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Merge pull request #38 from ALRhub/remove_mujoco

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Replace mujoco_py bindings with new pybind11-based bindings
This commit is contained in:
Philipp Becker 2022-07-13 13:52:18 +02:00 committed by GitHub
commit d412fb229c
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18 changed files with 135 additions and 81 deletions
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15
alr_envs/envs/mujoco/ant_jump/ant_jump.py
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@ -2,7 +2,7 @@ from typing import Tuple, Union, Optional
import numpy as np
from gym.core import ObsType
from gym.envs.mujoco.ant_v3 import AntEnv
from gym.envs.mujoco.ant_v4 import AntEnv
MAX_EPISODE_STEPS_ANTJUMP = 200
@ -36,9 +36,15 @@ class AntJumpEnv(AntEnv):
self.current_step = 0
self.max_height = 0
self.goal = 0
super().__init__(xml_file, ctrl_cost_weight, contact_cost_weight, healthy_reward, terminate_when_unhealthy,
healthy_z_range, contact_force_range, reset_noise_scale,
exclude_current_positions_from_observation)
super().__init__(xml_file=xml_file,
ctrl_cost_weight=ctrl_cost_weight,
contact_cost_weight=contact_cost_weight,
healthy_reward=healthy_reward,
terminate_when_unhealthy=terminate_when_unhealthy,
healthy_z_range=healthy_z_range,
contact_force_range=contact_force_range,
reset_noise_scale=reset_noise_scale,
exclude_current_positions_from_observation=exclude_current_positions_from_observation)
def step(self, action):
self.current_step += 1
@ -90,6 +96,7 @@ class AntJumpEnv(AntEnv):
# reset_model had to be implemented in every env to make it deterministic
def reset_model(self):
# Todo remove if not needed
noise_low = -self._reset_noise_scale
noise_high = self._reset_noise_scale

4
alr_envs/envs/mujoco/ant_jump/mp_wrapper.py
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@ -16,8 +16,8 @@ class MPWrapper(RawInterfaceWrapper):
@property
def current_pos(self) -> Union[float, int, np.ndarray]:
return self.env.sim.data.qpos[7:15].copy()
return self.data.qpos[7:15].copy()
@property
def current_vel(self) -> Union[float, int, np.ndarray, Tuple]:
return self.env.sim.data.qvel[6:14].copy()
return self.data.qvel[6:14].copy()

5
alr_envs/envs/mujoco/beerpong/assets/beerpong_wo_cup_big_table.xml
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@ -185,4 +185,9 @@
<motor ctrllimited="true" ctrlrange="-1.0 1.0" gear="2.0" joint="wam/palm_yaw_joint"/>
</actuator>
<sensor>
<framelinvel name="init_ball_vel" objtype="site" objname="init_ball_pos" />
</sensor>
</mujoco>

40
alr_envs/envs/mujoco/beerpong/beerpong.py
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@ -1,7 +1,6 @@
import os
from typing import Optional
import mujoco_py.builder
import numpy as np
from gym import utils
from gym.envs.mujoco import MujocoEnv
@ -51,9 +50,9 @@ class BeerPongEnv(MujocoEnv, utils.EzPickle):
self.repeat_action = 2
# TODO: If accessing IDs is easier in the (new) official mujoco bindings, remove this
self.model = None
self.site_id = lambda x: self.model.site_name2id(x)
self.body_id = lambda x: self.model.body_name2id(x)
self.geom_id = lambda x: self.model.geom_name2id(x)
self.geom_id = lambda x: self._mujoco_bindings.mj_name2id(self.model,
self._mujoco_bindings.mjtObj.mjOBJ_GEOM,
x)
# for reward calculation
self.dists = []
@ -66,7 +65,7 @@ class BeerPongEnv(MujocoEnv, utils.EzPickle):
self.ball_in_cup = False
self.dist_ground_cup = -1 # distance floor to cup if first floor contact
MujocoEnv.__init__(self, self.xml_path, frame_skip=1, mujoco_bindings="mujoco_py")
MujocoEnv.__init__(self, model_path=self.xml_path, frame_skip=1, mujoco_bindings="mujoco")
utils.EzPickle.__init__(self)
@property
@ -101,13 +100,13 @@ class BeerPongEnv(MujocoEnv, utils.EzPickle):
# TODO: Ask Max why we need to set the state twice.
self.set_state(start_pos, init_vel)
start_pos[7::] = self.data.site_xpos[self.site_id("init_ball_pos"), :].copy()
start_pos[7::] = self.data.site("init_ball_pos").xpos.copy()
self.set_state(start_pos, init_vel)
xy = self.np_random.uniform(self._cup_pos_min, self._cup_pos_max)
xyz = np.zeros(3)
xyz[:2] = xy
xyz[-1] = 0.840
self.model.body_pos[self.body_id("cup_table")] = xyz
self.model.body("cup_table").pos[:] = xyz
return self._get_obs()
def step(self, a):
@ -118,10 +117,10 @@ class BeerPongEnv(MujocoEnv, utils.EzPickle):
self.do_simulation(applied_action, self.frame_skip)
# self.reward_function.check_contacts(self.sim) # I assume this is not important?
if self._steps < self.release_step:
self.data.qpos[7::] = self.data.site_xpos[self.site_id("init_ball_pos"), :].copy()
self.data.qvel[7::] = self.data.site_xvelp[self.site_id("init_ball_pos"), :].copy()
self.data.qpos[7::] = self.data.site('init_ball_pos').xpos.copy()
self.data.qvel[7::] = self.data.sensor('init_ball_vel').data.copy()
crash = False
except mujoco_py.builder.MujocoException:
except Exception as e:
crash = True
ob = self._get_obs()
@ -146,18 +145,18 @@ class BeerPongEnv(MujocoEnv, utils.EzPickle):
return ob, reward, done, infos
def _get_obs(self):
theta = self.data.qpos.flat[:7]
theta_dot = self.data.qvel.flat[:7]
ball_pos = self.data.get_body_xpos("ball").copy()
cup_goal_diff_final = ball_pos - self.data.get_site_xpos("cup_goal_final_table").copy()
cup_goal_diff_top = ball_pos - self.data.get_site_xpos("cup_goal_table").copy()
theta = self.data.qpos.flat[:7].copy()
theta_dot = self.data.qvel.flat[:7].copy()
ball_pos = self.data.qpos.flat[7:].copy()
cup_goal_diff_final = ball_pos - self.data.site("cup_goal_final_table").xpos.copy()
cup_goal_diff_top = ball_pos - self.data.site("cup_goal_table").xpos.copy()
return np.concatenate([
np.cos(theta),
np.sin(theta),
theta_dot,
cup_goal_diff_final,
cup_goal_diff_top,
self.model.body_pos[self.body_id("cup_table")][:2].copy(),
self.model.body("cup_table").pos[:2].copy(),
# [self._steps], # Use TimeAwareObservation Wrapper instead ....
])
@ -166,10 +165,10 @@ class BeerPongEnv(MujocoEnv, utils.EzPickle):
return super(BeerPongEnv, self).dt * self.repeat_action
def _get_reward(self, action):
goal_pos = self.data.get_site_xpos("cup_goal_table")
ball_pos = self.data.get_body_xpos("ball")
ball_vel = self.data.get_body_xvelp("ball")
goal_final_pos = self.data.get_site_xpos("cup_goal_final_table")
goal_pos = self.data.site("cup_goal_table").xpos
goal_final_pos = self.data.site("cup_goal_final_table").xpos
ball_pos = self.data.qpos.flat[7:].copy()
ball_vel = self.data.qvel.flat[7:].copy()
self._check_contacts()
self.dists.append(np.linalg.norm(goal_pos - ball_pos))
@ -219,6 +218,7 @@ class BeerPongEnv(MujocoEnv, utils.EzPickle):
return reward, infos
def _check_contacts(self):
#TODO proper contact detection using sensors?
if not self.ball_table_contact:
self.ball_table_contact = self._check_collision({self.geom_id("ball_geom")},
{self.geom_id("table_contact_geom")})

10
alr_envs/envs/mujoco/beerpong/mp_wrapper.py
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@ -21,16 +21,16 @@ class MPWrapper(RawInterfaceWrapper):
@property
def current_pos(self) -> Union[float, int, np.ndarray, Tuple]:
return self.env.data.qpos[0:7].copy()
return self.data.qpos[0:7].copy()
@property
def current_vel(self) -> Union[float, int, np.ndarray, Tuple]:
return self.env.data.qvel[0:7].copy()
return self.data.qvel[0:7].copy()
# TODO: Fix this
def episode_callback(self, action: np.ndarray, mp) -> Tuple[np.ndarray, Union[np.ndarray, None]]:
if mp.learn_tau:
self.env.env.release_step = action[0] / self.env.dt # Tau value
self.release_step = action[0] / self.dt # Tau value
return action, None
else:
return action, None
@ -39,5 +39,5 @@ class MPWrapper(RawInterfaceWrapper):
xyz = np.zeros(3)
xyz[:2] = context
xyz[-1] = 0.840
self.env.env.model.body_pos[self.env.env.cup_table_id] = xyz
return self.get_observation_from_step(self.env.env._get_obs())
self.model.body_pos[self.cup_table_id] = xyz
return self.get_observation_from_step(self.get_obs())

10
alr_envs/envs/mujoco/half_cheetah_jump/half_cheetah_jump.py
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@ -2,7 +2,7 @@ import os
from typing import Tuple, Union, Optional
from gym.core import ObsType
from gym.envs.mujoco.half_cheetah_v3 import HalfCheetahEnv
from gym.envs.mujoco.half_cheetah_v4 import HalfCheetahEnv
import numpy as np
MAX_EPISODE_STEPS_HALFCHEETAHJUMP = 100
@ -27,8 +27,11 @@ class HalfCheetahJumpEnv(HalfCheetahEnv):
self.goal = 0
self.context = context
xml_file = os.path.join(os.path.dirname(__file__), "assets", xml_file)
super().__init__(xml_file, forward_reward_weight, ctrl_cost_weight, reset_noise_scale,
exclude_current_positions_from_observation)
super().__init__(xml_file=xml_file,
forward_reward_weight=forward_reward_weight,
ctrl_cost_weight=ctrl_cost_weight,
reset_noise_scale=reset_noise_scale,
exclude_current_positions_from_observation=exclude_current_positions_from_observation)
def step(self, action):
@ -74,6 +77,7 @@ class HalfCheetahJumpEnv(HalfCheetahEnv):
# overwrite reset_model to make it deterministic
def reset_model(self):
# TODO remove if not needed!
noise_low = -self._reset_noise_scale
noise_high = self._reset_noise_scale

4
alr_envs/envs/mujoco/half_cheetah_jump/mp_wrapper.py
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@ -15,8 +15,8 @@ class MPWrapper(RawInterfaceWrapper):
@property
def current_pos(self) -> Union[float, int, np.ndarray]:
return self.env.sim.data.qpos[3:9].copy()
return self.data.qpos[3:9].copy()
@property
def current_vel(self) -> Union[float, int, np.ndarray, Tuple]:
return self.env.sim.data.qvel[3:9].copy()
return self.data.qvel[3:9].copy()

40
alr_envs/envs/mujoco/hopper_jump/hopper_jump.py
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@ -2,8 +2,7 @@ import copy
import os
import numpy as np
from gym.envs.mujoco.hopper_v3 import HopperEnv
from gym.envs.mujoco.hopper_v4 import HopperEnv
MAX_EPISODE_STEPS_HOPPERJUMP = 250
@ -50,9 +49,16 @@ class HopperJumpEnv(HopperEnv):
self.contact_dist = None
xml_file = os.path.join(os.path.dirname(__file__), "assets", xml_file)
super().__init__(xml_file, forward_reward_weight, ctrl_cost_weight, healthy_reward, terminate_when_unhealthy,
healthy_state_range, healthy_z_range, healthy_angle_range, reset_noise_scale,
exclude_current_positions_from_observation)
super().__init__(xml_file=xml_file,
forward_reward_weight=forward_reward_weight,
ctrl_cost_weight=ctrl_cost_weight,
healthy_reward=healthy_reward,
terminate_when_unhealthy=terminate_when_unhealthy,
healthy_state_range=healthy_state_range,
healthy_z_range=healthy_z_range,
healthy_angle_range=healthy_angle_range,
reset_noise_scale=reset_noise_scale,
exclude_current_positions_from_observation=exclude_current_positions_from_observation)
# increase initial height
self.init_qpos[1] = 1.5
@ -67,7 +73,8 @@ class HopperJumpEnv(HopperEnv):
self.do_simulation(action, self.frame_skip)
height_after = self.get_body_com("torso")[2]
site_pos_after = self.data.get_site_xpos('foot_site')
#site_pos_after = self.data.get_site_xpos('foot_site')
site_pos_after = self.data.site('foot_site').xpos
self.max_height = max(height_after, self.max_height)
has_floor_contact = self._is_floor_foot_contact() if not self.contact_with_floor else False
@ -111,7 +118,8 @@ class HopperJumpEnv(HopperEnv):
return observation, reward, done, info
def _get_obs(self):
goal_dist = self.data.get_site_xpos('foot_site') - self.goal
# goal_dist = self.data.get_site_xpos('foot_site') - self.goal
goal_dist = self.data.site('foot_site').xpos - self.goal
return np.concatenate((super(HopperJumpEnv, self)._get_obs(), goal_dist.copy(), self.goal[:1]))
def reset_model(self):
@ -119,7 +127,8 @@ class HopperJumpEnv(HopperEnv):
# self.goal = self.np_random.uniform(0.3, 1.35, 1)[0]
self.goal = np.concatenate([self.np_random.uniform(0.3, 1.35, 1), np.zeros(2, )])
self.sim.model.body_pos[self.sim.model.body_name2id('goal_site_body')] = self.goal
# self.sim.model.body_pos[self.sim.model.body_name2id('goal_site_body')] = self.goal
self.data.body('goal_site_body').xpos[:] = np.copy(self.goal)
self.max_height = 0
self._steps = 0
@ -153,8 +162,15 @@ class HopperJumpEnv(HopperEnv):
return observation
def _is_floor_foot_contact(self):
floor_geom_id = self.model.geom_name2id('floor')
foot_geom_id = self.model.geom_name2id('foot_geom')
# floor_geom_id = self.model.geom_name2id('floor')
# foot_geom_id = self.model.geom_name2id('foot_geom')
# TODO: do this properly over a sensor in the xml file, see dmc hopper
floor_geom_id = self._mujoco_bindings.mj_name2id(self.model,
self._mujoco_bindings.mjtObj.mjOBJ_GEOM,
'floor')
foot_geom_id = self._mujoco_bindings.mj_name2id(self.model,
self._mujoco_bindings.mjtObj.mjOBJ_GEOM,
'foot_geom')
for i in range(self.data.ncon):
contact = self.data.contact[i]
collision = contact.geom1 == floor_geom_id and contact.geom2 == foot_geom_id
@ -163,7 +179,7 @@ class HopperJumpEnv(HopperEnv):
return True
return False
# TODO is that needed? if so test it
class HopperJumpStepEnv(HopperJumpEnv):
def __init__(self,
@ -193,7 +209,7 @@ class HopperJumpStepEnv(HopperJumpEnv):
self.do_simulation(action, self.frame_skip)
height_after = self.get_body_com("torso")[2]
site_pos_after = self.data.get_site_xpos('foot_site')
site_pos_after = self.data.site('foot_site').xpos.copy()
self.max_height = max(height_after, self.max_height)
ctrl_cost = self.control_cost(action)

10
alr_envs/envs/mujoco/hopper_jump/hopper_jump_on_box.py
View File

@ -1,4 +1,4 @@
from gym.envs.mujoco.hopper_v3 import HopperEnv
from gym.envs.mujoco.hopper_v4 import HopperEnv
import numpy as np
import os
@ -46,7 +46,7 @@ class HopperJumpOnBoxEnv(HopperEnv):
foot_pos = self.get_body_com("foot")
self.max_height = max(height_after, self.max_height)
vx, vz, vangle = self.sim.data.qvel[0:3]
vx, vz, vangle = self.data.qvel[0:3]
s = self.state_vector()
fell_over = not (np.isfinite(s).all() and (np.abs(s[2:]) < 100).all() and (height_after > .7))
@ -67,7 +67,8 @@ class HopperJumpOnBoxEnv(HopperEnv):
is_on_box_y = True # is y always true because he can only move in x and z direction?
is_on_box_z = box_height - 0.02 <= foot_pos[2] <= box_height + 0.02
is_on_box = is_on_box_x and is_on_box_y and is_on_box_z
if is_on_box: self.hopper_on_box = True
if is_on_box:
self.hopper_on_box = True
ctrl_cost = self.control_cost(action)
@ -133,9 +134,8 @@ class HopperJumpOnBoxEnv(HopperEnv):
self.current_step = 0
self.hopper_on_box = False
if self.context:
box_id = self.sim.model.body_name2id("box")
self.box_x = self.np_random.uniform(1, 3, 1)
self.sim.model.body_pos[box_id] = [self.box_x, 0, 0]
self.model.body("box").pos = [self.box_x[0], 0, 0]
return super().reset()
# overwrite reset_model to make it deterministic

4
alr_envs/envs/mujoco/hopper_jump/mp_wrapper.py
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@ -20,8 +20,8 @@ class MPWrapper(RawInterfaceWrapper):
@property
def current_pos(self) -> Union[float, int, np.ndarray, Tuple]:
return self.sim.data.qpos[3:6].copy()
return self.data.qpos[3:6].copy()
@property
def current_vel(self) -> Union[float, int, np.ndarray, Tuple]:
return self.sim.data.qvel[3:6].copy()
return self.data.qvel[3:6].copy()

15
alr_envs/envs/mujoco/hopper_throw/hopper_throw.py
View File

@ -1,7 +1,7 @@
import os
from typing import Optional
from gym.envs.mujoco.hopper_v3 import HopperEnv
from gym.envs.mujoco.hopper_v4 import HopperEnv
import numpy as np
MAX_EPISODE_STEPS_HOPPERTHROW = 250
@ -36,9 +36,16 @@ class HopperThrowEnv(HopperEnv):
self.max_episode_steps = max_episode_steps
self.context = context
self.goal = 0
super().__init__(xml_file, forward_reward_weight, ctrl_cost_weight, healthy_reward, terminate_when_unhealthy,
healthy_state_range, healthy_z_range, healthy_angle_range, reset_noise_scale,
exclude_current_positions_from_observation)
super().__init__(xml_file=xml_file,
forward_reward_weight=forward_reward_weight,
ctrl_cost_weight=ctrl_cost_weight,
healthy_reward=healthy_reward,
terminate_when_unhealthy=terminate_when_unhealthy,
healthy_angle_range=healthy_state_range,
healthy_z_range=healthy_z_range,
healthy_state_range=healthy_angle_range,
reset_noise_scale=reset_noise_scale,
exclude_current_positions_from_observation=exclude_current_positions_from_observation)
def step(self, action):
self.current_step += 1

21
alr_envs/envs/mujoco/hopper_throw/hopper_throw_in_basket.py
View File

@ -1,7 +1,7 @@
import os
from typing import Optional
from gym.envs.mujoco.hopper_v3 import HopperEnv
from gym.envs.mujoco.hopper_v4 import HopperEnv
import numpy as np
@ -44,9 +44,16 @@ class HopperThrowInBasketEnv(HopperEnv):
self.penalty = penalty
self.basket_x = 5
xml_file = os.path.join(os.path.dirname(__file__), "assets", xml_file)
super().__init__(xml_file, forward_reward_weight, ctrl_cost_weight, healthy_reward, terminate_when_unhealthy,
healthy_state_range, healthy_z_range, healthy_angle_range, reset_noise_scale,
exclude_current_positions_from_observation)
super().__init__(xml_file=xml_file,
forward_reward_weight=forward_reward_weight,
ctrl_cost_weight=ctrl_cost_weight,
healthy_reward=healthy_reward,
terminate_when_unhealthy=terminate_when_unhealthy,
healthy_state_range=healthy_state_range,
healthy_z_range=healthy_z_range,
healthy_angle_range=healthy_angle_range,
reset_noise_scale=reset_noise_scale,
exclude_current_positions_from_observation=exclude_current_positions_from_observation)
def step(self, action):
@ -109,13 +116,13 @@ class HopperThrowInBasketEnv(HopperEnv):
self.current_step = 0
self.ball_in_basket = False
if self.context:
basket_id = self.sim.model.body_name2id("basket_ground")
self.basket_x = self.np_random.uniform(3, 7, 1)
self.sim.model.body_pos[basket_id] = [self.basket_x, 0, 0]
self.basket_x = self.np_random.uniform(low=3, high=7, size=1)
self.model.body("basket_ground").pos[:] = [self.basket_x[0], 0, 0]
return super().reset()
# overwrite reset_model to make it deterministic
def reset_model(self):
# Todo remove if not needed!
noise_low = -self._reset_noise_scale
noise_high = self._reset_noise_scale

4
alr_envs/envs/mujoco/hopper_throw/mp_wrapper.py
View File

@ -16,8 +16,8 @@ class MPWrapper(RawInterfaceWrapper):
@property
def current_pos(self) -> Union[float, int, np.ndarray]:
return self.env.data.qpos[3:6].copy()
return self.data.qpos[3:6].copy()
@property
def current_vel(self) -> Union[float, int, np.ndarray, Tuple]:
return self.env.data.qvel[3:6].copy()
return self.data.qvel[3:6].copy()

10
alr_envs/envs/mujoco/reacher/mp_wrapper.py
View File

@ -10,18 +10,18 @@ class MPWrapper(RawInterfaceWrapper):
@property
def context_mask(self):
return np.concatenate([[False] * self.env.n_links, # cos
[False] * self.env.n_links, # sin
return np.concatenate([[False] * self.n_links, # cos
[False] * self.n_links, # sin
[True] * 2, # goal position
[False] * self.env.n_links, # angular velocity
[False] * self.n_links, # angular velocity
[False] * 3, # goal distance
# [False], # step
])
@property
def current_pos(self) -> Union[float, int, np.ndarray, Tuple]:
return self.env.data.qpos.flat[:self.env.n_links]
return self.data.qpos.flat[:self.n_links]
@property
def current_vel(self) -> Union[float, int, np.ndarray, Tuple]:
return self.env.data.qvel.flat[:self.env.n_links]
return self.data.qvel.flat[:self.n_links]

5
alr_envs/envs/mujoco/reacher/reacher.py
View File

@ -23,7 +23,10 @@ class ReacherEnv(MujocoEnv, utils.EzPickle):
file_name = f'reacher_{n_links}links.xml'
MujocoEnv.__init__(self, os.path.join(os.path.dirname(__file__), "assets", file_name), 2)
MujocoEnv.__init__(self,
model_path=os.path.join(os.path.dirname(__file__), "assets", file_name),
frame_skip=2,
mujoco_bindings="mujoco")
def step(self, action):
self._steps += 1

4
alr_envs/envs/mujoco/walker_2d_jump/mp_wrapper.py
View File

@ -16,8 +16,8 @@ class MPWrapper(RawInterfaceWrapper):
@property
def current_pos(self) -> Union[float, int, np.ndarray]:
return self.env.data.qpos[3:9].copy()
return self.data.qpos[3:9].copy()
@property
def current_vel(self) -> Union[float, int, np.ndarray, Tuple]:
return self.env.data.qvel[3:9].copy()
return self.data.qvel[3:9].copy()

14
alr_envs/envs/mujoco/walker_2d_jump/walker_2d_jump.py
View File

@ -1,7 +1,7 @@
import os
from typing import Optional
from gym.envs.mujoco.walker2d_v3 import Walker2dEnv
from gym.envs.mujoco.walker2d_v4 import Walker2dEnv
import numpy as np
MAX_EPISODE_STEPS_WALKERJUMP = 300
@ -36,9 +36,15 @@ class Walker2dJumpEnv(Walker2dEnv):
self._penalty = penalty
self.goal = 0
xml_file = os.path.join(os.path.dirname(__file__), "assets", xml_file)
super().__init__(xml_file, forward_reward_weight, ctrl_cost_weight, healthy_reward, terminate_when_unhealthy,
healthy_z_range, healthy_angle_range, reset_noise_scale,
exclude_current_positions_from_observation)
super().__init__(xml_file=xml_file,
forward_reward_weight=forward_reward_weight,
ctrl_cost_weight=ctrl_cost_weight,
healthy_reward=healthy_reward,
terminate_when_unhealthy=terminate_when_unhealthy,
healthy_z_range=healthy_z_range,
healthy_angle_range=healthy_angle_range,
reset_noise_scale=reset_noise_scale,
exclude_current_positions_from_observation=exclude_current_positions_from_observation)
def step(self, action):
self.current_step += 1

1
setup.py
View File

@ -28,7 +28,6 @@ setup(
extras_require=extras,
install_requires=[
'gym>=0.24.0',
"mujoco_py<2.2,>=2.1",
],
packages=[package for package in find_packages() if package.startswith("alr_envs")],
# packages=['alr_envs', 'alr_envs.envs', 'alr_envs.open_ai', 'alr_envs.dmc', 'alr_envs.meta', 'alr_envs.utils'],