Competitive Multi-Team Behavior in Dynamic Flight Scenarios

Efficiently learning strategic multi-agent behavior remains a challenge for robotic systems deployed in real-world scenarios, especially when considering underactuated or dynamically unstable systems. Such systems demand an integrated approach that informs long-term strategic planning with constraints imposed by reactive control, and vice versa, to effectively accomplish task objectives in competitive scenarios. In this paper, we introduce a hierarchical control model to address this: a high-level controller synthesizes strategic guidance from aggregated team experiences, while a low-level controller formulates corresponding task-specific continuous controls. We apply this concept to coordination of competitive multi-team behavior in dynamic flight scenarios with F-16 aircraft. This work introduces a hierarchical reinforcement learning approach for multi-agent coordination, leveraging decoupled distributional value representations at the high-level together with goal-conditioned policy learning at the low-level, providing a control structure that integrates long-horizon strategic planning with short-horizon dynamic control. We further provide a parallel simulator for efficient learning with multi-agent F-16 dynamics.