Automating the resolution of flight conflicts: Deep reinforcement learning in service of air traffic controllers. (arXiv:2206.07403v1 [cs.MA])

Dense and complex air traffic scenarios require higher levels of automation
than those exhibited by tactical conflict detection and resolution (CD\&R)
tools that air traffic controllers (ATCO) use today. However, the air traffic
control (ATC) domain, being safety critical, requires AI systems to which
operators are comfortable to relinquishing control, guaranteeing operational
integrity and automation adoption. Two major factors towards this goal are
quality of solutions, and transparency in decision making. This paper proposes
using a graph convolutional reinforcement learning …

air traffic arxiv learning reinforcement reinforcement learning traffic