DRG-MAPPO: Hierarchical Dynamic Role-Graph Multi-Agent Reinforcement Learning for Cooperative Air Combat

Researchers propose DRG-MAPPO, a hierarchical dynamic role-graph multi-agent reinforcement learning method aimed at improving tactical coordination for cooperative air combat. The work addresses the difficulty of achieving sophisticated tactical coordination in multi-agent reinforcement learning for autonomous air combat systems.

Multi-Agent Reinforcement Learning MARL has emerged as a pivotal paradigm for complex decision-making in autonomous systems and air combat. While MARL has demonstrated significant potential in air combat, achieving sophisticated tactical coordination remains a non-trivial challenge. This difficult