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

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.