Acting on the Tangent Space of the Constraint Manifold Implementation of "Robot Reinforcement Learning on the Constraint Manifold"
Install
pip install -e .
Run Examples
cd examples
CircularMotion Environment.
Environment options [A, E, T], algorithms options [TRPO, PPO, SAC, DDPG, TD3]
python circle_exp.py --render --env A --alg TRPO
PlanarAirHockey Environment.
Environment options [H, D, UH, UD], algorithms options [TRPO, PPO, SAC, DDPG, TD3]
python planar_air_hockey_exp.py --debug-gui --env H --alg SAC
IiwaAirHockey Environment.
Environment options [7H, RMP], algorithms options [TRPO, PPO, SAC, DDPG, TD3]
python iiwa_air_hockey_exp.py --debug-gui --env 7H --alg SAC
CollisionAvoidance Environment.
Environment options [C], algorithms options [TRPO, PPO, SAC, DDPG, TD3]
python collision_avoidance_exp.py --render --env C --alg SAC
Bibtex
@inproceedings{CORL_2021_Learning_on_the_Manifold,
author = "Liu, P. and Tateo D. and Bou-Ammar, H. and Peters, J.",
year = "2021",
title = "Robot Reinforcement Learning on the Constraint Manifold",
booktitle = "Proceedings of the Conference on Robot Learning (CoRL)",
key = "robot learning, constrained reinforcement learning, safe exploration",
}