Reinforcement learning empowered multi-AGV offloading scheduling in edge-cloud IIoT

Abstract

Abstract The edge-cloud computing architecture has been introduced to industrial circles to ensure the time constraints for industrial computing tasks. Besides the central cloud, various numbers of edge servers (ESes) are deployed in a distributed manner. In the meantime, most large factories currently use auto guided vehicles (AGVs). They usually travel along a given route and can help offload tasks to ESes. An ES maybe accessed by multiple AGVs, thus incurring offloading and processing delays due to resource competition. In this paper, we investigate the offloading scheduling issue for cyclical tasks and put forth the Multi-AGV Cyclical Offloading Optimization (MCOO) algorithm to reduce conflicts. The solution divides the offloading optimization problem into two parts. Firstly, the load balancing algorithm and greedy algorithm are utilized to find the optimal allocation of tasks for a single AGV under limited conditions. Then, multiple AGVs are asynchronously trained by applying the Reinforcement Learning-based A3C algorithm to optimize the offloading scheme. The simulation results show that the MCOO algorithm improves the global offloading performance both in task volume and adaptability compared with the baseline algorithms.

Keywords

• Mobile Edge Computing
• Task Offloading Optimization
• Reinforcement Learning
• Q-learning