Journal article
Online reinforcement learning multiplayer non-zero sum games of continuous-time Markov jump linear systems
Applied Mathematics and Computation, Vol.412, Art. 126537
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 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.
Details
- Title
- Online reinforcement learning multiplayer non-zero sum games of continuous-time Markov jump linear systems
- Authors/Creators
- X. Xin (Author/Creator) - Anhui UniversityY. Tu (Author/Creator) - Anhui UniversityV. Stojanovic (Author/Creator) - University of KragujevacH. Wang (Author/Creator) - Murdoch UniversityK. Shi (Author/Creator) - Chengdu UniversityS. He (Author/Creator) - Anhui UniversityT. Pan (Author/Creator) - Anhui University
- Publication Details
- Applied Mathematics and Computation, Vol.412, Art. 126537
- Publisher
- Elsevier
- Identifiers
- 991005544197007891
- Copyright
- © 2021 Elsevier Inc.
- Murdoch Affiliation
- School of Engineering and Energy
- Language
- English
- Resource Type
- Journal article
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Highly Cited Paper
- Collaboration types
- Domestic collaboration
- International collaboration
- Citation topics
- 4 Electrical Engineering, Electronics & Computer Science
- 4.116 Robotics
- 4.116.862 Reinforcement Learning
- Web Of Science research areas
- Mathematics, Applied
- ESI research areas
- Mathematics