QPlane: An Open-Source Reinforcement Learning Toolkit for Autonomous Fixed Wing Aircraft Simulation

Abstract

Reinforcement Learning (RL) is a fast-growing field of research that is mostly applied in the realm of video games due to the compatibility of RL and game tasks. AI Gym has established itself as the gold standard toolkit for Reinforcement Learning research. Unfortunately, toolkits like AI Gym are very optimized for benchmark purposes and may not always be suitable for real world type problems. Additionally, fixed wing flight simulation has specific requirements and may need other solutions. In this paper, we propose QPlane as an alternative toolkit for RL training of fixed wing aircraft. QPlane was developed in an effort to create a RL toolkit for fixed wing aircraft simulation that is easily modifiable for different scenarios. QPlane is replicable and flexible for ease of implementation to high performance computing, and is modular for quick environment and algorithm replacement. In this paper we present and discuss details of QPlane, as well as proof of concept results.