摘要
In this paper,the authors design a reinforcement learning algorithm to solve the adaptive linear-quadratic stochastic n-players non-zero sum differential game with completely unknown dynamics.For each player,a critic network is used to estimate the Q-function,and an actor network is used to estimate the control input.A model-free online Q-learning algorithm is obtained for solving this kind of problems.It is proved that under some mild conditions the system state and weight estimation errors can be uniformly ultimately bounded.A simulation with five players is given to verify the effectiveness of the algorithm.
基金
supported in part by the National Natural Science Foundation of China under Grant Nos.62122043,62192753
in part by Natural Science Foundation of Shandong Province for Distinguished Young Scholars under Grant No.ZR2022JQ31
in part by the Innovative Research Groups of the National Natural Science Foundation of China under Grant No.61821004.
