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arXiv · 2510.11474

Coordinated Strategies in Realistic Air Combat by Hierarchical Multi-Agent Reinforcement Learning

Ardian Selmonaj·Giacomo Del Rio·Adrian Schneider·Alessandro Antonucci
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Abstract

Achieving mission objectives in a realistic simulation of aerial combat is highly challenging due to imperfect situational awareness and nonlinear flight dynamics. In this work, we introduce a novel 3D multi-agent air combat environment and a Hierarchical Multi-Agent Reinforcement Learning framework to tackle these challenges. Our approach combines heterogeneous agent dynamics, curriculum learning, league-play, and a newly adapted training algorithm. To this end, the decision-making process is organized into two abstraction levels: low-level policies learn precise control maneuvers, while high-level policies issue tactical commands based on mission objectives. Empirical results show that our hierarchical approach improves both learning efficiency and combat performance in complex dogfight scenarios.

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Ardian Selmonaj, Giacomo Del Rio, Adrian Schneider, Alessandro Antonucci. 2025-10-13. Coordinated Strategies in Realistic Air Combat by Hierarchical Multi-Agent Reinforcement Learning. https://arxiv.org/abs/2510.11474

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