| Abstract: | In this article, a novel off‐policy cooperative game Q‐learning algorithm is proposed for achieving optimal tracking control of linear discrete‐time multiplayer systems suffering from exogenous dynamic disturbance. The key strategy, for the first time, is to integrate reinforcement learning, cooperative games with output regulation under the discrete‐time sampling framework for achieving data‐driven optimal tracking control and disturbance rejection. Without the information of state and input matrices of multiplayer systems, as well as the dynamics of exogenous disturbance and command generator, the coordination equilibrium solution and the steady‐state control laws are learned using data by a novel off‐policy Q‐learning approach, such that multiplayer systems have the capability of tolerating disturbance and follow the reference signal via the optimal approach. Moreover, the rigorous theoretical proofs of unbiasedness of coordination equilibrium solution and convergence of the proposed algorithm are presented. Simulation results are given to show the efficacy of the developed approach. |
