Abstract: Results from machine learning are notoriously difficult to interpret and verify, making them less attractive for applications in pure mathematics. We will introduce reinforcement learning (RL), a subfield of machine learning, and explain how to apply it to problems in knot theory. This technique produces verifiable results which are sometimeshttps://arxiv.org/pdf/2304.09304.pdf.

interpretable by domain experts. We will illustrate this by applying RL to the unknot recognition problem, and to the problem of detecting whether a knot is ribbon. The latter was used to rule out many proposed potential counterexamples to the smooth Poincare Conjecture in 4D. The presentation is based on