Knowledge Commons of Institute of Automation,CAS
Off-Policy Integral Reinforcement Learning Method to Solve Nonlinear Continuous-Time Multiplayer Nonzero-Sum Games | |
Song, Ruizhuo1; Lewis, Frank L.2,3; Wei, Qinglai4 | |
发表期刊 | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS |
2017-03-01 | |
卷号 | 28期号:3页码:704-713 |
文章类型 | Article |
摘要 | This paper establishes an off-policy integral reinforcement learning (IRL) method to solve nonlinear continuous-time (CT) nonzero-sum (NZS) games with unknown system dynamics. The IRL algorithm is presented to obtain the iterative control and off-policy learning is used to allow the dynamics to be completely unknown. Off-policy IRL is designed to do policy evaluation and policy improvement in the policy iteration algorithm. Critic and action networks are used to obtain the performance index and control for each player. The gradient descent algorithm makes the update of critic and action weights simultaneously. The convergence analysis of the weights is given. The asymptotic stability of the closed-loop system and the existence of Nash equilibrium are proved. The simulation study demonstrates the effectiveness of the developed method for nonlinear CT NZS games with unknown system dynamics. |
关键词 | Adaptive Critic Designs Adaptive Dynamic Programming (Adp) Approximate Dynamic Programming Integral Reinforcement Learning (Irl) Nonlinear Systems Nonzero Sum (Nzs) Off-policy |
WOS标题词 | Science & Technology ; Technology |
DOI | 10.1109/TNNLS.2016.2582849 |
关键词[WOS] | OPTIMAL TRACKING CONTROL ; ADAPTIVE OPTIMAL-CONTROL ; H-INFINITY CONTROL ; DIFFERENTIAL-GAMES ; UNKNOWN DYNAMICS ; FEEDBACK-CONTROL ; LINEAR-SYSTEMS ; CONTROL DESIGN ; OUTPUT DATA ; ALGORITHM |
收录类别 | SCI |
语种 | 英语 |
项目资助者 | Directorate for Biological Sciences through the National Science Foundation(ECCS-1128050) ; National Natural Science Foundation of China(61304079 ; Fundamental Research Funds for the Central Universities(FRF-TP-15-056A3) ; State Key Laboratory of Management and Control for Complex Systems(20150104) ; Office of Naval Research(N00014-13-1-0562) ; Air Force Office of Scientific Research European Office of Aerospace Research and Development(13-3055) ; U.S. Army Research Office(W911NF-11-D-0001) ; China National Natural Science Foundation(61120106011) ; China Education Ministry Project 111(B08015) ; 61433004 ; 61374105) |
WOS研究方向 | Computer Science ; Engineering |
WOS类目 | Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic |
WOS记录号 | WOS:000395980500019 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/14397 |
专题 | 多模态人工智能系统全国重点实验室_复杂系统智能机理与平行控制团队 |
作者单位 | 1.Univ Sci & Technol Beijing, Sch Automat & Elect Engn, Beijing 100083, Peoples R China 2.Univ Texas Arlington, UTA Res Inst, Arlington, TX 76019 USA 3.Northeastern Univ, State Key Lab Synthet Automat Proc Ind, Shenyang 110819, Peoples R China 4.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China |
推荐引用方式 GB/T 7714 | Song, Ruizhuo,Lewis, Frank L.,Wei, Qinglai. Off-Policy Integral Reinforcement Learning Method to Solve Nonlinear Continuous-Time Multiplayer Nonzero-Sum Games[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2017,28(3):704-713. |
APA | Song, Ruizhuo,Lewis, Frank L.,&Wei, Qinglai.(2017).Off-Policy Integral Reinforcement Learning Method to Solve Nonlinear Continuous-Time Multiplayer Nonzero-Sum Games.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,28(3),704-713. |
MLA | Song, Ruizhuo,et al."Off-Policy Integral Reinforcement Learning Method to Solve Nonlinear Continuous-Time Multiplayer Nonzero-Sum Games".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 28.3(2017):704-713. |
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