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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Veröffentlicht in:Applied mathematics and computation Jg. 412; S. 126537
Hauptverfasser: Xin, Xilin, Tu, Yidong, Stojanovic, Vladimir, Wang, Hai, Shi, Kaibo, He, Shuping, Pan, Tianhong
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Inc 01.01.2022
Schlagworte:
Coupled algebraic Riccati equations
Markov jump linear systems
Multiplayer non-zero sum games
Reinforcement learning
Coupled algebraic Riccati equations
Markov jump linear systems
Multiplayer non-zero sum games
Reinforcement learning
ISSN:0096-3003, 1873-5649
Online-Zugang:Volltext
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Zusammenfassung:•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.
ISSN:0096-3003
1873-5649
DOI:10.1016/j.amc.2021.126537