Spatial-Aware Graph Attention Agentic AI for Embodied UAV Edge Computing Networks

Abstract

The evolution of uncrewed aerial vehicles (UAVs) into embodied intelligent agents in the low-altitude economy is reshaping edge computing networks. However, the high mobility of UAVs induces severe topology dynamics, limiting the efficacy of traditional fully connected multiagent reinforcement learning because of dimensionality and credit assignment challenges. Furthermore, existing graph attention approaches neglect explicit communication boundary constraints, leading to mismatches between value evaluation and physical topology. To address these challenges, this paper proposes the spatial-aware graph attention multiagent twin delayed deep deterministic policy gradient (SAGA-MATD3) algorithm. By embedding a dynamic spatial masking mechanism based on the communication radius into the critic network, the proposed method enforces physical reachability constraints and attenuates extraneous noise. Simulation results demonstrate that SAGA-MATD3 significantly reduces service latency and improves fairness under an acceptable energy-consumption tradeoff, achieving a 37.1% improvement in convergence reward and enabling the self-organization of robust load-balanced mesh topologies.