# Untangling Braids with Multi-agent Q-Learning

arXiv: https://arxiv.org/abs/2109.14502 · HTML: https://mlsanity.com/paper/2109.14502/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 14 references that are in the map.
- First posted on arXiv 29 Sep 2021.

## Builds on (most cited first, 5 of 5)

- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [OpenAI Gym](https://mlsanity.com/paper/1606.01540/) (2016)
- [Learning to Unknot](https://mlsanity.com/paper/2010.16263/) (2020)
- [Solving Hard AI Planning Instances Using Curriculum-Driven Deep Reinforcement Learning](https://mlsanity.com/paper/2006.02689/) (2020)
- [ns3-gym: Extending OpenAI Gym for Networking Research](https://mlsanity.com/paper/1810.03943/) (2018)

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

- [Graph Neural Networks and 3-Dimensional Topology](https://mlsanity.com/paper/2305.05966/) (2023)
- [An application of neural networks to a problem in knot theory and group theory (untangling braids)](https://mlsanity.com/paper/2206.05373/) (2022)
- [Machine learning discovers invariants of braids and flat braids](https://mlsanity.com/paper/2307.12185/) (2023)

## Abstract (arXiv)

We use reinforcement learning to tackle the problem of untangling braids. We experiment with braids with 2 and 3 strands. Two competing players learn to tangle and untangle a braid. We interface the braid untangling problem with the OpenAI Gym environment, a widely used way of connecting agents to reinforcement learning problems. The results provide evidence that the more we train the system, the better the untangling player gets at untangling braids. At the same time, our tangling player produces good examples of tangled braids.