  Unmanned aerial vehicles (UAVs) can be powerful Internet-of-Things components
to execute sensing tasks over the next-generation cellular networks, which are
generally referred to as the cellular Internet of UAVs. However, due to the
high mobility of UAVs and the shadowing in the air-to-ground channels, UAVs
operate in an environment with dynamics and uncertainties. Therefore, UAVs need
to improve the quality of service (QoS) of sensing and communication without
complete information, which makes reinforcement learning suitable to be
employed in the cellular Internet of UAVs. In this article, we propose a
distributed sense-and-send protocol to coordinate the UAVs for sensing and
transmission. Then, we apply reinforcement learning in the cellular Internet of
UAVs to solve key problems such as trajectory control and resource management.
Finally, we point out several potential future research directions.
