  Dogfighting is a challenging scenario in aerial applications that requires a
comprehensive understanding of both strategic maneuvers and the aerodynamics of
agile aircraft. The aerial agent needs to not only understand tactically
evolving maneuvers of fighter jets from a long-term perspective but also react
to rapidly changing aerodynamics of aircraft from a short-term viewpoint. In
this paper, we introduce TempFuser, a novel long short-term temporal fusion
transformer architecture that can learn agile, tactical, and acrobatic flight
maneuvers in complex dogfight problems. Our approach integrates two distinct
temporal transition embeddings into a transformer-based network to
comprehensively capture both the long-term tactics and short-term agility of
aerial agents. By incorporating these perspectives, our policy network
generates end-to-end flight commands that secure dominant positions over the
long term and effectively outmaneuver agile opponents. After training in a
high-fidelity flight simulator, our model successfully learns to execute
strategic maneuvers, outperforming baseline policy models against various types
of opponent aircraft. Notably, our model exhibits human-like acrobatic
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 situations. Demo
videos are available at https://sites.google.com/view/tempfuser.
