Creating new air combat tactics and discovering novel maneuvers can require numerous hours of expert pilots' time. Additionally, for each different combat scenario, the same strategies may not work since small changes in equipment performance may drastically change the air combat outcome. For this reason, we created a reinforcement learning environment to help investigate potential air combat tactics in the field of beyond-visual-range (BVR) air combat: the BVR Gym. This type of air combat is important since long-range missiles are often the first weapon to be used in aerial combat. Some existing environments provide high-fidelity simulations but are either not open source or are not adapted to the BVR air combat domain. Other environments are open source but use less accurate simulation models. Our work provides a high-fidelity environment based on the open-source flight dynamics simulator JSBSim and is adapted to the BVR air combat domain. This article describes the building blocks of the environment and some use cases.

中文翻译：

---

BVR Gym：超视距空战的强化学习环境

创造新的空战战术和发现新颖的机动动作可能需要专家飞行员花费大量时间。此外，对于每种不同的战斗场景，相同的策略可能不起作用，因为装备性能的微小变化可能会极大地改变空战结果。为此，我们创建了一个强化学习环境来帮助研究超视距 (BVR) 空战领域的潜在空战战术：BVR Gym。这种类型的空战非常重要，因为远程导弹通常是空战中首先使用的武器。一些现有环境提供高保真模拟，但要么不是开源的，要么不适合超视距空战领域。其他环境是开源的，但使用不太准确的模拟模型。我们的工作提供了基于开源飞行动力学模拟器 JSBSim 的高保真环境，并适应超视距空战领域。本文介绍了环境的构建块和一些用例。