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bugfixes

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This commit is contained in:
Fabian 2022-07-07 10:47:04 +02:00
parent 4a3134d7be
commit fc00cf8a87
24 changed files with 235 additions and 302 deletions
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138
alr_envs/alr/__init__.py
View File

@ -157,60 +157,36 @@ register(
id='ALRAntJump-v0',
entry_point='alr_envs.alr.mujoco:AntJumpEnv',
max_episode_steps=MAX_EPISODE_STEPS_ANTJUMP,
kwargs={
"max_episode_steps": MAX_EPISODE_STEPS_ANTJUMP,
"context": True
}
)
register(
id='ALRHalfCheetahJump-v0',
entry_point='alr_envs.alr.mujoco:ALRHalfCheetahJumpEnv',
max_episode_steps=MAX_EPISODE_STEPS_HALFCHEETAHJUMP,
kwargs={
"max_episode_steps": MAX_EPISODE_STEPS_HALFCHEETAHJUMP,
"context": True
}
)
register(
id='HopperJumpOnBox-v0',
entry_point='alr_envs.alr.mujoco:ALRHopperJumpOnBoxEnv',
entry_point='alr_envs.alr.mujoco:HopperJumpOnBoxEnv',
max_episode_steps=MAX_EPISODE_STEPS_HOPPERJUMPONBOX,
kwargs={
"max_episode_steps": MAX_EPISODE_STEPS_HOPPERJUMPONBOX,
"context": True
}
)
register(
id='ALRHopperThrow-v0',
entry_point='alr_envs.alr.mujoco:ALRHopperThrowEnv',
max_episode_steps=MAX_EPISODE_STEPS_HOPPERTHROW,
kwargs={
"max_episode_steps": MAX_EPISODE_STEPS_HOPPERTHROW,
"context": True
}
)
register(
id='ALRHopperThrowInBasket-v0',
entry_point='alr_envs.alr.mujoco:ALRHopperThrowInBasketEnv',
max_episode_steps=MAX_EPISODE_STEPS_HOPPERTHROWINBASKET,
kwargs={
"max_episode_steps": MAX_EPISODE_STEPS_HOPPERTHROWINBASKET,
"context": True
}
)
register(
id='ALRWalker2DJump-v0',
entry_point='alr_envs.alr.mujoco:ALRWalker2dJumpEnv',
max_episode_steps=MAX_EPISODE_STEPS_WALKERJUMP,
kwargs={
"max_episode_steps": MAX_EPISODE_STEPS_WALKERJUMP,
"context": True
}
)
register(
@ -403,46 +379,48 @@ for _v in _versions:
## Table Tennis needs to be fixed according to Zhou's implementation
########################################################################################################################
## AntJump
_versions = ['ALRAntJump-v0']
for _v in _versions:
_name = _v.split("-")
_env_id = f'{_name[0]}ProMP-{_name[1]}'
kwargs_dict_ant_jump_promp = deepcopy(DEFAULT_BB_DICT_ProMP)
kwargs_dict_ant_jump_promp['wrappers'].append(mujoco.ant_jump.MPWrapper)
kwargs_dict_ant_jump_promp['name'] = _v
register(
id=_env_id,
entry_point='alr_envs.utils.make_env_helpers:make_bb_env_helper',
kwargs=kwargs_dict_ant_jump_promp
)
ALL_ALR_MOTION_PRIMITIVE_ENVIRONMENTS["ProMP"].append(_env_id)
########################################################################################################################
## HalfCheetahJump
_versions = ['ALRHalfCheetahJump-v0']
for _v in _versions:
_name = _v.split("-")
_env_id = f'{_name[0]}ProMP-{_name[1]}'
kwargs_dict_halfcheetah_jump_promp = deepcopy(DEFAULT_BB_DICT_ProMP)
kwargs_dict_halfcheetah_jump_promp['wrappers'].append(mujoco.half_cheetah_jump.MPWrapper)
kwargs_dict_halfcheetah_jump_promp['name'] = _v
register(
id=_env_id,
entry_point='alr_envs.utils.make_env_helpers:make_bb_env_helper',
kwargs=kwargs_dict_halfcheetah_jump_promp
)
ALL_ALR_MOTION_PRIMITIVE_ENVIRONMENTS["ProMP"].append(_env_id)
########################################################################################################################
# TODO: Add later when finished
# ########################################################################################################################
#
# ## AntJump
# _versions = ['ALRAntJump-v0']
# for _v in _versions:
# _name = _v.split("-")
# _env_id = f'{_name[0]}ProMP-{_name[1]}'
# kwargs_dict_ant_jump_promp = deepcopy(DEFAULT_BB_DICT_ProMP)
# kwargs_dict_ant_jump_promp['wrappers'].append(mujoco.ant_jump.MPWrapper)
# kwargs_dict_ant_jump_promp['name'] = _v
# register(
# id=_env_id,
# entry_point='alr_envs.utils.make_env_helpers:make_bb_env_helper',
# kwargs=kwargs_dict_ant_jump_promp
# )
# ALL_ALR_MOTION_PRIMITIVE_ENVIRONMENTS["ProMP"].append(_env_id)
#
# ########################################################################################################################
#
# ## HalfCheetahJump
# _versions = ['ALRHalfCheetahJump-v0']
# for _v in _versions:
# _name = _v.split("-")
# _env_id = f'{_name[0]}ProMP-{_name[1]}'
# kwargs_dict_halfcheetah_jump_promp = deepcopy(DEFAULT_BB_DICT_ProMP)
# kwargs_dict_halfcheetah_jump_promp['wrappers'].append(mujoco.half_cheetah_jump.MPWrapper)
# kwargs_dict_halfcheetah_jump_promp['name'] = _v
# register(
# id=_env_id,
# entry_point='alr_envs.utils.make_env_helpers:make_bb_env_helper',
# kwargs=kwargs_dict_halfcheetah_jump_promp
# )
# ALL_ALR_MOTION_PRIMITIVE_ENVIRONMENTS["ProMP"].append(_env_id)
#
# ########################################################################################################################
## HopperJump
_versions = ['HopperJump-v0', 'HopperJumpSparse-v0', 'ALRHopperJumpOnBox-v0', 'ALRHopperThrow-v0',
'ALRHopperThrowInBasket-v0']
_versions = ['HopperJump-v0', 'HopperJumpSparse-v0',
# 'ALRHopperJumpOnBox-v0', 'ALRHopperThrow-v0', 'ALRHopperThrowInBasket-v0'
]
# TODO: Check if all environments work with the same MPWrapper
for _v in _versions:
_name = _v.split("-")
@ -457,23 +435,23 @@ for _v in _versions:
)
ALL_ALR_MOTION_PRIMITIVE_ENVIRONMENTS["ProMP"].append(_env_id)
########################################################################################################################
## Walker2DJump
_versions = ['ALRWalker2DJump-v0']
for _v in _versions:
_name = _v.split("-")
_env_id = f'{_name[0]}ProMP-{_name[1]}'
kwargs_dict_walker2d_jump_promp = deepcopy(DEFAULT_BB_DICT_ProMP)
kwargs_dict_walker2d_jump_promp['wrappers'].append(mujoco.walker_2d_jump.MPWrapper)
kwargs_dict_walker2d_jump_promp['name'] = _v
register(
id=_env_id,
entry_point='alr_envs.utils.make_env_helpers:make_bb_env_helper',
kwargs=kwargs_dict_walker2d_jump_promp
)
ALL_ALR_MOTION_PRIMITIVE_ENVIRONMENTS["ProMP"].append(_env_id)
# ########################################################################################################################
#
#
# ## Walker2DJump
# _versions = ['ALRWalker2DJump-v0']
# for _v in _versions:
# _name = _v.split("-")
# _env_id = f'{_name[0]}ProMP-{_name[1]}'
# kwargs_dict_walker2d_jump_promp = deepcopy(DEFAULT_BB_DICT_ProMP)
# kwargs_dict_walker2d_jump_promp['wrappers'].append(mujoco.walker_2d_jump.MPWrapper)
# kwargs_dict_walker2d_jump_promp['name'] = _v
# register(
# id=_env_id,
# entry_point='alr_envs.utils.make_env_helpers:make_bb_env_helper',
# kwargs=kwargs_dict_walker2d_jump_promp
# )
# ALL_ALR_MOTION_PRIMITIVE_ENVIRONMENTS["ProMP"].append(_env_id)
### Depricated, we will not provide non random starts anymore
"""
@ -639,7 +617,7 @@ for i in _vs:
register(
id='ALRHopperJumpOnBox-v0',
entry_point='alr_envs.alr.mujoco:ALRHopperJumpOnBoxEnv',
entry_point='alr_envs.alr.mujoco:HopperJumpOnBoxEnv',
max_episode_steps=MAX_EPISODE_STEPS_HOPPERJUMPONBOX,
kwargs={
"max_episode_steps": MAX_EPISODE_STEPS_HOPPERJUMPONBOX,

3
alr_envs/alr/mujoco/__init__.py
View File

@ -1,8 +1,9 @@
from .beerpong.beerpong import BeerPongEnv, BeerPongEnvFixedReleaseStep, BeerPongEnvStepBasedEpisodicReward
from .ant_jump.ant_jump import AntJumpEnv
from .half_cheetah_jump.half_cheetah_jump import ALRHalfCheetahJumpEnv
from .hopper_jump.hopper_jump_on_box import ALRHopperJumpOnBoxEnv
from .hopper_jump.hopper_jump_on_box import HopperJumpOnBoxEnv
from .hopper_throw.hopper_throw import ALRHopperThrowEnv
from .hopper_throw.hopper_throw_in_basket import ALRHopperThrowInBasketEnv
from .reacher.reacher import ReacherEnv
from .walker_2d_jump.walker_2d_jump import ALRWalker2dJumpEnv
from .hopper_jump.hopper_jump import HopperJumpEnv

9
alr_envs/alr/mujoco/beerpong/mp_wrapper.py
View File

@ -7,7 +7,8 @@ from alr_envs.black_box.raw_interface_wrapper import RawInterfaceWrapper
class MPWrapper(RawInterfaceWrapper):
def get_context_mask(self):
@property
def context_mask(self) -> np.ndarray:
return np.hstack([
[False] * 7, # cos
[False] * 7, # sin
@ -15,16 +16,16 @@ class MPWrapper(RawInterfaceWrapper):
[False] * 3, # cup_goal_diff_final
[False] * 3, # cup_goal_diff_top
[True] * 2, # xy position of cup
[False] # env steps
# [False] # env steps
])
@property
def current_pos(self) -> Union[float, int, np.ndarray, Tuple]:
return self.env.sim.data.qpos[0:7].copy()
return self.env.data.qpos[0:7].copy()
@property
def current_vel(self) -> Union[float, int, np.ndarray, Tuple]:
return self.env.sim.data.qvel[0:7].copy()
return self.env.data.qvel[0:7].copy()
# TODO: Fix this
def _episode_callback(self, action: np.ndarray, mp) -> Tuple[np.ndarray, Union[np.ndarray, None]]:

2
alr_envs/alr/mujoco/half_cheetah_jump/half_cheetah_jump.py
View File

@ -69,7 +69,7 @@ class ALRHalfCheetahJumpEnv(HalfCheetahEnv):
options: Optional[dict] = None, ) -> Union[ObsType, Tuple[ObsType, dict]]:
self.max_height = 0
self.current_step = 0
self.goal = np.random.uniform(1.1, 1.6, 1) # 1.1 1.6
self.goal = self.np_random.uniform(1.1, 1.6, 1) # 1.1 1.6
return super().reset()
# overwrite reset_model to make it deterministic

1
alr_envs/alr/mujoco/hopper_jump/__init__.py
View File

@ -1,2 +1 @@
from .mp_wrapper import MPWrapper

34
alr_envs/alr/mujoco/hopper_jump/hopper_jump.py
View File

@ -1,10 +1,9 @@
import copy
from typing import Optional
from gym.envs.mujoco.hopper_v3 import HopperEnv
import numpy as np
import os
import numpy as np
from gym.envs.mujoco.hopper_v3 import HopperEnv
MAX_EPISODE_STEPS_HOPPERJUMP = 250
@ -23,10 +22,10 @@ class HopperJumpEnv(HopperEnv):
xml_file='hopper_jump.xml',
forward_reward_weight=1.0,
ctrl_cost_weight=1e-3,
healthy_reward=2.0, # 1 step
contact_weight=2.0, # 0 step
height_weight=10.0, # 3 step
dist_weight=3.0, # 3 step
healthy_reward=2.0,
contact_weight=2.0,
height_weight=10.0,
dist_weight=3.0,
terminate_when_unhealthy=False,
healthy_state_range=(-100.0, 100.0),
healthy_z_range=(0.5, float('inf')),
@ -42,7 +41,7 @@ class HopperJumpEnv(HopperEnv):
self._contact_weight = contact_weight
self.max_height = 0
self.goal = 0
self.goal = np.zeros(3, )
self._steps = 0
self.contact_with_floor = False
@ -58,6 +57,10 @@ class HopperJumpEnv(HopperEnv):
# increase initial height
self.init_qpos[1] = 1.5
@property
def exclude_current_positions_from_observation(self):
return self._exclude_current_positions_from_observation
def step(self, action):
self._steps += 1
@ -80,7 +83,7 @@ class HopperJumpEnv(HopperEnv):
costs = ctrl_cost
done = False
goal_dist = np.linalg.norm(site_pos_after - np.array([self.goal, 0, 0]))
goal_dist = np.linalg.norm(site_pos_after - self.goal)
if self.contact_dist is None and self.contact_with_floor:
self.contact_dist = goal_dist
@ -99,7 +102,7 @@ class HopperJumpEnv(HopperEnv):
height=height_after,
x_pos=site_pos_after,
max_height=self.max_height,
goal=self.goal,
goal=self.goal[:1],
goal_dist=goal_dist,
height_rew=self.max_height,
healthy_reward=self.healthy_reward * 2,
@ -109,14 +112,15 @@ class HopperJumpEnv(HopperEnv):
return observation, reward, done, info
def _get_obs(self):
goal_dist = self.data.get_site_xpos('foot_site') - np.array([self.goal, 0, 0])
return np.concatenate((super(HopperJumpEnv, self)._get_obs(), goal_dist.copy(), self.goal.copy()))
goal_dist = self.data.get_site_xpos('foot_site') - self.goal
return np.concatenate((super(HopperJumpEnv, self)._get_obs(), goal_dist.copy(), self.goal[:1]))
def reset_model(self):
super(HopperJumpEnv, self).reset_model()
self.goal = self.np_random.uniform(0.3, 1.35, 1)[0]
self.sim.model.body_pos[self.sim.model.body_name2id('goal_site_body')] = np.array([self.goal, 0, 0])
# 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.max_height = 0
self._steps = 0

4
alr_envs/alr/mujoco/hopper_jump/hopper_jump_on_box.py
View File

@ -6,7 +6,7 @@ import os
MAX_EPISODE_STEPS_HOPPERJUMPONBOX = 250
class ALRHopperJumpOnBoxEnv(HopperEnv):
class HopperJumpOnBoxEnv(HopperEnv):
"""
Initialization changes to normal Hopper:
- healthy_reward: 1.0 -> 0.01 -> 0.001
@ -153,7 +153,7 @@ class ALRHopperJumpOnBoxEnv(HopperEnv):
if __name__ == '__main__':
render_mode = "human" # "human" or "partial" or "final"
env = ALRHopperJumpOnBoxEnv()
env = HopperJumpOnBoxEnv()
obs = env.reset()
for i in range(2000):

3
alr_envs/alr/mujoco/hopper_jump/mp_wrapper.py
View File

@ -14,7 +14,8 @@ class MPWrapper(RawInterfaceWrapper):
[False] * (2 + int(not self.exclude_current_positions_from_observation)), # position
[True] * 3, # set to true if randomize initial pos
[False] * 6, # velocity
[True]
[True] * 3, # goal distance
[True] # goal
])
@property

12
alr_envs/black_box/black_box_wrapper.py
View File

@ -67,7 +67,9 @@ class BlackBoxWrapper(gym.ObservationWrapper):
def observation(self, observation):
# return context space if we are
return observation[self.env.context_mask] if self.return_context_observation else observation
obs = observation[self.env.context_mask] if self.return_context_observation else observation
# cast dtype because metaworld returns incorrect that throws gym error
return obs.astype(self.observation_space.dtype)
def get_trajectory(self, action: np.ndarray) -> Tuple:
clipped_params = np.clip(action, self.traj_gen_action_space.low, self.traj_gen_action_space.high)
@ -147,7 +149,7 @@ class BlackBoxWrapper(gym.ObservationWrapper):
infos[k] = elems
if self.render_kwargs:
self.render(**self.render_kwargs)
self.env.render(**self.render_kwargs)
if done or self.replanning_schedule(self.current_pos, self.current_vel, obs, c_action,
t + 1 + self.current_traj_steps):
@ -170,13 +172,13 @@ class BlackBoxWrapper(gym.ObservationWrapper):
def render(self, **kwargs):
"""Only set render options here, such that they can be used during the rollout.
This only needs to be called once"""
self.render_kwargs = kwargs or self.render_kwargs
self.render_kwargs = kwargs
# self.env.render(mode=self.render_mode, **self.render_kwargs)
self.env.render(**self.render_kwargs)
# self.env.render(**self.render_kwargs)
def reset(self, *, seed: Optional[int] = None, return_info: bool = False, options: Optional[dict] = None):
self.current_traj_steps = 0
return super(BlackBoxWrapper, self).reset(seed=seed, return_info=return_info, options=options)
return super(BlackBoxWrapper, self).reset()
def plot_trajs(self, des_trajs, des_vels):
import matplotlib.pyplot as plt

3
alr_envs/black_box/controller/meta_world_controller.py
View File

@ -10,13 +10,12 @@ class MetaWorldController(BaseController):
Unlike the other Controllers, this is a special tracking_controller for MetaWorld environments.
They use a position delta for the xyz coordinates and a raw position for the gripper opening.
:param env: A position environment
"""
def get_action(self, des_pos, des_vel, c_pos, c_vel):
gripper_pos = des_pos[-1]
cur_pos = env.current_pos[:-1]
cur_pos = c_pos[:-1]
xyz_pos = des_pos[:-1]
assert xyz_pos.shape == cur_pos.shape, \

0
alr_envs/black_box/controller/controller_factory.py → alr_envs/black_box/factory/controller_factory.py
View File

12
alr_envs/examples/examples_movement_primitives.py
View File

@ -63,16 +63,16 @@ def example_custom_mp(env_name="Reacher5dProMP-v0", seed=1, iterations=1, render
# mp_dict.update({'black_box_kwargs': {'learn_sub_trajectories': True}})
# mp_dict.update({'black_box_kwargs': {'do_replanning': lambda pos, vel, t: lambda t: t % 100}})
rewards = 0
obs = env.reset()
# This time rendering every trajectory
if render:
env.render(mode="human")
rewards = 0
obs = env.reset()
# number of samples/full trajectories (multiple environment steps)
for i in range(iterations):
ac = env.action_space.sample()
ac = env.action_space.sample() * 1000
obs, reward, done, info = env.step(ac)
rewards += reward
@ -139,7 +139,7 @@ def example_fully_custom_mp(seed=1, iterations=1, render=True):
if __name__ == '__main__':
render = False
render = True
# # DMP
# example_mp("alr_envs:HoleReacherDMP-v1", seed=10, iterations=1, render=render)
#
@ -150,7 +150,7 @@ if __name__ == '__main__':
# example_mp("alr_envs:HoleReacherDetPMP-v1", seed=10, iterations=1, render=render)
# Altered basis functions
example_custom_mp("Reacher5dProMP-v0", seed=10, iterations=10, render=render)
example_custom_mp("HopperJumpSparseProMP-v0", seed=10, iterations=10, render=render)
# Custom MP
# example_fully_custom_mp(seed=10, iterations=1, render=render)

96
alr_envs/meta/__init__.py
View File

@ -1,3 +1,5 @@
from copy import deepcopy
from gym import register
from . import goal_object_change_mp_wrapper, goal_change_mp_wrapper, goal_endeffector_change_mp_wrapper, \
@ -7,27 +9,39 @@ ALL_METAWORLD_MOTION_PRIMITIVE_ENVIRONMENTS = {"DMP": [], "ProMP": []}
# MetaWorld
DEFAULT_BB_DICT_ProMP = {
"name": 'EnvName',
"wrappers": [],
"trajectory_generator_kwargs": {
'trajectory_generator_type': 'promp'
},
"phase_generator_kwargs": {
'phase_generator_type': 'linear'
},
"controller_kwargs": {
'controller_type': 'metaworld',
},
"basis_generator_kwargs": {
'basis_generator_type': 'zero_rbf',
'num_basis': 5,
'num_basis_zero_start': 1
}
}
_goal_change_envs = ["assembly-v2", "pick-out-of-hole-v2", "plate-slide-v2", "plate-slide-back-v2",
"plate-slide-side-v2", "plate-slide-back-side-v2"]
for _task in _goal_change_envs:
task_id_split = _task.split("-")
name = "".join([s.capitalize() for s in task_id_split[:-1]])
_env_id = f'{name}ProMP-{task_id_split[-1]}'
kwargs_dict_goal_change_promp = deepcopy(DEFAULT_BB_DICT_ProMP)
kwargs_dict_goal_change_promp['wrappers'].append(goal_change_mp_wrapper.MPWrapper)
kwargs_dict_goal_change_promp['name'] = _task
register(
id=_env_id,
entry_point='alr_envs.utils.make_env_helpers:make_promp_env_helper',
kwargs={
"name": _task,
"wrappers": [goal_change_mp_wrapper.MPWrapper],
"traj_gen_kwargs": {
"num_dof": 4,
"num_basis": 5,
"duration": 6.25,
"post_traj_time": 0,
"zero_start": True,
"policy_type": "metaworld",
}
}
entry_point='alr_envs.utils.make_env_helpers:make_bb_env_helper',
kwargs=kwargs_dict_goal_change_promp
)
ALL_METAWORLD_MOTION_PRIMITIVE_ENVIRONMENTS["ProMP"].append(_env_id)
@ -36,21 +50,13 @@ for _task in _object_change_envs:
task_id_split = _task.split("-")
name = "".join([s.capitalize() for s in task_id_split[:-1]])
_env_id = f'{name}ProMP-{task_id_split[-1]}'
kwargs_dict_object_change_promp = deepcopy(DEFAULT_BB_DICT_ProMP)
kwargs_dict_object_change_promp['wrappers'].append(object_change_mp_wrapper.MPWrapper)
kwargs_dict_object_change_promp['name'] = _task
register(
id=_env_id,
entry_point='alr_envs.utils.make_env_helpers:make_promp_env_helper',
kwargs={
"name": _task,
"wrappers": [object_change_mp_wrapper.MPWrapper],
"traj_gen_kwargs": {
"num_dof": 4,
"num_basis": 5,
"duration": 6.25,
"post_traj_time": 0,
"zero_start": True,
"policy_type": "metaworld",
}
}
entry_point='alr_envs.utils.make_env_helpers:make_bb_env_helper',
kwargs=kwargs_dict_object_change_promp
)
ALL_METAWORLD_MOTION_PRIMITIVE_ENVIRONMENTS["ProMP"].append(_env_id)
@ -69,21 +75,14 @@ for _task in _goal_and_object_change_envs:
task_id_split = _task.split("-")
name = "".join([s.capitalize() for s in task_id_split[:-1]])
_env_id = f'{name}ProMP-{task_id_split[-1]}'
kwargs_dict_goal_and_object_change_promp = deepcopy(DEFAULT_BB_DICT_ProMP)
kwargs_dict_goal_and_object_change_promp['wrappers'].append(goal_object_change_mp_wrapper.MPWrapper)
kwargs_dict_goal_and_object_change_promp['name'] = _task
register(
id=_env_id,
entry_point='alr_envs.utils.make_env_helpers:make_promp_env_helper',
kwargs={
"name": _task,
"wrappers": [goal_object_change_mp_wrapper.MPWrapper],
"traj_gen_kwargs": {
"num_dof": 4,
"num_basis": 5,
"duration": 6.25,
"post_traj_time": 0,
"zero_start": True,
"policy_type": "metaworld",
}
}
entry_point='alr_envs.utils.make_env_helpers:make_bb_env_helper',
kwargs=kwargs_dict_goal_and_object_change_promp
)
ALL_METAWORLD_MOTION_PRIMITIVE_ENVIRONMENTS["ProMP"].append(_env_id)
@ -92,20 +91,13 @@ for _task in _goal_and_endeffector_change_envs:
task_id_split = _task.split("-")
name = "".join([s.capitalize() for s in task_id_split[:-1]])
_env_id = f'{name}ProMP-{task_id_split[-1]}'
kwargs_dict_goal_and_endeffector_change_promp = deepcopy(DEFAULT_BB_DICT_ProMP)
kwargs_dict_goal_and_endeffector_change_promp['wrappers'].append(goal_endeffector_change_mp_wrapper.MPWrapper)
kwargs_dict_goal_and_endeffector_change_promp['name'] = _task
register(
id=_env_id,
entry_point='alr_envs.utils.make_env_helpers:make_promp_env_helper',
kwargs={
"name": _task,
"wrappers": [goal_endeffector_change_mp_wrapper.MPWrapper],
"traj_gen_kwargs": {
"num_dof": 4,
"num_basis": 5,
"duration": 6.25,
"post_traj_time": 0,
"zero_start": True,
"policy_type": "metaworld",
}
}
entry_point='alr_envs.utils.make_env_helpers:make_bb_env_helper',
kwargs=kwargs_dict_goal_and_endeffector_change_promp
)
ALL_METAWORLD_MOTION_PRIMITIVE_ENVIRONMENTS["ProMP"].append(_env_id)

21
alr_envs/meta/base_metaworld_mp_wrapper.py Normal file
View File

@ -0,0 +1,21 @@
from abc import ABC
from typing import Tuple, Union
import numpy as np
from alr_envs.black_box.raw_interface_wrapper import RawInterfaceWrapper
class BaseMetaworldMPWrapper(RawInterfaceWrapper, ABC):
@property
def current_pos(self) -> Union[float, int, np.ndarray]:
r_close = self.env.data.get_joint_qpos("r_close")
# TODO check if this is correct
# return np.hstack([self.env.data.get_body_xpos('hand').flatten() / self.env.action_scale, r_close])
return np.hstack([self.env.data.mocap_pos.flatten() / self.env.action_scale, r_close])
@property
def current_vel(self) -> Union[float, int, np.ndarray, Tuple]:
# TODO check if this is correct
return np.zeros(4, )
# raise NotImplementedError("Velocity cannot be retrieved.")

23
alr_envs/meta/goal_change_mp_wrapper.py
View File

@ -1,11 +1,9 @@
from typing import Tuple, Union
import numpy as np
from alr_envs.black_box.raw_interface_wrapper import RawInterfaceWrapper
from alr_envs.meta.base_metaworld_mp_wrapper import BaseMetaworldMPWrapper
class MPWrapper(RawInterfaceWrapper):
class MPWrapper(BaseMetaworldMPWrapper):
"""
This Wrapper is for environments where merely the goal changes in the beginning
and no secondary objects or end effectors are altered at the start of an episode.
@ -49,20 +47,3 @@ class MPWrapper(RawInterfaceWrapper):
# Goal
[True] * 3, # goal position
])
@property
def current_pos(self) -> Union[float, int, np.ndarray]:
r_close = self.env.data.get_joint_qpos("r_close")
return np.hstack([self.env.data.mocap_pos.flatten() / self.env.action_scale, r_close])
@property
def current_vel(self) -> Union[float, int, np.ndarray, Tuple]:
raise NotImplementedError("Velocity cannot be retrieved.")
@property
def goal_pos(self) -> Union[float, int, np.ndarray, Tuple]:
raise ValueError("Goal position is not available and has to be learnt based on the environment.")
@property
def dt(self) -> Union[float, int]:
return self.env.dt

23
alr_envs/meta/goal_endeffector_change_mp_wrapper.py
View File

@ -1,11 +1,9 @@
from typing import Tuple, Union
import numpy as np
from alr_envs.black_box.raw_interface_wrapper import RawInterfaceWrapper
from alr_envs.meta.base_metaworld_mp_wrapper import BaseMetaworldMPWrapper
class MPWrapper(RawInterfaceWrapper):
class MPWrapper(BaseMetaworldMPWrapper):
"""
This Wrapper is for environments where merely the goal changes in the beginning
and no secondary objects or end effectors are altered at the start of an episode.
@ -49,20 +47,3 @@ class MPWrapper(RawInterfaceWrapper):
# Goal
[True] * 3, # goal position
])
@property
def current_pos(self) -> Union[float, int, np.ndarray]:
r_close = self.env.data.get_joint_qpos("r_close")
return np.hstack([self.env.data.mocap_pos.flatten() / self.env.action_scale, r_close])
@property
def current_vel(self) -> Union[float, int, np.ndarray, Tuple]:
raise NotImplementedError("Velocity cannot be retrieved.")
@property
def goal_pos(self) -> Union[float, int, np.ndarray, Tuple]:
raise ValueError("Goal position is not available and has to be learnt based on the environment.")
@property
def dt(self) -> Union[float, int]:
return self.env.dt

23
alr_envs/meta/goal_object_change_mp_wrapper.py
View File

@ -1,11 +1,9 @@
from typing import Tuple, Union
import numpy as np
from alr_envs.black_box.raw_interface_wrapper import RawInterfaceWrapper
from alr_envs.meta.base_metaworld_mp_wrapper import BaseMetaworldMPWrapper
class MPWrapper(RawInterfaceWrapper):
class MPWrapper(BaseMetaworldMPWrapper):
"""
This Wrapper is for environments where merely the goal changes in the beginning
and no secondary objects or end effectors are altered at the start of an episode.
@ -49,20 +47,3 @@ class MPWrapper(RawInterfaceWrapper):
# Goal
[True] * 3, # goal position
])
@property
def current_pos(self) -> Union[float, int, np.ndarray]:
r_close = self.env.data.get_joint_qpos("r_close")
return np.hstack([self.env.data.mocap_pos.flatten() / self.env.action_scale, r_close])
@property
def current_vel(self) -> Union[float, int, np.ndarray, Tuple]:
raise NotImplementedError("Velocity cannot be retrieved.")
@property
def goal_pos(self) -> Union[float, int, np.ndarray, Tuple]:
raise ValueError("Goal position is not available and has to be learnt based on the environment.")
@property
def dt(self) -> Union[float, int]:
return self.env.dt

23
alr_envs/meta/object_change_mp_wrapper.py
View File

@ -1,11 +1,9 @@
from typing import Tuple, Union
import numpy as np
from alr_envs.black_box.raw_interface_wrapper import RawInterfaceWrapper
from alr_envs.meta.base_metaworld_mp_wrapper import BaseMetaworldMPWrapper
class MPWrapper(RawInterfaceWrapper):
class MPWrapper(BaseMetaworldMPWrapper):
"""
This Wrapper is for environments where merely the goal changes in the beginning
and no secondary objects or end effectors are altered at the start of an episode.
@ -49,20 +47,3 @@ class MPWrapper(RawInterfaceWrapper):
# Goal
[True] * 3, # goal position
])
@property
def current_pos(self) -> Union[float, int, np.ndarray]:
r_close = self.env.data.get_joint_qpos("r_close")
return np.hstack([self.env.data.mocap_pos.flatten() / self.env.action_scale, r_close])
@property
def current_vel(self) -> Union[float, int, np.ndarray, Tuple]:
raise NotImplementedError("Velocity cannot be retrieved.")
@property
def goal_pos(self) -> Union[float, int, np.ndarray, Tuple]:
raise ValueError("Goal position is not available and has to be learnt based on the environment.")
@property
def dt(self) -> Union[float, int]:
return self.env.dt

14
alr_envs/utils/__init__.py
View File

@ -20,7 +20,7 @@ def make_dmc(
environment_kwargs: dict = {},
time_limit: Union[None, float] = None,
channels_first: bool = True
):
):
# Adopted from: https://github.com/denisyarats/dmc2gym/blob/master/dmc2gym/__init__.py
# License: MIT
# Copyright (c) 2020 Denis Yarats
@ -32,12 +32,10 @@ def make_dmc(
env_id = f'dmc_{domain_name}_{task_name}_{seed}-v1'
if from_pixels:
assert not visualize_reward, 'cannot use visualize reward when learning from pixels'
assert not visualize_reward, 'Cannot use visualize reward when learning from pixels.'
# shorten episode length
if episode_length is None:
# Default lengths for benchmarking suite is 1000 and for manipulation tasks 250
episode_length = 250 if domain_name == "manipulation" else 1000
# Default lengths for benchmarking suite is 1000 and for manipulation tasks 250
episode_length = episode_length or (250 if domain_name == "manipulation" else 1000)
max_episode_steps = (episode_length + frame_skip - 1) // frame_skip
if env_id not in gym.envs.registry.env_specs:
@ -61,7 +59,7 @@ def make_dmc(
camera_id=camera_id,
frame_skip=frame_skip,
channels_first=channels_first,
),
),
max_episode_steps=max_episode_steps,
)
)
return gym.make(env_id)

42
alr_envs/utils/make_env_helpers.py
View File

@ -8,7 +8,7 @@ from gym.envs.registration import EnvSpec, registry
from gym.wrappers import TimeAwareObservation
from alr_envs.black_box.black_box_wrapper import BlackBoxWrapper
from alr_envs.black_box.controller.controller_factory import get_controller
from alr_envs.black_box.factory.controller_factory import get_controller
from alr_envs.black_box.factory.basis_generator_factory import get_basis_generator
from alr_envs.black_box.factory.phase_generator_factory import get_phase_generator
from alr_envs.black_box.factory.trajectory_generator_factory import get_trajectory_generator
@ -43,11 +43,7 @@ def make_rank(env_id: str, seed: int, rank: int = 0, return_callable=True, **kwa
def make(env_id, seed, **kwargs):
# This access is required to allow for nested dict updates
spec = registry.get(env_id)
all_kwargs = deepcopy(spec.kwargs)
nested_update(all_kwargs, kwargs)
return _make(env_id, seed, **all_kwargs)
return _make(env_id, seed, **kwargs)
def _make(env_id: str, seed, **kwargs):
@ -62,12 +58,25 @@ def _make(env_id: str, seed, **kwargs):
Returns: Gym environment
"""
if any(deprec in env_id for deprec in ["DetPMP", "detpmp"]):
warnings.warn("DetPMP is deprecated and converted to ProMP")
env_id = env_id.replace("DetPMP", "ProMP")
env_id = env_id.replace("detpmp", "promp")
# 'dmc:domain-task'
# 'gym:name-vX'
# 'meta:name-vX'
# 'meta:bb:name-vX'
# 'hand:name-vX'
# 'name-vX'
# 'bb:name-vX'
#
# env_id.split(':')
# if 'dmc' :
try:
# This access is required to allow for nested dict updates for BB envs
spec = registry.get(env_id)
all_kwargs = deepcopy(spec.kwargs)
nested_update(all_kwargs, kwargs)
kwargs = all_kwargs
# Add seed to kwargs in case it is a predefined gym+dmc hybrid environment.
if env_id.startswith("dmc"):
kwargs.update({"seed": seed})
@ -77,22 +86,25 @@ def _make(env_id: str, seed, **kwargs):
env.seed(seed)
env.action_space.seed(seed)
env.observation_space.seed(seed)
except gym.error.Error:
except (gym.error.Error, AttributeError):
# MetaWorld env
import metaworld
if env_id in metaworld.ML1.ENV_NAMES:
env = metaworld.envs.ALL_V2_ENVIRONMENTS_GOAL_OBSERVABLE[env_id + "-goal-observable"](seed=seed, **kwargs)
# setting this avoids generating the same initialization after each reset
env._freeze_rand_vec = False
env.seeded_rand_vec = True
# Manually set spec, as metaworld environments are not registered via gym
env.unwrapped.spec = EnvSpec(env_id)
# Set Timelimit based on the maximum allowed path length of the environment
env = gym.wrappers.TimeLimit(env, max_episode_steps=env.max_path_length)
env.seed(seed)
env.action_space.seed(seed)
env.observation_space.seed(seed)
env.goal_space.seed(seed)
# env.seed(seed)
# env.action_space.seed(seed)
# env.observation_space.seed(seed)
# env.goal_space.seed(seed)
else:
# DMC

26
setup.py
View File

@ -1,10 +1,10 @@
import itertools
from setuptools import setup
from setuptools import setup, find_packages
# Environment-specific dependencies for dmc and metaworld
extras = {
"dmc": ["dm_control"],
"dmc": ["dm_control==1.0.1"],
"meta": ["metaworld @ git+https://github.com/rlworkgroup/metaworld.git@master#egg=metaworld"],
"mujoco": ["mujoco==2.2.0", "imageio>=2.14.1"],
}
@ -16,12 +16,28 @@ extras["all"] = list(set(itertools.chain.from_iterable(map(lambda group: extras[
setup(
author='Fabian Otto, Onur Celik, Marcel Sandermann, Maximilian Huettenrauch',
name='simple_gym',
version='0.0.1',
packages=['alr_envs', 'alr_envs.alr', 'alr_envs.open_ai', 'alr_envs.dmc', 'alr_envs.meta', 'alr_envs.utils'],
version='0.1',
classifiers=[
# Python 3.6 is minimally supported (only with basic gym environments and API)
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.7",
"Programming Language :: Python :: 3.8",
"Programming Language :: Python :: 3.9",
"Programming Language :: Python :: 3.10",
],
extras_require=extras,
install_requires=[
'gym',
'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.alr', 'alr_envs.open_ai', 'alr_envs.dmc', 'alr_envs.meta', 'alr_envs.utils'],
package_data={
"alr_envs": [
"alr/mujoco/*/assets/*.xml",
]
},
python_requires=">=3.6",
url='https://github.com/ALRhub/alr_envs/',
# license='AGPL-3.0 license',
author_email='',

9
test/test_envs.py → test/test_bb_envs.py
View File

@ -34,12 +34,7 @@ class TestMPEnvironments(unittest.TestCase):
obs = env.reset()
self._verify_observations(obs, env.observation_space, "reset()")
length = env.spec.max_episode_steps
if iterations is None:
if length is None:
iterations = 1
else:
iterations = length
iterations = iterations or (env.spec.max_episode_steps or 1)
# number of samples(multiple environment steps)
for i in range(iterations):
@ -76,7 +71,7 @@ class TestMPEnvironments(unittest.TestCase):
traj2 = self._run_env(env_id, seed=seed)
for i, time_step in enumerate(zip(*traj1, *traj2)):
obs1, rwd1, done1, obs2, rwd2, done2 = time_step
self.assertTrue(np.array_equal(obs1, obs2), f"Observations [{i}] {obs1} and {obs2} do not match.")
self.assertTrue(np.allclose(obs1, obs2), f"Observations [{i}] {obs1} and {obs2} do not match.")
self.assertEqual(rwd1, rwd2, f"Rewards [{i}] {rwd1} and {rwd2} do not match.")
self.assertEqual(done1, done2, f"Dones [{i}] {done1} and {done2} do not match.")

7
test/test_dmc_envs.py
View File

@ -36,12 +36,7 @@ class TestStepDMCEnvironments(unittest.TestCase):
obs = env.reset()
self._verify_observations(obs, env.observation_space, "reset()")
length = env.spec.max_episode_steps
if iterations is None:
if length is None:
iterations = 1
else:
iterations = length
iterations = iterations or (env.spec.max_episode_steps or 1)
# number of samples(multiple environment steps)
for i in range(iterations):

7
test/test_metaworld_envs.py
View File

@ -35,12 +35,7 @@ class TestStepMetaWorlEnvironments(unittest.TestCase):
obs = env.reset()
self._verify_observations(obs, env.observation_space, "reset()")
length = env.max_path_length
if iterations is None:
if length is None:
iterations = 1
else:
iterations = length
iterations = iterations or (env.spec.max_episode_steps or 1)
# number of samples(multiple environment steps)
for i in range(iterations):