  In recent years, unmanned aerial vehicles (UAVs) have been considered for
telecommunications purposes as relays, caches, or IoT data collectors. In
addition to being easy to deploy, their maneuverability allows them to adjust
their location to optimize the capacity of the link to the user equipment on
the ground or of the link to the basestation. The majority of the previous work
that analyzes the optimal placement of such a UAV makes at least one of two
assumptions: the channel can be predicted using a simple model or the locations
of the users on the ground are known. In this paper, we use deep reinforcement
learning (deep RL) to optimally place a UAV serving a ground user in an urban
environment, without the previous knowledge of the channel or user location.
Our algorithm relies on signal-to-interference-plus-noise ratio (SINR)
measurements and a 3D map of the topology to account for blockage and
scatterers. Furthermore, it is designed to operate in any urban environment.
Results in conditions simulated by a ray tracing software show that with the
constraint on the maximum number of iterations our algorithm has a 90% success
rate in converging to a target SINR.
