  Unmanned aerial vehicle (UAV) has recently attracted a lot of attention as a
candidate to meet the 6G ubiquitous connectivity demand and boost the
resiliency of terrestrial networks. Thanks to the high spectral efficiency and
low latency, non-orthogonal multiple access (NOMA) is a potential access
technique for future communication networks. In this paper, we propose to use
the UAV as a moving base station (BS) to serve multiple users using NOMA and
jointly solve for the 3D-UAV placement and resource allocation problem. Since
the corresponding optimization problem is non-convex, we rely on the recent
advances in artificial intelligence (AI) and propose AdaptSky, a deep
reinforcement learning (DRL)-based framework, to efficiently solve it. To the
best of our knowledge, AdaptSky is the first framework that optimizes NOMA
power allocation jointly with 3D-UAV placement using both sub-6GHz and
millimeter wave mmWave spectrum. Furthermore, for the first time in NOMA-UAV
networks, AdaptSky integrates the dueling network (DN) architecture to the DRL
technique to improve its learning capabilities. Our findings show that AdaptSky
does not only exhibit a fast-adapting learning and outperform the
state-of-the-art baseline approach in data rate and fairness, but also it
generalizes very well. The AdaptSky source code is accessible to use here:
https://github.com/Fouzibenfaid/AdaptSky
