# From Design to Deployment of Zero-touch Deep Reinforcement Learning WLANs

arXiv: https://arxiv.org/abs/2207.06172 · HTML: https://mlsanity.com/paper/2207.06172/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jul 2022.

## Built on by (most cited first, 3 of 3)

- [Towards Zero Touch Networks: Cross-Layer Automated Security Solutions for 6G Wireless Networks](https://mlsanity.com/paper/2502.20627/) (2025)
- [Base Station Deployment under EMF constrain by Deep Reinforcement learning](https://mlsanity.com/paper/2601.02385/) (2025)
- [Reinforcement-Learning based routing for packet-optical networks with hybrid telemetry](https://mlsanity.com/paper/2406.12602/) (2024)

## Abstract (arXiv)

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.