基于深度强化学习多用户移动边缘计算轻量任务卸载优化

移动边缘计算(MEC)在提高移动设备的计算体验质量方面具有一定的应用前景.它可以为支持传统通信和MEC服务的切片式无线接入网提供紧密邻近的计算功能.然而,这种密集计算问题是一种高维的NP难问题,一些机器学习方法在解决该问题的时候不能取得良好的效果.针对这些问题,本文将最佳计算卸载问题建模为马尔可夫决策过程,目标是最大化长期效用性能,从而根据队列状态、能量队列状态以及移动用户与BS之间的信道质量做出卸载决策.为了降低状态空间中高维性的问题,提出了应用深度确定性策略梯度的基于候选网络优化边缘计算优化卸载ECOO算法,从而产生一种用于解决随机任务卸载的新型学习算法.通过仿真实验证明,ECOO算法在能耗和时延方面优于一些深度强化学习算法,在处理高维问题时效果更好.

Deep reinforcement learning-based optimization of lightweight task offloading for multi-user mobile edge computing

To improve the quality of computation experience for mobile devices,mobile edge computing (MEC)is a promising paradigm by providing computing capabilities in close proximity within a sliced radio access network,which supports both traditional communication and MEC services.However,this kind of intensive computing problem is a high dimensional NP hard problem,and some machine learning methods do not have a good effect on solving this problem.In this paper,the Markov decision process model is established to find the excellent task offloading scheme,which maximizes the long-term utility performance,so as to make the best offloading decision according to the queue state,energy queue state and channel quality between mobile users and BS.In order to explore the curse of high dimension in state space,a candidate network is proposed based on edge computing optimize offloading (ECOO)algorithm with the application of deep deterministic policy gradient algorithm.Through simulation experiments,it is proved that the ECOO algorithm is superior to some deep reinforcement learning algorithms in terms of energy consumption and time delay.So the ECOO is good at dealing with high dimensional problems.