mlsanityData to 14 Oct 2026

Untangling Braids with Multi-agent Q-Learning

arXiv 2109.14502 ↗First posted 29 Sep 2021cs.LG cs.AI math.GTcited by 3

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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.

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

5 of 14 references.

References · 5

  1. 2013Playing Atari with Deep Reinforcement Learningcited by 3,274
  2. 2016OpenAI Gymcited by 1,736
  3. 2020Learning to Unknotcited by 40
  4. 2020Solving Hard AI Planning Instances Using Curriculum-Driven Deep Reinforcement Learningcited by 11
  5. 2018ns3-gym: Extending OpenAI Gym for Networking Researchcited by 6

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Cited by year

Citing papers per year: 2022: 1; 2023: 2.

3 papers.