  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.
