In the realm of mobile edge computing (MEC), efficient computation task
offloading plays a pivotal role in ensuring a seamless quality of experience
(QoE) for users. Maintaining a high QoE is paramount in today's interconnected
world, where users demand responsive and reliable services. This challenge
stands as one of the most primary key factors contributing to handling dynamic
and uncertain mobile environment. In this study, we delve into computation
offloading in MEC systems, where strict task processing deadlines and energy
constraints can adversely affect the system performance. We formulate the
computation task offloading problem as a Markov decision process (MDP) to
maximize the long-term QoE of each user individually. We propose a
decentralized QoE-oriented computation offloading (QOCO) algorithm based on
deep reinforcement learning (DRL) that empowers mobile devices to make their
offloading decisions without requiring knowledge of decisions made by other
devices. Through numerical studies, we evaluate the performance of QOCO.
Simulation results validate that the QOCO algorithm efficiently exploits the
computational resources of edge nodes. Consequently, it can complete 14% more
tasks and reduce task delay and energy consumption by 9% and 6%, respectively.
These together contribute to a significant improvement of at least 37% in
average QoE compared to an existing algorithm.

在移动边缘计算领域，通过有效的计算任务卸载可以确保用户无缝的体验质量。本研究以马尔科夫决策过程为基础，提出了一种基于深度强化学习的去中心化 QoS 导向的计算任务卸载算法，通过充分利用边缘节点的计算资源，能够显著提高用户的体验质量。