From Design to Deployment of Zero Touch Deep Reinforcement Learning WLANs

Abstract

Machine learning 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 gained 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, which are generally formulated as hard optimization problems. At the same time, successful training and deployment of DRL agents in real-world scenarios face a number of challenges that we outline and address in this article. Tackling the case of wireless local area network radio resource management, we report guidelines that extend to other usecases and more general contexts.