Complex relationship graph abstraction for autonomous air combat collaboration: A learning and expert knowledge hybrid approach

Abstract

Large-scale air combat is accompanied by complex relationships among the participants, e.g., siege, support. These relationships often present numerous, multi-relational, and high-order characteristics. However, previous studies have encountered significant difficulties in dissecting large-scale air confrontations with such complex relationships. In view of this, a novel Multi-Agent Deep Reinforcement Learning (MADRL) and expert knowledge hybrid algorithm named Transitive RelatIonShip graph reasOing for autoNomous aIr combat Collaboration (TRISONIC) is proposed, which solves the large-scale autonomous air combat problem with complex relationships. Specifically, TRISONIC creates a Graph Neural Networks (GNNs) and expert knowledge composite approach to jointly reason out the key relationships into an Abstract Relationship Graph (ARG). After this particular relationship simplification process, representative collaboration tactics emerged via subsequent intention communication and joint decision making mechanisms. Empirically, we demonstrate that the proposed method outperforms state-of-the-art algorithms with an at least 67.4% relative winning rate in a high-fidelity air combat simulation environment.