# Inverse Optimal Planning for Air Traffic Control

## TL;DR

This paper introduces a method to learn air traffic control rules from real data using inverse reinforcement learning, enabling autonomous planning that mimics real traffic patterns while ensuring safety and efficiency.

## Contribution

It presents a novel inverse optimal planning approach to derive air traffic control rules from data for improved autonomous traffic management.

## Key findings

- Successfully learned airport arrival routes and separation rules
- Generated trajectories that are safe, feasible, and efficient
- Demonstrated applicability to dense commercial air traffic

## Abstract

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.