def __init__(self,
goal_reward=10,
actuation_cost_coeff=30,
distance_cost_coeff=1,
init_sigma=0.1):
super().__init__()
Serializable.quick_init(self, locals())
self.dynamics = PointDynamics(dim=2, sigma=0)
self.init_mu = np.zeros(2, dtype=np.float32)
self.init_sigma = init_sigma
self.goal_positions = np.array([[5, 0], [-5, 0], [0, 5], [0, -5]],
dtype=np.float32)
self.goal_threshold = 1.
self.goal_reward = goal_reward
self.action_cost_coeff = actuation_cost_coeff
self.distance_cost_coeff = distance_cost_coeff
self.xlim = (-7, 7)
self.ylim = (-7, 7)
self.vel_bound = 1.
self.reset()
self.observation = None
self._ax = None
self._env_lines = list()
self.fixed_plots = None
self.dynamic_plots = []
self.__spec = EnvSpec(action_space=self.action_space,
observation_space=self.observation_space)
def __init__(self,
reward_type='dense',
terminate_at_goal=True,
goal_reward_weight=3e-1,
goal_radius=0.25,
goal_distance=5,
goal_angle_range=(0, 2 * np.pi),
velocity_reward_weight=0,
ctrl_cost_coeff=1e-2,
contact_cost_coeff=1e-3,
survive_reward=5e-2,
fixed_goal_position=None,
*args,
**kwargs):
assert reward_type in REWARD_TYPES
self._reward_type = reward_type
self.terminate_at_goal = terminate_at_goal
self.goal_reward_weight = goal_reward_weight
self.goal_radius = goal_radius
self.goal_distance = goal_distance
self.goal_angle_range = goal_angle_range
self.velocity_reward_weight = velocity_reward_weight
self.ctrl_cost_coeff = ctrl_cost_coeff
self.contact_cost_coeff = contact_cost_coeff
self.survive_reward = survive_reward
MujocoEnv.__init__(self, *args, **kwargs)
Serializable.quick_init(self, locals())
def __init__(self):
self.exteroceptive_observation = [12.0, 0, 0.5]
self.hurdles_xpos = [-15., -13., -9., -5., -1., 3., 7., 11.,
15.] # ,19.,23.,27.]
path = os.path.join(MODELS_PATH, 'half_cheetah_hurdle.xml')
MujocoEnv.__init__(self, file_path=path)
# MujocoEnv.__init__(self)
Serializable.quick_init(self, locals())
def __init__(self, observation_space, action_space):
"""
:type observation_space: Space
:type action_space: Space
"""
Serializable.quick_init(self, locals())
self._observation_space = observation_space
self._action_space = action_space
def __init__(self, env, base_policy, num_skills, steps_per_option=100):
Serializable.quick_init(self, locals())
self._base_policy = base_policy
self._env = env
self._steps_per_option = steps_per_option
self._num_skills = num_skills
self.observation_space = self._env.observation_space
self.action_space = spaces.Discrete(num_skills)
self.spec = EnvSpec(self.observation_space, self.action_space)
self._obs = self.reset()
def __init__(self, env, num_skills, z):
Serializable.quick_init(self, locals())
self._env = env
self._num_skills = num_skills
self._z = z
obs_space = self._env.observation_space
low = np.hstack([obs_space.low, np.full(num_skills, 0)])
high = np.hstack([obs_space.high, np.full(num_skills, 1)])
self.observation_space = spaces.Box(low=low, high=high)
self.action_space = self._env.action_space
self.spec = EnvSpec(self.observation_space, self.action_space)
def __init__(self, goal=(-1, -1), arm_distance_coeff=0):
"""
goal (`list`): List of two elements denoting the x and y coordinates of
the goal location. Either of the coordinate can also be a string
'any' to make the reward not to depend on the corresponding
coordinate.
arm_distance_coeff ('float'): Coefficient for the arm-to-object distance
cost.
"""
super(PusherEnv, self).__init__(file_path=self.FILE_PATH)
Serializable.quick_init(self, locals())
self._goal_mask = [coordinate != 'any' for coordinate in goal]
self._goal = np.array(goal)[self._goal_mask].astype(np.float32)
self._arm_distance_coeff = 0.1# arm_distance_coeff
self._action_cost_coeff = 0.1
# Make the the complete robot visible when visualizing.
self.model.stat.extent = 10
def __init__(self, *args, **kwargs):
super(HalfCheetahEnv, self).__init__(*args, **kwargs)
Serializable.__init__(self, *args, **kwargs)
def __init__(self, low_level_policy, *args, **kwargs):
Serializable.quick_init(self, locals())
self._low_level_policy = low_level_policy
super().__init__(*args, **kwargs)
def __init__(self, env, delay=0.01):
Serializable.quick_init(self, locals())
ProxyEnv.__init__(self, env)
self._delay = delay