MultiSTOP: Solving Functional Equations with Reinforcement Learning
This work addresses a specific problem in physics for researchers by extending an existing algorithm with domain-specific constraints, making it incremental.
The authors tackled the problem of solving functional equations in physics by developing MultiSTOP, a Reinforcement Learning framework that produces numerical solutions instead of bounds, and they applied it to a one-dimensional Conformal Field Theory equation.
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.