Deep Reinforcement Learning Based Task Offloading Algorithm for Mobile-edge Computing Systems

Abstract

Mobile-edge computing(MEC) is deemed to a promising paradigm. By deploying high-performance servers on the mobile access network side, MEC can provide auxiliary computing power for mobile devices, greatly reducing the computing pressure of mobile devices and improving the quality of the computing experience. In this paper, we consider the offloading problem of tasks in single-user MEC system. In order to minimize the mean energy consumption of mobile devices and the mean slowdown of tasks in the queue, we propose a deep reinforcement learning(DRL) based task offloading algorithm, and a new reward function is designed, which can guide the algorithm to optimize the trade-off between mean energy consumption and mean slowdown. The simulation results show that the deep reinforcement learning based algorithm outperforms the baseline algorithms.