A Multi-Agent DRL-Based Computation Offloading and Resource Allocation Method With Attention Mechanism in MEC-Enabled IIoT

The widespread adoption of Industrial Internet of Things (IIoT) has significantly transformed various aspects of industrial manufacturing. However, the massive volume and complexity of IIoT data highlight the need for innovative solutions to enhance the overall performance of IIoT systems. In this regard, mobile edge computing, assisted by deep reinforcement learning, can alleviate the burden on IIoT systems through computation offloading. Nevertheless, in increasingly digitized industrial environments, how to make real-time, efficient task offloading decisions remains a subject of deep exploration. To address this issue, we propose a two-stage resource allocation and task offloading method, named MCORM. In the first stage, we use the Combinatorial Upper Confidence Bound algorithm, based on the combinatorial multi-armed bandit problem, to solve the resource allocation problem. In the second stage, the Multi-Agent Proximal Policy Optimization algorithm is employed to determine the approximate optimal offloading strategy. Specifically, the Convolutional Block Attention Module is utilized to process observation information, focusing on important features. Finally, extensive experiments are conducted using both simulated and real datasets. The results demonstrate that our proposed algorithm MCORM can reduce latency and energy consumption effectively, promoting efficient industrial production.