Inverse Optimal Planning for Air Traffic Control
This addresses air traffic management for airports, but it is incremental as it applies existing inverse reinforcement learning to a new domain.
The paper tackled the problem of learning air traffic control rules from real data to assist human operators and support autonomous air traffic, resulting in trajectories that are safe, feasible, and efficient.
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.