NDLI: Integral temporal difference learning for continuous-time linear quadratic regulations

Content Provider	Springer Nature Link
Author	Chun, Tae Yoon Lee, Jae Young Park, Jin Bae Choi, Yoon Ho
Copyright Year	2017
Abstract	In this paper, we propose a temporal difference (TD) learning method, called integral TD learning that efficiently finds solutions to continuous-time (CT) linear quadratic regulation (LQR) problems in an online fashion where system matrix A is unknown. The idea originates from a computational reinforcement learning method known as TD(0), which is the simplest TD method in a finite Markov decision process. For the proposed integral TD method, we mathematically analyze the positive definiteness of the updated value functions, monotone convergence conditions, and stability properties concerning the locations of the closed-loop poles in terms of the learning rate and the discount factor. The proposed method includes the existing value iteration method for CT LQR problems as a special case. Finally, numerical simulations are carried out to verify the effectiveness of the proposed method and further investigate the aforementioned mathematical properties.
Starting Page	226
Ending Page	238
Page Count	13
File Format	PDF
ISSN	15986446
Journal	International Journal of Control, Automation and Systems
Volume Number	15
Issue Number	1
e-ISSN	20054092
Language	English
Publisher	Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers
Publisher Date	2017-01-19
Publisher Place	Bucheon, Seoul
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Adaptive optimal control linear quadratic regulation reinforcement learning temporal difference value iteration Control, Robotics, Mechatronics
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Computer Science Applications

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