  Over the past few years, the use of swarms of Unmanned Aerial Vehicles (UAVs)
in monitoring and remote area surveillance applications has become widespread
thanks to the price reduction and the increased capabilities of drones. The
drones in the swarm need to cooperatively explore an unknown area, in order to
identify and monitor interesting targets, while minimizing their movements. In
this work, we propose a distributed Reinforcement Learning (RL) approach that
scales to larger swarms without modifications. The proposed framework relies on
the possibility for the UAVs to exchange some information through a
communication channel, in order to achieve context-awareness and implicitly
coordinate the swarm's actions. Our experiments show that the proposed method
can yield effective strategies, which are robust to communication channel
impairments, and that can easily deal with non-uniform distributions of targets
and obstacles. Moreover, when agents are trained in a specific scenario, they
can adapt to a new one with minimal additional training. We also show that our
approach achieves better performance compared to a computationally intensive
look-ahead heuristic.
