Adaptive Multi-Step Evaluation Design With Stability Guarantee for Discrete-Time Optimal Learning Control

This paper is concerned with a novel integrated multi-step heuristic dynamic programming (MsHDP) algorithm for solving optimal control problems. It is shown that, initialized by the zero cost function, MsHDP can converge to the optimal solution of the Hamilton-Jacobi-Bellman (HJB) equation. Then, th...

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Published in:	IEEE/CAA journal of automatica sinica Vol. 10; no. 9; pp. 1797 - 1809
Main Authors:	Wang, Ding, Wang, Jiangyu, Zhao, Mingming, Xin, Peng, Qiao, Junfei
Format:	Journal Article
Language:	English
Published:	Piscataway Chinese Association of Automation (CAA) 01.09.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Faculty of Information Technology,the Beijing Key Laboratory of Computational Intelligence and Intelligent System,the Beijing Laboratory of Smart Environmental Protection,and the Beijing Institute of Artificial Intelligence,Beijing University of Technology,Beijing 100124,China
Subjects:	Adaptive control Adaptive critic Algorithms artificial neural networks Convergence Cost function Dynamic programming Hamilton-Jacobi-Bellman (HJB) equation Heuristic algorithms Iterative methods Mathematical models multi-step heuristic dynamic programming multi-step reinforcement learning Neural networks Observers Optimal control Policies Stability analysis Stability criteria Time optimal control artificial neural networks Adaptive critic optimal control Hamilton-Jacobi-Bellman(HJB)equation multi-step heuristic dynamic pro-gramming multi-step reinforcement learning
ISSN:	2329-9266, 2329-9274
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Abstract	This paper is concerned with a novel integrated multi-step heuristic dynamic programming (MsHDP) algorithm for solving optimal control problems. It is shown that, initialized by the zero cost function, MsHDP can converge to the optimal solution of the Hamilton-Jacobi-Bellman (HJB) equation. Then, the stability of the system is analyzed using control policies generated by MsHDP.Also, a general stability criterion is designed to determine the admissibility of the current control policy. That is, the criterion is applicable not only to traditional value iteration and policy iteration but also to MsHDP. Further, based on the convergence and the stability criterion, the integrated MsHDP algorithm using immature control policies is developed to accelerate learning efficiency greatly. Besides, actor-critic is utilized to implement the integrated MsHDP scheme, where neural networks are used to evaluate and improve the iterative policy as the parameter architecture. Finally, two simulation examples are given to demonstrate that the learning effectiveness of the integrated MsHDP scheme surpasses those of other fixed or integrated methods.
AbstractList	This paper is concerned with a novel integrated multi-step heuristic dynamic programming (MsHDP) algorithm for solving optimal control problems. It is shown that, initialized by the zero cost function, MsHDP can converge to the optimal solution of the Hamilton-Jacobi-Bellman (HJB) equation. Then, the stability of the system is analyzed using control policies generated by MsHDP.Also, a general stability criterion is designed to determine the admissibility of the current control policy. That is, the criterion is applicable not only to traditional value iteration and policy iteration but also to MsHDP. Further, based on the convergence and the stability criterion, the integrated MsHDP algorithm using immature control policies is developed to accelerate learning efficiency greatly. Besides, actor-critic is utilized to implement the integrated MsHDP scheme, where neural networks are used to evaluate and improve the iterative policy as the parameter architecture. Finally, two simulation examples are given to demonstrate that the learning effectiveness of the integrated MsHDP scheme surpasses those of other fixed or integrated methods. This paper is concerned with a novel integrated multi-step heuristic dynamic programming(MsHDP)algorithm for solving optimal control problems.It is shown that,initialized by the zero cost function,MsHDP can converge to the optimal solution of the Hamilton-Jacobi-Bellman(HJB)equation.Then,the stability of the system is analyzed using control policies gener-ated by MsHDP.Also,a general stability criterion is designed to determine the admissibility of the current control policy.That is,the criterion is applicable not only to traditional value iteration and policy iteration but also to MsHDP.Further,based on the convergence and the stability criterion,the integrated MsHDP algorithm using immature control policies is developed to accel-erate learning efficiency greatly.Besides,actor-critic is utilized to implement the integrated MsHDP scheme,where neural net-works are used to evaluate and improve the iterative policy as the parameter architecture.Finally,two simulation examples are given to demonstrate that the learning effectiveness of the inte-grated MsHDP scheme surpasses those of other fixed or inte-grated methods.
Author	Qiao, Junfei Xin, Peng Wang, Ding Zhao, Mingming Wang, Jiangyu
AuthorAffiliation	Faculty of Information Technology,the Beijing Key Laboratory of Computational Intelligence and Intelligent System,the Beijing Laboratory of Smart Environmental Protection,and the Beijing Institute of Artificial Intelligence,Beijing University of Technology,Beijing 100124,China
AuthorAffiliation_xml	– name: Faculty of Information Technology,the Beijing Key Laboratory of Computational Intelligence and Intelligent System,the Beijing Laboratory of Smart Environmental Protection,and the Beijing Institute of Artificial Intelligence,Beijing University of Technology,Beijing 100124,China
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References	ref35 wang (ref25) 2020; 105 ref12 ref34 ref37 ref14 ref36 ref31 ref30 ref11 ref33 ref10 ref32 ref2 sutton (ref41) 1998 ref39 ref38 ref19 ref18 bertsekas (ref16) 1995; 1 ref24 ref23 ref26 ref20 silver (ref1) 2016; 529 mnih (ref22) 0; 48 ref21 ref43 werbos (ref15) 1992 ref28 powell (ref13) 2007 ref27 ref29 ref8 ref7 ref9 ref4 ref3 ref6 ref5 ref40 landelius (ref42) 1997 sutton (ref17) 1998
References_xml	– volume: 1 year: 1995 ident: ref16 publication-title: Dynamic Programming and Optimal Control Chapter – ident: ref28 doi: 10.1109/TAC.2019.2926167 – ident: ref2 doi: 10.1109/MCI.2019.2919363 – ident: ref32 doi: 10.1109/TSMC.2015.2417510 – ident: ref39 doi: 10.1109/TAC.2018.2790260 – year: 1998 ident: ref17 publication-title: Reinforcement Learning – ident: ref31 doi: 10.1109/TNNLS.2013.2281663 – ident: ref12 doi: 10.1109/TNNLS.2022.3178746 – ident: ref33 doi: 10.1109/JAS.2021.1003814 – volume: 48 start-page: 1928 year: 0 ident: ref22 article-title: Asynchronous methods for deep reinforcement learning publication-title: Proc Int Conf Machine Learning – ident: ref3 doi: 10.1109/TCYB.2021.3121542 – start-page: 163 year: 1998 ident: ref41 article-title: Eligibility traces publication-title: Reinforcement Learning An Introduction – ident: ref34 doi: 10.1109/TCYB.2015.2492242 – ident: ref10 doi: 10.1109/TNNLS.2021.3123361 – volume: 529 start-page: 484 year: 2016 ident: ref1 article-title: Mastering the game of go with deep neural networks and tree search publication-title: Nature doi: 10.1038/nature16961 – ident: ref14 doi: 10.1109/72.623201 – ident: ref4 doi: 10.1109/TIE.2017.2698377 – ident: ref8 doi: 10.1109/JAS.2022.105692 – ident: ref27 doi: 10.1109/TSMCB.2008.926614 – ident: ref11 doi: 10.1109/TAC.2019.2905215 – ident: ref43 doi: 10.1016/j.epsr.2004.06.011 – ident: ref5 doi: 10.1109/TPWRS.2018.2823641 – ident: ref18 doi: 10.1109/72.914523 – year: 1997 ident: ref42 article-title: Reinforcement learning and distributed local model synthesis publication-title: Linkoping Univ Linkoping – ident: ref9 doi: 10.1109/TNNLS.2021.3051030 – ident: ref21 doi: 10.1109/TSMC.2017.2771516 – ident: ref35 doi: 10.1109/MCAS.2009.933854 – ident: ref29 doi: 10.1109/TNNLS.2022.3143527 – year: 2007 ident: ref13 publication-title: Approximate Dynamic Programming Solving the Curses of Dimensionality doi: 10.1002/9780470182963 – ident: ref24 doi: 10.1109/TCYB.2021.3107801 – year: 1992 ident: ref15 article-title: Approximate dynamic programming for real-time control and neural modeling publication-title: Handbook of Intelligent Control Neural Fuzzy and Adaptive Approaches – volume: 105 start-page: 104443:1 year: 2020 ident: ref25 article-title: Adaptive-critic-based hybrid intelligent optimal tracking for a class of nonlinear discrete-time systems publication-title: Engineering Applications of Artificial Intelligence – ident: ref7 doi: 10.1007/s10462-021-10118-9 – ident: ref37 doi: 10.1109/TNNLS.2022.3152268 – ident: ref23 doi: 10.1109/TNNLS.2020.2965208 – ident: ref30 doi: 10.1109/TSMC.2019.2898389 – ident: ref6 doi: 10.1109/TPWRS.2021.3100898 – ident: ref19 doi: 10.1109/TNNLS.2019.2919614 – ident: ref36 doi: 10.1109/TNN.2011.2168538 – ident: ref38 doi: 10.1016/j.neunet.2021.08.025 – ident: ref40 doi: 10.1109/TSMC.2021.3071968 – ident: ref26 doi: 10.1109/TAC.2021.3108121 – ident: ref20 doi: 10.1016/j.ins.2017.05.005
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SubjectTerms	Adaptive control Adaptive critic Algorithms artificial neural networks Convergence Cost function Dynamic programming Hamilton-Jacobi-Bellman (HJB) equation Heuristic algorithms Iterative methods Mathematical models multi-step heuristic dynamic programming multi-step reinforcement learning Neural networks Observers Optimal control Policies Stability analysis Stability criteria Time optimal control
Title	Adaptive Multi-Step Evaluation Design With Stability Guarantee for Discrete-Time Optimal Learning Control
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