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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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alr_envs/alr/__init__.py
+58 -80
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,
alr_envs/alr/mujoco/__init__.py
+2 -1
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
alr_envs/alr/mujoco/beerpong/mp_wrapper.py
+5 -4
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]]:
alr_envs/alr/mujoco/half_cheetah_jump/half_cheetah_jump.py
+1 -1
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
alr_envs/alr/mujoco/hopper_jump/__init__.py
-1
View File
@@ -1,2 +1 @@
from .mp_wrapper import MPWrapper
alr_envs/alr/mujoco/hopper_jump/hopper_jump.py
+19 -15
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
alr_envs/alr/mujoco/hopper_jump/hopper_jump_on_box.py
+2 -2
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):
alr_envs/alr/mujoco/hopper_jump/mp_wrapper.py
+2 -1
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