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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