  Urban air mobility is an innovative mode of transportation in which electric
vertical takeoff and landing (eVTOL) vehicles operate between nodes called
vertiports. We outline a self-organized vertiport arrival system based on deep
reinforcement learning. The airspace around the vertiport is assumed to be
circular, and the vehicles can freely operate inside. Each aircraft is
considered an individual agent and follows a shared policy, resulting in
decentralized actions that are based on local information. We investigate the
development of the reinforcement learning policy during training and illustrate
how the algorithm moves from suboptimal local holding patterns to a safe and
efficient final policy. The latter is validated in simulation-based scenarios,
including robustness analyses against sensor noise and a changing distribution
of inbound traffic. Lastly, we deploy the final policy on small-scale unmanned
aerial vehicles to showcase its real-world usability.
