  Targets search and detection encompasses a variety of decision problems such
as coverage, surveillance, search, observing and pursuit-evasion along with
others. In this paper we develop a multi-agent deep reinforcement learning
(MADRL) method to coordinate a group of aerial vehicles (drones) for the
purpose of locating a set of static targets in an unknown area. To that end, we
have designed a realistic drone simulator that replicates the dynamics and
perturbations of a real experiment, including statistical inferences taken from
experimental data for its modeling. Our reinforcement learning method, which
utilized this simulator for training, was able to find near-optimal policies
for the drones. In contrast to other state-of-the-art MADRL methods, our method
is fully decentralized during both learning and execution, can handle
high-dimensional and continuous observation spaces, and does not require tuning
of additional hyperparameters.
