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arXiv · 2211.02147

A Survey on Reinforcement Learning in Aviation Applications

Pouria Razzaghi·Amin Tabrizian·Wei Guo·Shulu Chen·Abenezer Taye·Ellis Thompson·Alexis Bregeon·Ali Baheri·Peng Wei
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Abstract

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.

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Pouria Razzaghi, Amin Tabrizian, Wei Guo, Shulu Chen, Abenezer Taye, Ellis Thompson, Alexis Bregeon, Ali Baheri, Peng Wei. 2022-11-03. A Survey on Reinforcement Learning in Aviation Applications. https://doi.org/10.1016/j.engappai.2024.108911

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