# Multi-agent reinforcement learning for intent-based service assurance in cellular networks

arXiv: https://arxiv.org/abs/2208.03740 · HTML: https://mlsanity.com/paper/2208.03740/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 13 references that are in the map.
- First posted on arXiv 7 Aug 2022.

## Builds on (most cited first, 3 of 3)

- [QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1803.11485/) (2018)
- [Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2003.08839/) (2020)
- [From 4G to 5G: Self-organized Network Management meets Machine Learning](https://mlsanity.com/paper/1707.09300/) (2017)

## Built on by (most cited first, 2 of 2)

- [Intent Assurance using LLMs guided by Intent Drift](https://mlsanity.com/paper/2402.00715/) (2024)
- [Domain Adaptation of Reinforcement Learning Agents based on Network Service Proximity](https://mlsanity.com/paper/2303.01013/) (2023)

## Abstract (arXiv)

Recently, intent-based management has received good attention in telecom networks owing to stringent performance requirements for many of the use cases. Several approaches in the literature employ traditional closed-loop driven methods to fulfill the intents on the KPIs. However, these methods consider every closed-loop independent of each other which degrades the combined performance. Also, such existing methods are not easily scalable. Multi-agent reinforcement learning (MARL) techniques have shown significant promise in many areas in which traditional closed-loop control falls short, typically for complex coordination and conflict management among loops. In this work, we propose a method based on MARL to achieve intent-based management without the need for knowing a model of the underlying system. Moreover, when there are conflicting intents, the MARL agents can implicitly incentivize the loops to cooperate and promote trade-offs, without human interaction, by prioritizing the important KPIs. Experiments have been performed on a network emulator for optimizing KPIs of three services. Results obtained demonstrate that the proposed system performs quite well and is able to fulfill all existing intents when there are enough resources or prioritize the KPIs when resources are scarce.