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

Electrical Engineering and Systems Science > Signal Processing

[Submitted on 10 Jun 2025 (v1), last revised 9 Nov 2026 (this version, v2)]

Title:Graph Attention-based Decentralized Actor-Critic for Dual-Objective Control of Multi-UAV Networks

View PDFAbstract: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.

Submission history

From: Haoran Peng [view email][v1] Tue, 10 Jun 2025 19:30:53 UTC (9,157 KB)

[v2] Mon, 9 Nov 2026 20:46:18 UTC (23,063 KB)

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