Online reinforcement learning multiplayer non-zero sum games of continuous-time Markov jump linear systems
•A novel online mode-free integral reinforcement learning algorithm is proposed to solve the mutiplayer non-zero sum games.•The online learning is used to compute the corresponding N coupled algebraic Riccati equations.•The policy iterative algorithm is applied to solve the coupled algebraic Riccati...
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| Published in: | Applied mathematics and computation Vol. 412; p. 126537 |
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| Main Authors: | , , , , , , |
| Format: | Journal Article |
| Language: | English |
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01.01.2022
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| ISSN: | 0096-3003, 1873-5649 |
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| Abstract | •A novel online mode-free integral reinforcement learning algorithm is proposed to solve the mutiplayer non-zero sum games.•The online learning is used to compute the corresponding N coupled algebraic Riccati equations.•The policy iterative algorithm is applied to solve the coupled algebraic Riccati equations corresponding to the multiplayer nonzero sum games.
In this paper, a novel online mode-free integral reinforcement learning algorithm is proposed to solve the multiplayer non-zero sum games. We first collect and learn the subsystems information of states and inputs; then we use the online learning to compute the corresponding N coupled algebraic Riccati equations. The policy iterative algorithm proposed in this paper can solve the coupled algebraic Riccati equations corresponding to the multiplayer non-zero sum games. Finally, the effectiveness and feasibility of the design method of this paper is proved by simulation example with three players. |
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| AbstractList | •A novel online mode-free integral reinforcement learning algorithm is proposed to solve the mutiplayer non-zero sum games.•The online learning is used to compute the corresponding N coupled algebraic Riccati equations.•The policy iterative algorithm is applied to solve the coupled algebraic Riccati equations corresponding to the multiplayer nonzero sum games.
In this paper, a novel online mode-free integral reinforcement learning algorithm is proposed to solve the multiplayer non-zero sum games. We first collect and learn the subsystems information of states and inputs; then we use the online learning to compute the corresponding N coupled algebraic Riccati equations. The policy iterative algorithm proposed in this paper can solve the coupled algebraic Riccati equations corresponding to the multiplayer non-zero sum games. Finally, the effectiveness and feasibility of the design method of this paper is proved by simulation example with three players. |
| ArticleNumber | 126537 |
| Author | He, Shuping Xin, Xilin Tu, Yidong Wang, Hai Pan, Tianhong Shi, Kaibo Stojanovic, Vladimir |
| Author_xml | – sequence: 1 givenname: Xilin surname: Xin fullname: Xin, Xilin organization: Key Laboratory of Intelligent Computing and Signal Processing (Ministry of Education), School of Electrical Engineering and Automation, Anhui University, Hefei 230601, China – sequence: 2 givenname: Yidong surname: Tu fullname: Tu, Yidong organization: Key Laboratory of Intelligent Computing and Signal Processing (Ministry of Education), School of Electrical Engineering and Automation, Anhui University, Hefei 230601, China – sequence: 3 givenname: Vladimir surname: Stojanovic fullname: Stojanovic, Vladimir organization: Department of Automatic Control, Robotics and Fluid Technique, Faculty of Mechanical and Civil Engineering, University of Kragujevac, Kraljevo 36000, Serbia – sequence: 4 givenname: Hai surname: Wang fullname: Wang, Hai organization: Discipline of Engineering and Energy, Murdoch University, 90 South Street, Murdoch, WA 6150, Australia – sequence: 5 givenname: Kaibo surname: Shi fullname: Shi, Kaibo organization: School of Electronic Information and Electrical Engineering, Chengdu University, Chengdu 610106, China – sequence: 6 givenname: Shuping surname: He fullname: He, Shuping email: shuping.he@ahu.edu.cn organization: Key Laboratory of Intelligent Computing and Signal Processing (Ministry of Education), School of Electrical Engineering and Automation, Anhui University, Hefei 230601, China – sequence: 7 givenname: Tianhong surname: Pan fullname: Pan, Tianhong email: hpan@ahu.edu.cn organization: Key Laboratory of Intelligent Computing and Signal Processing (Ministry of Education), School of Electrical Engineering and Automation, Anhui University, Hefei 230601, China |
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| Keywords | Coupled algebraic Riccati equations Markov jump linear systems Multiplayer non-zero sum games Reinforcement learning |
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| SubjectTerms | Coupled algebraic Riccati equations Markov jump linear systems Multiplayer non-zero sum games Reinforcement learning |
| Title | Online reinforcement learning multiplayer non-zero sum games of continuous-time Markov jump linear systems |
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