Reinforcement Learning techniques for the flavor problem in particle physics

Abstract

This short review discusses recent applications of Reinforcement Learning (RL) techniques to the flavor problem in particle physics. Traditional approaches to fermion masses and mixing often rely on extensions of the Standard Model based on horizontal symmetries, but the vast landscape of possible models makes systematic exploration infeasible. Recent works have shown that RL can efficiently navigate this landscape by constructing models that reproduce observed quark and lepton observables. These approaches demonstrate that RL not only rediscovers models already proposed in the literature but also uncovers new, phenomenologically acceptable solutions.