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holereacher pmp updates

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This commit is contained in:
Maximilian Huettenrauch 2021-04-07 18:18:51 +02:00
parent a3d365033a
commit bafa28edca
3 changed files with 18 additions and 16 deletions
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3
alr_envs/classic_control/hole_reacher.py
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@ -99,7 +99,10 @@ class HoleReacher(gym.Env):
if self._steps == 199:
reward = - np.linalg.norm(self.end_effector - self.bottom_center_of_hole) ** 2
else:
if self.collision_penalty != 0:
reward = -self.collision_penalty
else:
reward = - np.linalg.norm(self.end_effector - self.bottom_center_of_hole) ** 2
reward -= 5e-8 * np.sum(acc ** 2)

6
alr_envs/classic_control/utils.py
View File

@ -134,18 +134,18 @@ def make_holereacher_env_pmp(rank, seed=0):
hole_width=0.15,
hole_depth=1,
hole_x=1,
collision_penalty=1000)
collision_penalty=100)
_env = DetPMPEnvWrapper(_env,
num_dof=5,
num_basis=5,
width=0.005,
width=0.025,
policy_type="velocity",
start_pos=_env.start_pos,
duration=2,
post_traj_time=0,
dt=_env.dt,
weights_scale=0.25,
weights_scale=0.2,
zero_start=True,
zero_goal=False
)

23
dmp_env_wrapper_example.py
View File

@ -1,5 +1,5 @@
from alr_envs.classic_control.utils import make_viapointreacher_env
from alr_envs.classic_control.utils import make_holereacher_env, make_holereacher_fix_goal_env
from alr_envs.classic_control.utils import make_holereacher_env, make_holereacher_fix_goal_env, make_holereacher_env_pmp
from alr_envs.utils.dmp_async_vec_env import DmpAsyncVectorEnv
import numpy as np
@ -8,22 +8,21 @@ if __name__ == "__main__":
n_samples = 1
n_cpus = 4
dim = 30
dim = 15
# env = DmpAsyncVectorEnv([make_viapointreacher_env(i) for i in range(n_cpus)],
# n_samples=n_samples)
test_env = make_holereacher_env(0)()
test_env = make_holereacher_env_pmp(0)()
# params = np.random.randn(n_samples, dim)
params = np.array([ 0.57622273, 0.98294602, 1.48964131, 0.65430972,
-0.26028221, 4.84693322, 1.77366128, 0.51080511,
-2.38201107, -0.84990048, 1.02289828, 1.20675551,
0.38075566, -1.84282938, -3.48690172, 2.17434711,
-1.79285349, -1.7533641 , 0.62802966, 1.18928357,
0.2818753 , -3.27708291, -0.91761804, -0.38350967,
2.25849139, 21.57786524, -14.38494647, -11.5380005 ,
-11.09529721, -0.39453533])
# params = 1 * np.random.randn(dim)
params = np.array([[ -0.13106822, -0.66268577, -1.37025136, -1.34813613,
-0.34040336, -1.41684643, 2.81882318, -1.93383471,
-5.84213385, -3.8623558 , -1.31946267, 3.19346678,
-9.6581148 , -8.27402906, -0.42374776, -2.06852054,
7.21224904, -6.81061422, -9.54973119, -6.18636867,
-6.82998929, 13.00398992, -18.28106949, -6.06678165,
2.79744735]])
# params = np.hstack([50 * np.random.randn(n_samples, 25), np.tile(np.array([np.pi/2, -np.pi/4, -np.pi/4, -np.pi/4, -np.pi/4]), [n_samples, 1])])