Over the past few years, the use of swarms of Unmanned Aerial Vehicles (UAVs) in monitoring and remote area surveillance applications has become economically efficient 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 can easily deal with non-uniform distributions of targets, drawing from past experience to improve its performance. In particular, our experiments show that when agents are trained for a specific scenario, they can adapt to a new one with a minimal amount of additional training. We show that our RL approach achieves favorable performance compared to a computationally intensive look-ahead heuristic.
|Titel||Proceedings of the 6th ACM Workshop on Micro Aerial Vehicle Networks, Systems, and Applications|
|Forlag||Association for Computing Machinery|
|Status||Udgivet - jul. 2020|
|Begivenhed||6th ACM Workshop on Micro Aerial Vehicle Networks, Systems, and Applications, co-located with MobiSys 2020 - |
Varighed: 15 jun. 2020 → …
|Konference||6th ACM Workshop on Micro Aerial Vehicle Networks, Systems, and Applications, co-located with MobiSys 2020|
|Periode||15/06/2020 → …|