# Learning Connectivity-Maximizing Network Configurations

arXiv: https://arxiv.org/abs/2112.07663 · HTML: https://mlsanity.com/paper/2112.07663/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 27 references that are in the map.
- First posted on arXiv 14 Dec 2021.

## Builds on (most cited first, 8 of 8)

- [End-to-End Training of Deep Visuomotor Policies](https://mlsanity.com/paper/1504.00702/) (2015)
- [PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes](https://mlsanity.com/paper/1711.00199/) (2017)
- [Solving Rubik's Cube with a Robot Hand](https://mlsanity.com/paper/1910.07113/) (2019)
- [Value Iteration Networks](https://mlsanity.com/paper/1602.02867/) (2016)
- [PRIMAL: Pathfinding via Reinforcement and Imitation Multi-Agent Learning](https://mlsanity.com/paper/1809.03531/) (2018)
- [Combining Optimal Control and Learning for Visual Navigation in Novel Environments](https://mlsanity.com/paper/1903.02531/) (2019)
- [Emergent Social Learning via Multi-agent Reinforcement Learning](https://mlsanity.com/paper/2010.00581/) (2020)
- [Mobile Wireless Network Infrastructure on Demand](https://mlsanity.com/paper/2002.03026/) (2020)

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

- [Solving Large-scale Spatial Problems with Convolutional Neural Networks](https://mlsanity.com/paper/2306.08191/) (2023)
- [Transferability of Convolutional Neural Networks in Stationary Learning Tasks](https://mlsanity.com/paper/2307.11588/) (2023)

## Abstract (arXiv)

In this letter we propose a data-driven approach to optimizing the algebraic connectivity of a team of robots. While a considerable amount of research has been devoted to this problem, we lack a method that scales in a manner suitable for online applications for more than a handful of agents. To that end, we propose a supervised learning approach with a convolutional neural network (CNN) that learns to place communication agents from an expert that uses an optimization-based strategy. We demonstrate the performance of our CNN on canonical line and ring topologies, 105k randomly generated test cases, and larger teams not seen during training. We also show how our system can be applied to dynamic robot teams through a Unity-based simulation. After training, our system produces connected configurations over an order of magnitude faster than the optimization-based scheme for teams of 10-20 agents.