{"ID":2856395,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.11474","arxiv_id":"2510.11474","title":"Coordinated Strategies in Realistic Air Combat by Hierarchical Multi-Agent Reinforcement Learning","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.","short_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 cha...","url_abs":"https://arxiv.org/abs/2510.11474","url_pdf":"https://arxiv.org/pdf/2510.11474v2","authors":"[\"Ardian Selmonaj\",\"Giacomo Del Rio\",\"Adrian Schneider\",\"Alessandro Antonucci\"]","published":"2025-10-13T14:44:51Z","proceeding":"cs.RO","tasks":"[\"cs.RO\",\"cs.AI\",\"cs.HC\",\"cs.LG\",\"cs.MA\"]","methods":"[\"Reinforcement Learning\",\"Generative Adversarial Network\"]","has_code":false}