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

• DOI: 10.1016/j.amc.2021.126537
• Publication Date: 2021-08-11T10:34:50Z
• Authors: Xilin Xin; Yidong Tu; Vladimir Stojanovic; Hai Wang; Kaibo Shi; Shuping He; Tianhong Pan
• Keywords: Zero (linguistics); Algebraic Riccati equation; Zero-sum game; Q-learning

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.