Reinforcement Learning techniques for the flavor problem in particle physics

TL;DR

This paper reviews how Reinforcement Learning (RL) techniques are applied to efficiently explore and identify models addressing the flavor problem in particle physics, including rediscovering known models and finding new solutions.

Contribution

It highlights the novel use of RL to systematically navigate the complex landscape of particle physics models related to fermion masses and mixing.

Findings

RL can rediscover existing models in the flavor problem

RL uncovers new phenomenologically acceptable solutions

RL enhances systematic exploration of model space 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.