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

作者：

Highlights：

• 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.

摘要

•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.

论文关键词：Reinforcement learning,Markov jump linear systems,Multiplayer non-zero sum games,Coupled algebraic Riccati equations

论文评审过程：Received 1 November 2020, Revised 28 June 2021, Accepted 14 July 2021, Available online 11 August 2021, Version of Record 11 August 2021.

论文官网地址：https://doi.org/10.1016/j.amc.2021.126537