  Owing to the enhanced flexibility in deployment and decreasing costs of
manufacturing, the demand for unmanned aerial vehicles (UAVs) is expected to
soar in the upcoming years. In this paper, we explore a UAV aided search and
rescue~(SAR) operation in indoor environments, where the GPS signals might not
be reliable. We consider a SAR scenario where the UAV tries to locate a victim
trapped in an indoor environment by sensing the RF signals emitted from a smart
device owned by the victim. To locate the victim as fast as possible, we
leverage tools from reinforcement learning~(RL). Received signal strength~(RSS)
at the UAV depends on the distance from the source, indoor shadowing, and
fading parameters, and antenna radiation pattern of the receiver mounted on the
UAV. To make our analysis more realistic, we model two indoor scenarios with
different dimensions using commercial ray-tracing software. Then, the
corresponding RSS values at each possible discrete UAV location are extracted
and used in a Q-learning framework. Unlike the traditional location-based
navigation approach that exploits GPS coordinates, our method uses the RSS to
define the states and rewards of the RL algorithm. We compare the performance
of the proposed method where directional and omnidirectional antennas are used.
The results reveal that the use of directional antennas provides faster
convergence rates than the omnidirectional antennas.
