Stackelberg Game Theoretical Learning for Multi-Agent Formation Tracking Control

In this article, the formation tracking problem is discussed within a multi-agent framework. Drawing from the hierarchical model featuring a single leader and multiple followers, we use the Stackelberg game to model the system and prove the Stackelberg-Nash equilibrium exists. The solution of the preceding game is resolved by using the deep reinforcement learning algorithm and a generative communication strategy learning algorithm is proposed. The efficacy of the aforementioned methodology is validated through experimental simulations, substantiating its effectiveness.