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• W3191504080 abstract "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." @default.
• W3191504080 created "2021-08-16" @default.
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• W3191504080 date "2022-01-01" @default.
• W3191504080 modified "2023-10-14" @default.
• W3191504080 title "Online reinforcement learning multiplayer non-zero sum games of continuous-time Markov jump linear systems" @default.
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• W3191504080 doi "https://doi.org/10.1016/j.amc.2021.126537" @default.
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