Adaptive Distributed Formation Control for Multi-Group Large-Scale Multi-Agent Systems: A Hybrid Game Approach

Abstract

This paper presents a distributed adaptive formation control for large-scale multi-agent systems(LS-MAS) that addresses the “Curse of Dimensionality”. A novel hybrid game theoretic algorithm that effectively integrates the mean field, Stackelberg, and cooperative game seamlessly has been developed. In particular, LS-MAS has been separated into a multiple number of groups, each has one group leader and a significant amount of followers. Next, a cooperative game is utilized for the inter-group formation of leaders, the mean-field game is adopted for intra-group followers, and a Stackelberg game is connecting the leader and followers in same group. A hybrid reinforcement learning algorithm learns the solution of the hybrid game optimal distributed formation. It comprises a multi-actor-critic to obtain the optimal formation control among inter-group leaders in a distributed manner, and a Stackelberg game-based actor-critic-mass algorithm to obtain the followers’ adaptive formation control. Lastly, to demonstrate the efficacy of the proposed approaches, numerical simulations have been conducted.