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arXiv Machine Learning By Haocheng Sun (Beijing University of Posts,Telecommunications), Mulai Tan (Air Force Engineering University)

BVR Sim: An Open and High-Throughput Environment for Heterogeneous Air-Combat Reinforcement Learning

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BVR Sim is an open‑source, Gymnasium‑style environment for heterogeneous air‑combat reinforcement learning, supporting multiple JSBSim aircraft models (F‑15, F‑16, F/A‑18, F‑22) with configurable weapons, sensors, and opponents. It offers a unified tactical action interface, interchangeable Python and accelerated C++ backends, entity‑oriented observations, compositional rewards, scripted opponents, replay and visualization, and adapters for multi‑agent learning frameworks. At a 0.4‑second decision interval, the C++ backend achieves 104 simulated seconds per wall‑clock second in 1‑vs‑1 and remains practical through 10‑vs‑10 scenarios, and a policy trained on the F‑16 transfers to four unseen aircraft with a 45.5% mean win rate after controller adaptation.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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