Reconciling Early and Late Time Tensions with Reinforcement Learning

TL;DR

This paper introduces a reinforcement learning approach to simultaneously address early and late-time cosmological tensions, achieving a better fit than the standard $\\Lambda$CDM model and reducing tensions in a model-independent way.

Contribution

The paper presents a novel reinforcement learning method that optimizes the Hubble parameter evolution, effectively reducing cosmological tensions without relying on specific models.

Findings

Achieves a fit surpassing the standard $\\Lambda$CDM model.

Weakens both early and late-time cosmological tensions.

Operates in a completely model-independent manner.

Abstract

We study the possibility of accommodating both early and late-time tensions using a novel reinforcement learning technique. By applying this technique, we aim to optimize the evolution of the Hubble parameter from recombination to the present epoch, addressing both tensions simultaneously. To maximize the goodness of fit, our learning technique achieves a fit that surpasses even the $\Lambda$CDM model. Our results demonstrate a tendency to weaken both early and late time tensions in a completely model-independent manner.