Distributed Reinforcement Learning for Flexible and Efficient UAV Swarm Control

Federico Venturini, Federico Mason, Francesco Pase, Federico Chiariotti, Alberto Testolin, Michele Zorzi

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

Over the past few years, the use of swarms of uav 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 uav 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.

Original languageEnglish
JournalIEEE Transactions on Cognitive Communications and Networking
Number of pages15
ISSN2332-7731
DOIs
Publication statusE-pub ahead of print - 2 Mar 2021

Keywords

  • Artificial intelligence
  • Drones
  • Reinforcement learning
  • Sensors
  • Surveillance
  • Training
  • Transfer learning
  • Wireless communication
  • distributed decision making
  • mobile robots
  • neural network applications.

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