first = True
MujocoEnv.__init__(
self,
os.path.join(os.path.abspath(os.path.dirname(__file__)), "assets",
"half_cheetah_disabled.xml"))
self._init_geom_rgba = self.model.geom_rgba.copy()
self._init_geom_contype = self.model.geom_contype.copy()
self._init_geom_size = self.model.geom_size.copy()
self._init_geom_pos = self.model.geom_pos.copy()
self.dt = self.model.opt.timestep
self.cripple_mask = np.ones(self.action_space.shape)
self.reward_range = (-np.inf, np.inf)
utils.EzPickle.__init__(self, locals())