Deep Ad-hoc Sub-Team Partition Learning for Multi-Agent Air Combat Cooperation

Abstract

In the future, unmanned autonomous air combat will encounter large-scale confrontation scenarios, where agents must consider complex time-varying relationships among aircraft when making decisions. Previous works have already introduced Multi-Agent Reinforcement Learning (MARL) into air combat and succeeded in surpassing the human expert level. However, they mainly focus on small-scale air combat with low relationship complexity, e.g., 1-vs-1 or 2-vs-2. As more agents join the confrontation, existing algorithms tend to suffer significant performance degradation due to the increase in problem dimensions. In view of this, this paper proposes Deep Ad-hoc Sub-Team Partition Learning(DASPL) to address large-scale air combat problems. DASPL models multi-agent air combat as a graph to handle the complex relations and introduces an automatic partitioning mechanism to generate dynamic sub-teams, which converts the existing large-scale multi-agent air combat cooperation problem into multiple small-scale equivalence problems. Additionally, DASPL incorporates an efficient message passing method among the participating sub-teams.