BVR Gym: A Reinforcement Learning Environment for Beyond-Visual-Range Air Combat

TL;DR

The paper introduces BVR Gym, an open-source, high-fidelity reinforcement learning environment based on JSBSim, designed specifically for investigating beyond-visual-range air combat tactics and maneuvers.

Contribution

It provides a novel, open-source, high-fidelity simulation environment tailored for BVR air combat, filling gaps left by existing tools.

Findings

Environment is based on JSBSim for high fidelity

Adapted specifically for BVR air combat scenarios

Enables testing of new tactics and maneuvers

Abstract

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.