Decentralized Autonomous Traffic Management through Corridor Networks
The work extends multi-agent reinforcement learning to manage decentralized traffic flow in air corridor networks for autonomous aircraft. The method learns behaviors that transfer effectively across different network geometries and traffic densities without needing centralized coordination or retraining. This demonstrates that local, learned policies can scale robustly to complex, real-world airspace challenges.