Distributed Optimization for Multiplayer Non-Zero-Sum Games
Dezheng Wang, Bin Zhang · 2023
In this paper, a data-driven distributed optimization method for multiplayer nonzerosum (NZS) differential games is presented for multiplayer of nonlinear affine systems. We identify the differential games as a consensus optimization problem with test data. We adopt general basis functions to approximate the strategy functions and cost functions. The distributed optimization algorithm, which is widely researched in machine learning, is applied to obtain the solution to the consensus optimization problem. Finally, we choose two numerical examples to prove the validity of the method.