TY - GEN
T1 - DOPT
T2 - 2025 IEEE International Conference on Robotics and Automation, ICRA 2025
AU - Shen, Zhaolong
AU - Quan, Quan
N1 - Publisher Copyright:
© 2025 IEEE.
PY - 2025
Y1 - 2025
N2 - In recent times, Lyapunov theory has been in-corporated into learning-based control methods to provide a stability guarantee. However, merely satisfying the Lyapunov conditions does not fully leverage the capabilities of the Neural Network (NN) controller. Furthermore, training an effective Lyapunov candidate requires substantial data, which inherently results in sample inefficiency. To address these limitations, we propose an off-policy variant of the vanilla D-learning method that uses current and historical data to iteratively enhance the NN controller within the framework of Lyapunov theory. Our method outperforms the Deep Deterministic Policy Gradient (DDPG) and D-learning in terms of stability, sample efficiency, and the quality of the trained controllers and Lyapunov candidates. Link to code: github.com/Shenzhaolong1330/DOPT.
AB - In recent times, Lyapunov theory has been in-corporated into learning-based control methods to provide a stability guarantee. However, merely satisfying the Lyapunov conditions does not fully leverage the capabilities of the Neural Network (NN) controller. Furthermore, training an effective Lyapunov candidate requires substantial data, which inherently results in sample inefficiency. To address these limitations, we propose an off-policy variant of the vanilla D-learning method that uses current and historical data to iteratively enhance the NN controller within the framework of Lyapunov theory. Our method outperforms the Deep Deterministic Policy Gradient (DDPG) and D-learning in terms of stability, sample efficiency, and the quality of the trained controllers and Lyapunov candidates. Link to code: github.com/Shenzhaolong1330/DOPT.
UR - https://www.scopus.com/pages/publications/105016548509
U2 - 10.1109/ICRA55743.2025.11127827
DO - 10.1109/ICRA55743.2025.11127827
M3 - 会议稿件
AN - SCOPUS:105016548509
T3 - Proceedings - IEEE International Conference on Robotics and Automation
SP - 9637
EP - 9643
BT - 2025 IEEE International Conference on Robotics and Automation, ICRA 2025
A2 - Ott, Christian
A2 - Admoni, Henny
A2 - Behnke, Sven
A2 - Bogdan, Stjepan
A2 - Bolopion, Aude
A2 - Choi, Youngjin
A2 - Ficuciello, Fanny
A2 - Gans, Nicholas
A2 - Gosselin, Clement
A2 - Harada, Kensuke
A2 - Kayacan, Erdal
A2 - Kim, H. Jin
A2 - Leutenegger, Stefan
A2 - Liu, Zhe
A2 - Maiolino, Perla
A2 - Marques, Lino
A2 - Matsubara, Takamitsu
A2 - Mavromatti, Anastasia
A2 - Minor, Mark
A2 - O'Kane, Jason
A2 - Park, Hae Won
A2 - Park, Hae-Won
A2 - Rekleitis, Ioannis
A2 - Renda, Federico
A2 - Ricci, Elisa
A2 - Riek, Laurel D.
A2 - Sabattini, Lorenzo
A2 - Shen, Shaojie
A2 - Sun, Yu
A2 - Wieber, Pierre-Brice
A2 - Yamane, Katsu
A2 - Yu, Jingjin
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 19 May 2025 through 23 May 2025
ER -