A Survey on Reinforcement Learning in Aviation Applications
It provides a comprehensive overview for researchers and practitioners in aviation, but is incremental as it surveys existing work without new results.
This survey paper reviews reinforcement learning (RL) applications in aviation, highlighting RL's data-driven approach for sequential decision-making problems in the industry, and identifies technical gaps and future research directions.
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.