MultiSTOP: Solving Functional Equations with Reinforcement Learning

TL;DR

MultiSTOP is a reinforcement learning framework designed to solve functional equations in physics, producing accurate numerical solutions by incorporating multiple domain-specific constraints, including integral forms.

Contribution

It extends the BootSTOP algorithm with multiple constraints to improve solution accuracy for functional equations in physics.

Findings

Successfully applied to a conformal field theory equation.

Produces numerical solutions rather than bounds.

Enhanced accuracy through additional constraints.

Abstract

We develop MultiSTOP, a Reinforcement Learning framework for solving functional equations in physics. This new methodology produces actual numerical solutions instead of bounds on them. We extend the original BootSTOP algorithm by adding multiple constraints derived from domain-specific knowledge, even in integral form, to improve the accuracy of the solution. We investigate a particular equation in a one-dimensional Conformal Field Theory.