# Multi-agent Reinforcement Learning with Graph Q-Networks for Antenna Tuning

arXiv: https://arxiv.org/abs/2302.01199 · HTML: https://mlsanity.com/paper/2302.01199/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 29 references that are in the map.
- First posted on arXiv 20 Jan 2023.

## Builds on (most cited first, 12 of 12)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019)
- [Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks](https://mlsanity.com/paper/1810.02244/) (2018)
- [Rainbow: Combining Improvements in Deep Reinforcement Learning](https://mlsanity.com/paper/1710.02298/) (2017)
- [QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1803.11485/) (2018)
- [QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1905.05408/) (2019)
- [Online Antenna Tuning in Heterogeneous Cellular Networks with Deep Reinforcement Learning](https://mlsanity.com/paper/1903.06787/) (2019)
- [Coordinated Reinforcement Learning for Optimizing Mobile Networks](https://mlsanity.com/paper/2109.15175/) (2021)
- [Remote Electrical Tilt Optimization via Safe Reinforcement Learning](https://mlsanity.com/paper/2010.05842/) (2020)
- [A Reinforcement Learning Approach to Power Control and Rate Adaptation in Cellular Networks](https://mlsanity.com/paper/1611.06497/) (2016)
- [A Graph Attention Learning Approach to Antenna Tilt Optimization](https://mlsanity.com/paper/2112.14843/) (2021)

## Built on by (most cited first, 4 of 4)

- [Online Frequency Scheduling by Learning Parallel Actions](https://mlsanity.com/paper/2406.05041/) (2024)
- [Structured Cooperative Multi-Agent Reinforcement Learning: a Bayesian Network Perspective](https://mlsanity.com/paper/2510.09937/) (2025)
- [Graph Neural Networks, Deep Reinforcement Learning and Probabilistic Topic Modeling for Strategic Multiagent Settings](https://mlsanity.com/paper/2511.10501/) (2025)
- [Temporally Consistent Graph Q-Networks for Intelligent Network Control](https://mlsanity.com/paper/2606.13848/) (2026)

## Abstract (arXiv)

Future generations of mobile networks are expected to contain more and more antennas with growing complexity and more parameters. Optimizing these parameters is necessary for ensuring the good performance of the network. The scale of mobile networks makes it challenging to optimize antenna parameters using manual intervention or hand-engineered strategies. Reinforcement learning is a promising technique to address this challenge but existing methods often use local optimizations to scale to large network deployments. We propose a new multi-agent reinforcement learning algorithm to optimize mobile network configurations globally. By using a value decomposition approach, our algorithm can be trained from a global reward function instead of relying on an ad-hoc decomposition of the network performance across the different cells. The algorithm uses a graph neural network architecture which generalizes to different network topologies and learns coordination behaviors. We empirically demonstrate the performance of the algorithm on an antenna tilt tuning problem and a joint tilt and power control problem in a simulated environment.