  Urban air mobility (UAM) has the potential to revolutionize our daily
transportation, offering rapid and efficient deliveries of passengers and cargo
between dedicated locations within and around the urban environment. Before the
commercialization and adoption of this emerging transportation mode, however,
aviation safety must be guaranteed, i.e., all the aircraft have to be safely
separated by strategic and tactical deconfliction. Reinforcement learning has
demonstrated effectiveness in the tactical deconfliction of en route commercial
air traffic in simulation. However, its performance is found to be dependent on
the traffic density. In this project, we propose a novel framework that
combines demand capacity balancing (DCB) for strategic conflict management and
reinforcement learning for tactical separation. By using DCB to precondition
traffic to proper density levels, we show that reinforcement learning can
achieve much better performance for tactical safety separation. Our results
also indicate that this DCB preconditioning can allow target levels of safety
to be met that are otherwise impossible. In addition, combining strategic DCB
with reinforcement learning for tactical separation can meet these safety
levels while achieving greater operational efficiency than alternative
solutions.
