Distributed Reinforcement Learning for Flexible and Efficient UAV Swarm Control

TL;DR

This paper introduces a scalable distributed reinforcement learning framework for UAV swarms that enables effective, robust, and adaptable exploration and monitoring of unknown areas, outperforming heuristic methods.

Contribution

The paper presents a novel distributed RL approach for UAV swarms that scales without modification and maintains robustness under communication impairments.

Findings

Effective strategies for UAV swarms demonstrated

Robust to communication channel impairments

Adapts to new scenarios with minimal retraining

Abstract

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.