Please use this identifier to cite or link to this item: https://scidar.kg.ac.rs/handle/123456789/13467
Title: Online reinforcement learning multiplayer non-zero sum games of continuous-time Markov jump linear systems
Authors: Xin X.
Tu Y.
Stojanović, Vladimir
Wang H.
Shi K.
He Z.
pan, sudip
Issue Date: 2022
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.
URI: https://scidar.kg.ac.rs/handle/123456789/13467
Type: article
DOI: 10.1016/j.amc.2021.126537
ISSN: 0096-3003
SCOPUS: 2-s2.0-85112302552
Appears in Collections:Faculty of Mechanical and Civil Engineering, Kraljevo

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