Graph Attention-based Decentralized Actor-Critic for Dual-Objective Control of Multi-UAV Swarms

TL;DR

This paper introduces a novel graph attention-based decentralized actor-critic method for multi-UAV systems, effectively balancing coverage and battery life through dual policies and advanced neural network architectures.

Contribution

It presents a new decentralized reinforcement learning approach using graph attention networks and dual-critic architecture for dual-objective UAV control.

Findings

GADC outperforms existing methods in coverage and battery efficiency.

The approach scales well to larger UAV swarms.

Experimental results validate superior real-world performance.

Abstract

This research focuses on optimizing multi-UAV systems with dual objectives: maximizing service coverage as the primary goal while extending battery lifetime as the secondary objective. We propose a Graph Attention-based Decentralized Actor-Critic (GADC) to optimize the dual objectives. The proposed approach leverages a graph attention network to process UAVs' limited local observation and reduce the dimension of the environment states. Subsequently, an actor-double-critic network is developed to manage dual policies for joint objective optimization. The proposed GADC uses a Kullback-Leibler (KL) divergence factor to balance the tradeoff between coverage performance and battery lifetime in the multi-UAV system. We assess the scalability and efficiency of GADC through comprehensive benchmarking against state-of-the-art methods, considering both theory and experimental aspects. Extensive testing in both ideal settings and NVIDIA Sionna's realistic ray tracing environment demonstrates GADC's superior performance.