A Survey on Reinforcement Learning in Aviation Applications

• URL: http://arxiv.org/abs/2211.02147v3
• Date: Thu, 25 Jul 2024 20:19:22 GMT
• Title: A Survey on Reinforcement Learning in Aviation Applications
• Authors: Pouria Razzaghi, Amin Tabrizian, Wei Guo, Shulu Chen, Abenezer Taye, Ellis Thompson, Alexis Bregeon, Ali Baheri, Peng Wei,
• Abstract summary: reinforcement learning 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.
• Score: 7.719858656477459
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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