Reinforcement learning agent simplifies knot diagrams and improves unknotting number bounds

Researchers have developed a reinforcement learning pipeline to simplify knot diagrams by learning move proposals and a value heuristic for navigating Reidemeister moves. This system has been applied to complex unknot diagrams, including the $4_1\#9_{10}$ link, where it successfully recovered the established upper bound of three for the unknotting number. Additionally, a self-improving extension of the pipeline was introduced to systematically enhance upper bounds for the unknotting numbers of prime knots. AI

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IMPACT Novel application of RL to mathematical topology problems, potentially inspiring new research directions in AI for scientific discovery.

RANK_REASON This is a research paper detailing a novel application of reinforcement learning to a mathematical problem.

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• arXiv
• Reidemeister moves
• Daniel Tubbenhauer
• 4_1#9_10

• paper
• other