Online reinforcement learning multiplayer non-zero sum games of continuous-time Markov jump linear systems - Xin, Xilin; Tu, Yidong; Stojanovic, Vladimir; Wang, Hai; Shi, Kaibo; He, Shuping; Pan, Tianhong (Applied mathematics and computation, 2022)
•A novel online mode-free integral reinforcement learning algorithm is proposed to solve the mutiplayer non-zero sum games.•The online learning is used to compute the corresponding N coupled algebraic Riccati equations.•The policy iterative algorithm is applied to solve the coupled algebraic Riccati equations corresponding to the multiplayer nonzero sum games. 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.