Asynchronous Networked Aggregative Games

We propose a fully asynchronous networked aggregative game (Asy-NAG) where each player minimizes local cost function that depends on its own action and the aggregate of all players’ actions. In sharp contrast to existing NAGs, in our Asy-NAG can compute an estimate at any wall-clock time by only using (possibly stale) information from neighboring players directed network. Such update does not require coordination among players. Moreover, we design novel distributed algorithm with aggressive mechanism for adaptively adjust optimization stepsize per update. Particularly, slow terms updating their estimates smartly increase stepsizes catch up fast ones. Then, adopt augmented network approach address asynchronicity delays between players, rigorously show convergence Nash equilibrium via perturbed coordinate which is also independent interest. Finally, evaluate performance through numerical simulations.