Diffusion Stochastic Learning over Multi-Team Network Games

This work addresses stochastic multi-network competing problems. Unlike prior studies that focus exclusively on the two-team (or two-network) case, we consider the more general setting with T ≥ 2 teams. In the multi-team setting, each team consists of a set of partially connected cooperative agents, and there exists at least one link between any two teams to enable information exchange. We propose a new algorithm based on a diffusion learning strategy, which subsumes the existing two-team competing algorithm as a special case. We show that the state vectors generated by the learning algorithm asymptotically converge to a neighborhood of the Nash equilibrium in the sense of first-, second-, and fourth-order error moments. The theoretical claims are supported by simulations on a quadratic game.