  Landing an unmanned aerial vehicle (UAV) on a ground marker is an open
problem despite the effort of the research community. Previous attempts mostly
focused on the analysis of hand-crafted geometric features and the use of
external sensors in order to allow the vehicle to approach the land-pad. In
this article, we propose a method based on deep reinforcement learning that
only requires low-resolution images taken from a down-looking camera in order
to identify the position of the marker and land the UAV on it. The proposed
approach is based on a hierarchy of Deep Q-Networks (DQNs) used as high-level
control policy for the navigation toward the marker. We implemented different
technical solutions, such as the combination of vanilla and double DQNs, and a
partitioned buffer replay. Using domain randomization we trained the vehicle on
uniform textures and we tested it on a large variety of simulated and
real-world environments. The overall performance is comparable with a
state-of-the-art algorithm and human pilots.
