BVR Gym: A Reinforcement Learning Environment for Beyond-Visual-Range Air Combat
This work addresses the problem of efficiently discovering air combat maneuvers for military or research applications, but it is incremental as it builds on existing simulators with domain-specific adaptations.
The authors tackled the challenge of developing new air combat tactics by creating BVR Gym, a reinforcement learning environment for beyond-visual-range air combat, which provides a high-fidelity simulation based on JSBSim and is adapted to this specific domain.
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.