In aerial combat, dogfighting poses intricate challenges that demand an understanding of both strategic maneuvers and the aerodynamics of agile fighter aircraft. In this paper, we introduce TempFuser, a novel long short-term temporal fusion transformer designed to learn tactical and agile flight maneuvers in aerial dogfights. Our approach employs two distinct LSTM-based input embeddings to encode long-term sparse and short-term dense state representations. By integrating these embeddings through a transformer encoder, our model captures the tactics and agility of fighter jets, enabling it to generate end-to-end flight commands that secure dominant positions and outmaneuver the opponent. After extensive training against various types of opponent aircraft in a high-fidelity flight simulator, our model successfully learns to perform complex fighter maneuvers, consistently outperforming several baseline models. Notably, our model exhibits human-like strategic maneuvers even when facing adversaries with superior specifications, all without relying on explicit prior knowledge. Moreover, it demonstrates robust pursuit performance in challenging supersonic and low-altitude environments. Demo videos are available at https://sites.google.com/view/tempfuser.

翻译：空中缠斗中，近距格斗面临复杂挑战，需要理解战略机动与敏捷战斗机空气动力学特性。本文提出TempFuser——一种新颖的长短时时序融合Transformer，专为学习空中缠斗中的战术与敏捷飞行机动设计。本方法采用两种基于LSTM的输入嵌入编码，分别表征长时稀疏与短时密集状态表示。通过Transformer编码器整合这些嵌入，模型捕捉战斗机的战术与机动性，实现端到端飞行指令生成，从而占据优势位置并超越对手。在高保真飞行模拟器中与多种类型对手飞机进行大量训练后，该模型成功学会执行复杂战斗机机动，并持续优于多个基线模型。值得注意的是，即使面对性能更优的对抗方，模型无需任何显式先验知识即可展现类人战略机动；同时，在极具挑战性的超音速与低空环境中仍保持稳健的追击性能。演示视频见https://sites.google.com/view/tempfuser。