Online reinforcement learning multiplayer non-zero sum games of continuous-time Markov jump linear systems

Xilin Xin, Yidong Tu, Vladimir Stojanovic, Hai Wang, Kaibo Shi, Shuping He and Tianhong Pan

Applied Mathematics and Computation, 2022, vol. 412, issue C

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.

Keywords: Reinforcement learning; Markov jump linear systems; Multiplayer non-zero sum games; Coupled algebraic Riccati equations (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:412:y:2022:i:c:s0096300321006214

DOI: 10.1016/j.amc.2021.126537

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