Revealing systematics in phenomenologically viable flux vacua with reinforcement learning

TL;DR

This paper applies reinforcement learning to efficiently explore the high-dimensional string flux vacua landscape, revealing new correlations and significantly reducing sampling time compared to traditional methods.

Contribution

It introduces reinforcement learning as a novel approach to sampling string vacua, improving efficiency and uncovering previously unknown correlations in the flux landscape.

Findings

Reinforcement learning outperforms traditional sampling methods in speed.

Strategies learned are interpretable and reveal new correlations.

Applicable to type IIB flux landscape on specific geometries.

Abstract

The organising principles underlying the structure of phenomenologically viable string vacua can be accessed by sampling such vacua. In many cases this is prohibited by the computational cost of standard sampling methods in the high dimensional model space. Here we show how this problem can be alleviated using reinforcement learning techniques to explore string flux vacua. We demonstrate in the case of the type IIB flux landscape that vacua with requirements on the expectation value of the superpotential and the string coupling can be sampled significantly faster by using reinforcement learning than by using metropolis or random sampling. Our analysis is on conifold and symmetric torus background geometries. We show that reinforcement learning is able to exploit successful strategies for identifying such phenomenologically interesting vacua. The strategies are interpretable and reveal previously unknown correlations in the flux landscape.