Compared with model-based control and optimization methods, reinforcement learning (RL) provides a data-driven, learning-based framework to formulate and solve sequential decision-making problems. The RL framework has become promising due to largely improved data availability and computing power in the aviation industry. Many aviation-based applications can be formulated or treated as sequential decision-making problems. Some of them are offline planning problems, while others need to be solved online and are safety-critical. In this survey paper, we first describe standard RL formulations and solutions. Then we survey the landscape of existing RL-based applications in aviation. Finally, we summarize the paper, identify the technical gaps, and suggest future directions of RL research in aviation.
@article{arxiv.2211.02147,
title = {A Survey on Reinforcement Learning in Aviation Applications},
author = {Pouria Razzaghi and Amin Tabrizian and Wei Guo and Shulu Chen and Abenezer Taye and Ellis Thompson and Alexis Bregeon and Ali Baheri and Peng Wei},
journal= {arXiv preprint arXiv:2211.02147},
year = {2024}
}