About me
I received the Ph. D. degree in Computer Application Technology from the University of Chinese Academy of Sciences, Beijing, China. My research interests include optimal control, adaptive dynamic programming (ADP), deep reinforcement learning (DRL), and autonomous driving.
Currently, I’m highly interested in pre-trained-models-powered artificial intelligence generated content (AIGC) for optimal control and DRL, and their applications in autonomous driving and industrial systems.
I’m actively involved in academic services. I have been serving as a reviewer for various intenational journals and conferences. I was a Guest Editor of the IEEE Journal of Radio Frequency Identification.
At present, I am dedicated to open-sourcing nonlinear optimal control algorithms, especially ADP and reinforcement learning (ADPRL), and further establishing a tool library of nonlinear optimal control. Please refer to my Repo: ADPRL for nonlinear optimal control.
Please feel free to contact me at: lujingweihh@gmail.com/jingweilu@ieee.org
Updates
- 03/2024: Our paper “Event-triggered parallel control using deep reinforcement learning with application to comfortable autonomous driving” has been accepted by the IEEE Transactions on Intelligent Vehicles.
- 02/2024: Our paper “Nearly optimal stabilization of unknown continuous-time nonlinear systems: A new parallel control approach” has been accepted by the Neurocomputing.
- 06/2023: Our paper “Continuous-time stochastic policy iteration adaptive dynamic programming” has been accepted by the IEEE Transactions on Systems, Man, and Cybernetics: Systems.