def __init__(self, *args, **kwargs):
xml_path = os.path.split(
os.path.realpath(__file__))[0] + '/models/silvia' + str(
kwargs['legs']) + '.xml'
MujocoEnv.__init__(self, xml_path, 5)
utils.EzPickle.__init__(self)
Serializable.__init__(self, *args, **kwargs)
def __init__(self,
goal=(0, -1),
arm_object_distance_cost_coeff=0,
goal_object_distance_cost_coeff=1.0,
ctrl_cost_coeff=0.1):
"""
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.
goal_distance_coeff ('float'): Coefficient for the object-to-goal
distance cost.
"""
utils.EzPickle.__init__(**locals())
self._goal_mask = [coordinate != 'any' for coordinate in goal]
self._goal = np.array(goal)[self._goal_mask].astype(np.float32)
self._arm_object_distance_cost_coeff = arm_object_distance_cost_coeff
self._goal_object_distance_cost_coeff = goal_object_distance_cost_coeff
self._ctrl_cost_coeff = ctrl_cost_coeff
MujocoEnv.__init__(self, model_path=self.MODEL_PATH, frame_skip=5)
self.model.stat.extent = 10
def __init__(self, **kwargs):
self.MAPPING = {0: [0, 1], 1: [2, 3]}
self.REST_POSE = np.array([0.0, 0.0, 0.0,
0.0]) #NEED TO DEFINE WITH ALEXIE
self.LEGS_UP = np.array([0.0, 0.0, 0.0,
0.0]) #NEED TO DEFINE WITH ALEXIE
self.COXA_LIMIT = (-1 / 3, 1 / 3)
self.KNEE_LIMIT = (-1 / 2, 1 / 2)
self.COXA_INDEX = [0, 2]
self.KNEE_INDEX = [1, 3]
self.legs = kwargs['legs']
self.act_dim = self.legs * 2
self.leg_indices = kwargs['leg_indices']
self.act_index, self.other_index = self.get_index()
self.obs_dim = kwargs['observation_dim']
self.input_dim = kwargs['policy_output_dim']
self.move = self.REST_POSE.copy()[0:2]
self.pos = self.REST_POSE.copy()
xml_path = os.path.dirname(
os.path.abspath(__file__)
) + '/envs/model/xmlfilename.xml' #NEED TO MODIFY XML FILE NAME
MujocoEnv.__init__(self, xml_path, 5) #frame skip : 5, Initialize self
utils.EzPickle.__init__(self)
def __init__(self: MujocoEnv, xml_path, ref_trajecs: RefTrajecs):
'''@param: self: gym environment class extending the MimicEnv class
@param: xml_path: path to the mujoco environment XML file
@param: ref_trajecs: Instance of the ReferenceTrajectory'''
self.refs = ref_trajecs
# set simulation and control frequency
self._sim_freq, self._frame_skip = self.get_sim_freq_and_frameskip()
# keep the body in the air for testing purposes
self._FLY = False or cfg.is_mod(cfg.MOD_FLY)
# when we evaluate a model during or after the training,
# we might want to weaken ET conditions or monitor and plot data
self._EVAL_MODEL = False
# control desired walking speed
self.FOLLOW_DESIRED_SPEED_PROFILE = False
# track individual reward components
self.pos_rew, self.vel_rew, self.com_rew = 0, 0, 0
self.mean_epret_smoothed = 0
# track running mean of the return and use it for ET reward
self.ep_rews = []
# initialize Mujoco Environment
MujocoEnv.__init__(self, xml_path, self._frame_skip)
# init EzPickle (think it is required to be able to save and load models)
gym.utils.EzPickle.__init__(self)
# make sure simulation and control run at the desired frequency
self.model.opt.timestep = 1 / self._sim_freq
self.control_freq = self._sim_freq / self._frame_skip
# sync reference data with the control frequency
self.refs.set_sampling_frequency(self.control_freq)
# The motor torque ranges should always be specified in the config file
# and overwrite the forcerange in the .MJCF file
self.model.actuator_forcerange[:, :] = cfg.TORQUE_RANGES
def __init__(self, require_shadow=False):
xml_path = 'half_cheetah(no_shadow).xml'
if require_shadow:
xml_path = "half_cheetah.xml"
xml_path = os.path.join(os.path.dirname(__file__),
"modified_mujoco_assets", xml_path)
MujocoEnv.__init__(self, xml_path, 1)
utils.EzPickle.__init__(self)
def __init__(self, goal_velocity=None):
# self.set_task(self.sample_tasks(1)[0])
if goal_velocity:
self.goal_velocity = goal_velocity
else:
self.set_task(self.sample_tasks(1)[0]) #1.0
MujocoEnv.__init__(self, 'half_cheetah.xml', 5)
gym.utils.EzPickle.__init__(self)
def __init__(self, random_reset=True, ptsize=2):
utils.EzPickle.__init__(self)
MujocoEnv.__init__(
self,
os.path.join(os.path.abspath(os.path.dirname(__file__)), 'assets',
'point_pos_distractor.xml'), 2)
self.random_reset = random_reset
def __init__(self):
self.goal = np.ones((3,))
MujocoEnv.__init__(self, os.path.join(os.path.dirname(__file__), "assets", "pr2.xml"), 2)
gym.utils.EzPickle.__init__(self)
context = zmq.Context()
self.socket = context.socket(zmq.REQ)
self.socket.connect("tcp://localhost:5555")
# Socket to talk to server
print("Connecting to the client…")
self.start()
def __init__(self):
self.viewer = None
self.work_path = Context.work_path
self.world = Context.world
self.step_num = 0
self.episode_num = 0
self.model_path = self._compile_model()
MujocoEnv.__init__(self,
self.model_path,
frame_skip=Context.config['env.frame_skip'])
def __init__(self):
self.mode_locations = np.array([[0.225, 0.225]], dtype=np.float32)
self.noise_scale = 0.10
self.max_norm = 0.30
self.set_task(self.sample_tasks(1)[0])
asset_path = os.path.join(
os.getcwd(),
'meta_policy_search/envs/mujoco_envs/assets/reacher_3link.xml')
MujocoEnv.__init__(self, asset_path, 2)
utils.EzPickle.__init__(self)
def __init__(self):
np.random.seed(1337)
self.NUM_TRAIN = 100
self.NUM_EVAL = 30
self.NUM_TASKS = self.NUM_TRAIN + self.NUM_EVAL
self._tasks = np.random.uniform(0.0, 3.0, (self.NUM_TASKS, ))
self.set_task(self._tasks[0])
MujocoEnv.__init__(self, 'half_cheetah.xml', 5)
gym.utils.EzPickle.__init__(self)
def __init__(self):
np.random.seed(1337)
self.NUM_TRAIN = 150
self.NUM_EVAL = 30
self.NUM_TASKS = self.NUM_TRAIN + self.NUM_EVAL
a = np.random.random(self.NUM_TASKS) * 2 * np.pi
r = 3 * np.random.random(self.NUM_TASKS)**0.5
self._tasks = np.stack((r * np.cos(a), r * np.sin(a)), axis=-1)
self.set_task(self._tasks[0])
MujocoEnv.__init__(self, 'ant.xml', 5)
gym.utils.EzPickle.__init__(self)
def __init__(self):
np.random.seed(1337)
self.NUM_TRAIN = 100
self.NUM_EVAL = 30
self.NUM_TASKS = self.NUM_TRAIN + self.NUM_EVAL
self._tasks = np.random.uniform(0.,
2.0 * np.pi,
size=(self.NUM_TASKS, ))
self.set_task(self._tasks[0])
MujocoEnv.__init__(self, 'humanoid.xml', 5)
gym.utils.EzPickle.__init__(self)
def __init__(self, env_type='default', reward_type='dense'):
self.goal = np.array([1.0, 1.0])
self._use_simulator = True
self._env_type = env_type
self._reward_type = reward_type
if env_type == 'default':
model_path = self.MODEL_PATH_DEFAULT
elif env_type == 'y':
model_path = self.MODEL_PATH_Y
elif env_type == 't':
model_path = self.MODEL_PATH_T
elif env_type == 'skewed_square':
model_path = self.MODEL_PATH_DEFAULT
MujocoEnv.__init__(self, model_path=model_path, frame_skip=5)
def __init__(self, action_repeat=1):
"""
Constructor for :class:`SpiderEnv`.
:param action_repeat: Number of times action should be repeated in MuJoCo
between each RL time step
"""
EzPickle.__init__(self)
self._has_button_been_pressed_before = False
MujocoEnv.__init__(
self,
str(Path("../../mujoco/spider.xml").resolve()),
frame_skip=action_repeat,
)
def __init__(self, **kwargs):
self.MAPPING = {0: [0, 1],
1: [2, 3]}
self.REST_POSE = np.array([-1/4, 1/8, 1/4, -1/8]) # NEED TO DEFINE WITH ALEXIE
#0: RIGHT HIP, 1: RIGHT KNEE, 2: LEFT HIP, 3: LEFT KNEE
# self.LEGS_UP = np.array([0.0, 0.0, 0.0, 0.0]) # NEED TO DEFINE WITH ALEXIE
# self.RIGHT_HIP_LIMIT = (0, 1/2)
# self.LEFT_HIP_LIMIT = (-1/2, 0)
self.RIGHT_HIP_LIMIT = (-1/3, -1/6) # -
self.LEFT_HIP_LIMIT = (1/6, 1/3) # +
self.RIGHT_KNEE_LIMIT = (0, 1/4) # +
self.LEFT_KNEE_LIMIT = (-1/4, 0) # -
self.RIGHT_HIP_INDEX = 0
self.RIGHT_KNEE_INDEX = 1
self.LEFT_HIP_INDEX = 2
self.LEFT_KNEE_INDEX = 3
self.RIGHT_HIP_BL_INDEX = 4
self.RIGHT_KNEE_BL_INDEX = 5
self.LEFT_HIP_BL_INDEX = 6
self.LEFT_KNEE_BL_INDEX = 7
self.KNEE_INDEX = [1, 3]
self.HIP_INDEX = [0, 2]
self.len_Femur = 0.39152
self.len_Tibia = 0.39857
self.dist_btwn = 0.035
self.pos = self.REST_POSE.copy()
self.reward = 0
self.numofmotor = 4
self.policy = kwargs['policy']
# xml_path = os.path.dirname(
# os.path.abspath(__file__)) + '/envs/model/past_NABI-v0.xml' # NEED TO MODIFY XML FILE NAME
xml_path = '/home/jin/project/rlnabi/PPO/envs/model/Nabi-v0.xml'
# xml_path = '/home/jin/project/rlnabi/PPO/envs/model/Nabi-v1_jump.xml'
frame_skip = 1
MujocoEnv.__init__(self, xml_path, frame_skip) # frame skip : 5, Initialize self
utils.EzPickle.__init__(self)
def __init__(self):
self.goal_radius = 4.0
self.noise_scale = 0.8
self.mode_locations = []
max_range = np.pi
for i in range(1):
ang = i * max_range / 3 - np.pi / 2
self.mode_locations.append([
self.goal_radius * np.sin(ang), self.goal_radius * np.cos(ang)
])
self.mode_locations = np.array(self.mode_locations)
self.set_task(self.sample_tasks(1)[0])
asset_path = os.path.join(
os.getcwd(),
'meta_policy_search/envs/mujoco_envs/assets/ant_target.xml')
MujocoEnv.__init__(self, asset_path, 2)
gym.utils.EzPickle.__init__(self)
def __init__(self, episode_len=499):
# # 1 x 1 x 8 x 2
# self.valid_targets = np.array(
# [[[
# [8.0, 0.0],
# [0.0, 8.0],
# [-8.0, 0.0],
# [0.0, -8.0],
# ]]]
# )
self.timestep = 0.0
self.episode_len = episode_len
# self.init_xy = init_pos.copy()
xml_path = os.path.join(os.path.dirname(__file__), "assets",
'low_gear_ratio_ant.xml')
# xml_path = os.path.join(os.path.dirname(__file__), "assets", 's_maze.xml')
MujocoEnv.__init__(self, xml_path, 5)
utils.EzPickle.__init__(self)
def __init__(self):
a = np.sqrt(0.225**2 + 0.225**2)
# regular hexagon
self.mode_locations = np.array([
[a, 0],
[a / 2, np.sqrt(3) * a / 2],
[-a / 2, np.sqrt(3) * a / 2],
[-a / 2, -np.sqrt(3) * a / 2],
[a / 2, -np.sqrt(3) * a / 2],
[-a, 0],
],
dtype=np.float32)
self.noise_scale = 0.1
self.max_norm = 0.30
self.set_task(self.sample_tasks(1)[0])
asset_path = os.path.join(
os.getcwd(),
'meta_policy_search/envs/mujoco_envs/assets/reacher_3link.xml')
MujocoEnv.__init__(self, asset_path, 2)
utils.EzPickle.__init__(self)
def __init__(self,
task='hfield',
reset_task_every_episode=False,
*args,
**kwargs):
self.reset_task_every_episode = reset_task_every_episode
self.first = True
self._action_bounds = None
MujocoEnv.__init__(self, self.FILE, 1)
self._action_bounds = self.action_space.low, self.action_space.high
gym.utils.EzPickle.__init__(self)
# hfield default configuration
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
assert task in [
None, 'None', 'hfield', 'same', 'hill', 'gentle', 'steep', 'basin'
]
self.task = task
def __init__(self, use_rel_pos_obs=False, terminate_near_target=False):
self.terminate_near_target = terminate_near_target
self.terminate_radius = 0.5
self.use_rel_pos_obs = use_rel_pos_obs
# 1 x 1 x 8 x 2
self.valid_targets = np.array([[[
# [2.0, 0.0],
# # [1.41, 1.41],
# [0.0, 2.0],
# # [-1.41, 1.41],
# [-2.0, 0.0],
# # [-1.41, -1.41],
# [0.0, -2.0],
# # [1.41, -1.41]
[4.0, 0.0],
[0.0, 4.0],
[-4.0, 0.0],
[0.0, -4.0],
]]])
xml_path = os.path.join(os.path.dirname(__file__), "assets",
'low_gear_ratio_ant.xml')
MujocoEnv.__init__(self, xml_path, 5)
utils.EzPickle.__init__(self)
def reset(self, *args, **kwargs):
MujocoEnv.reset(self, *args, **kwargs)
return self._get_obs()
def __init__(self, task=None):
MetaEnv.__init__(self, task)
MujocoEnv.__init__(self, 'ant.xml', 5)
gym.utils.EzPickle.__init__(self)
def __init__(self):
MujocoEnv.__init__(self, 'half_cheetah.xml', 5)
gym.utils.EzPickle.__init__(self)
def __init__(self, goal_direction=None):
self.goal_direction = goal_direction if goal_direction else 1.0
MujocoEnv.__init__(self, 'half_cheetah.xml', 5)
gym.utils.EzPickle.__init__(self, goal_direction)
def __init__(self):
MujocoEnv.__init__(self, 'swimmer.xml', 4)
gym.utils.EzPickle.__init__(self)
def __init__(self, goal_direction=None):
self.goal_direction = goal_direction if goal_direction else 1.0
MujocoEnv.__init__(self, "half_cheetah.xml", 5)
EzPickle.__init__(self, goal_direction)
def __init__(self):
self.set_task(self.sample_tasks(1)[0])
MujocoEnv.__init__(self, 'ant.xml', 5)
gym.utils.EzPickle.__init__(self)
def __init__(self, goal_direction=None):
MujocoEnv.__init__(self, 'ant.xml', 5)
gym.utils.EzPickle.__init__(self)
def __init__(self):
self.set_task(self.sample_tasks(1)[0])
MujocoEnv.__init__(self, 'walker2d.xml', 8)
gym.utils.EzPickle.__init__(self)
