论文标题
从设计到部署零接触深的强化学习WLAN
From Design to Deployment of Zero-touch Deep Reinforcement Learning WLANs
论文作者
论文摘要
机器学习（ML）越来越多地用于自动化网络任务，以称为零接触网络和服务管理（ZSM）的范式。特别是，深入的增强学习（DRL）技术最近引起了他们在不同领域做出复杂决策的能力的广泛关注。在ZSM上下文中，DRL是诸如动态资源分配之类的任务的吸引人候选人，通常被称为硬优化问题。同时，在现实世界中，成功培训和部署DRL代理商在本文中概述并解决了许多挑战。解决无线局域网（WLAN）无线电资源管理的案例，我们报告了扩展到其他用户酶和更多一般环境的指南。
Machine learning (ML) is increasingly used to automate networking tasks, in a paradigm known as zero-touch network and service management (ZSM). In particular, Deep Reinforcement Learning (DRL) techniques have recently gathered much attention for their ability to learn taking complex decisions in different fields. In the ZSM context, DRL is an appealing candidate for tasks such as dynamic resource allocation, that is generally formulated as hard optimization problems. At the same time, successful training and deployment of DRL agents in real-world scenarios faces a number of challenges that we outline and address in this paper. Tackling the case of Wireless Local Area Network (WLAN) radio resource management, we report guidelines that extend to other usecases and more general contexts.
