Reinforcement Learning for Linear Continuous-time Systems: an Incremental Learning Approach

Tao Bian; Zhong-Ping Jiang

doi:10.1109/JAS.2019.1911390

Volume 6 Issue 2

Mar. 2019

IEEE/CAA Journal of Automatica Sinica

JCR Impact Factor: 19.2, Top 1 (SCI Q1)

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Tao Bian and Zhong-Ping Jiang, "Reinforcement Learning for Linear Continuous-time Systems: an Incremental Learning Approach," IEEE/CAA J. Autom. Sinica, vol. 6, no. 2, pp. 433-440, Mar. 2019. doi: 10.1109/JAS.2019.1911390

Citation:

Tao Bian and Zhong-Ping Jiang, "Reinforcement Learning for Linear Continuous-time Systems: an Incremental Learning Approach," IEEE/CAA J. Autom. Sinica, vol. 6, no. 2, pp. 433-440, Mar. 2019. doi: 10.1109/JAS.2019.1911390

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Reinforcement Learning for Linear Continuous-time Systems: an Incremental Learning Approach

doi: 10.1109/JAS.2019.1911390

Tao Bian^{1
,
,},
Zhong-Ping Jiang^2
,

1.
Bank of America Merrill Lynch, One Bryant Park, New York, NY 10036 USA
2.
Control and Networks Lab, Department of Electrical and Computer Engineering, Tandon School of Engineering, New York University, 5 Metrotech Center, Brooklyn, NY 11201 USA

Funds:

the National Science Foundation ECCS-1230040

the National Science Foundation ECCS-1501044

More Information

Author Bio:
Tao Bian received the B.Eng. degree in automation from Huazhong University of Science and Technology, Wuhan, China, in 2012, and the M.Sc. and Ph.D. degrees in electrical engineering from New York University, NY, in 2014 and 2017, respectively. Currently, he is an Assistant Vice President; quantitative finance analyst at Bank of America Merrill Lynch.
His research interests include reinforcement learning, adaptive optimal control, and control of stochastic systems. Dr. Bian is the winner of the Steve and Rosalind Hsia Best Biomedical Paper Award (with his advisor Prof. Jiang) at the 2016 World Congress on Intelligent Control and Automation in Guilin, China

Zhong-Ping Jiang(F'08) received the B.Sc. degree in mathematics from the University of Wuhan, Wuhan, China, in 1988, the M.Sc. degree in statistics from the University of Paris XI, France, in 1989, and the Ph.D. degree in automatic control and mathematics from the Ecole des Mines de Paris (now, called Paris Tech-Mines), France, in 1993, under the direction of Prof. Laurent Praly.
Currently, he is a Professor of Electrical and Computer Engineering at the Tandon School of Engineering, New York University. His main research interests include stability theory, robust/adaptive/distributed nonlinear control, adaptive dynamic programming and their applications to information, mechanical and biological systems. He is coauthor of the books Stability and Stabilization of Nonlinear Systems (with Dr. I. Karafyllis, Springer, 2011), Nonlinear Control of Dynamic Networks (with Drs. T. Liu and D.J. Hill, Taylor & Francis, 2014), Robust Adaptive Dynamic Programming (with Y. Jiang, Wiley-IEEE Press, 2017), and Nonlinear Control Under Information Constraints (with T. Liu, Science Press, 2018). He was named a Highly Cited Researcher by Web of Science in 2018.
Dr. Jiang is a Deputy co-Editor-in-Chief of the Journal of Control and Decision, a Senior Editor of the IEEE Control Systems Letters and of the Systems and Control Letters, an Editor for the International Journal of Robust and Nonlinear Control and has served as an Associate Editor for several journals including Mathematics of Control, Signals and Systems(MCSS), Systems & Control Letters, IEEE Transactions on Automatic Control, European Journal of Control, and Science China: Information Sciences. Dr. Jiang is a recipient of the prestigious Queen Elizabeth Ⅱ Fellowship Award from the Australian Research Council (1998), the CAREER Award from the U.S. National Science Foundation (2001), JSPS Invitation Fellowship from the Japan Society for the Promotion of Science (2005), the Distinguished Overseas Chinese Scholar Award from the NSF of China (2007), and the Chair Professorship by the Ministry of Education of China (2009).
Prof. Jiang is a Fellow of the IEEE and a Fellow of the IFAC. (e-mail: zjiang@nyu.edu)
Corresponding author: Tao Bian, e-mail: tbian@nyu.edu
Received Date: 2018-10-08
Accepted Date: 2018-12-24

Abstract

Abstract

In this paper, we introduce a novel reinforcement learning (RL) scheme for linear continuous-time dynamical systems. Different from traditional batch learning algorithms, an incremental learning approach is developed, which provides a more efficient way to tackle the on-line learning problem in real-world applications. We provide concrete convergence and robust analysis on this incremental-learning algorithm. An extension to solving robust optimal control problems is also given. Two simulation examples are also given to illustrate the effectiveness of our theoretical result.
- Adaptive optimal control,
- robust dynamic programming,
- value iteration (Ⅵ)

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References

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Reinforcement Learning for Linear Continuous-time Systems: an Incremental Learning Approach

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