逆最適計画による航空交通管制のルール学習
Inverse Optimal Planning for Air Traffic Control
実データから最大エントロピー逆強化学習で航空交通管制のルールをコスト関数として学習し、探索型経路計画のペナルティとして用いることで、安全で効率的な着陸経路を生成できることを示した。
著者: Ekaterina Tolstaya, Alejandro Ribeiro, Vijay Kumar, Ashish Kapoor
分類: cs.RO
原文アブストラクト
We envision a system that concisely describes the rules of air traffic control, assists human operators and supports dense autonomous air traffic around commercial airports. We develop a method to learn the rules of air traffic control from real data as a cost function via maximum entropy inverse reinforcement learning. This cost function is used as a penalty for a search-based motion planning method that discretizes both the control and the state space. We illustrate the methodology by showing that our approach can learn to imitate the airport arrival routes and separation rules of dense commercial air traffic. The resulting trajectories are shown to be safe, feasible, and efficient.
