Flash-Attention-Enhanced Multi-Agent Deep Deterministic Policy Gradient for Mobile Edge Computing in Digital Twin-Powered Internet of Things

Yuzhe Gao, Xiaoming Yuan (), Songyu Wang, Lixin Chen, Zheng Zhang and Tianran Wang
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Yuzhe Gao: Hebei Key Laboratory of Marine Perception Network and Data Processing, Northeastern University at Qinhuangdao, Qinhuangdao 066004, China
Xiaoming Yuan: Hebei Key Laboratory of Marine Perception Network and Data Processing, Northeastern University at Qinhuangdao, Qinhuangdao 066004, China
Songyu Wang: Hebei Key Laboratory of Marine Perception Network and Data Processing, Northeastern University at Qinhuangdao, Qinhuangdao 066004, China
Lixin Chen: Hebei Key Laboratory of Marine Perception Network and Data Processing, Northeastern University at Qinhuangdao, Qinhuangdao 066004, China
Zheng Zhang: Hebei Key Laboratory of Marine Perception Network and Data Processing, Northeastern University at Qinhuangdao, Qinhuangdao 066004, China
Tianran Wang: Hebei Key Laboratory of Marine Perception Network and Data Processing, Northeastern University at Qinhuangdao, Qinhuangdao 066004, China

Mathematics, 2025, vol. 13, issue 13, 1-21

Abstract: Offloading decisions and resource allocation problems in mobile edge computing (MEC) emerge as key challenges as they directly impact system performance and user experience in dynamic and resource-constrained Internet of Things (IoT) environments. This paper constructs a comprehensive and layered digital twin (DT) model for MEC, enabling real-time cooperation with the physical world and intelligent decision making. Within this model, a novel Flash-Attention-enhanced Multi-Agent Deep Deterministic Policy Gradient (FA-MADDPG) algorithm is proposed to effectively tackle MEC problems. It enhances the model by arming a critic network with attention to provide a high-quality decision. It also changes a matrix operation in a mathematical way to speed up the training process. Experiments are performed in our proposed DT environment, and results demonstrate that FA-MADDPG has good convergence. Compared with other algorithms, it achieves excellent performance in delay and energy consumption under various settings, with high time efficiency.

Keywords: Internet of Things; mobile edge computing; digital twin; multi-agent reinforcement learning; attention mechanism; flash attention (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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