Reinforcement learning for graph theory, Parallelizing Wagner's approach
Alix Bouffard, Jane Breen
9/1/2025
math.COcs.LG
No confident code implementation found for this paper.
Abstract
Our work applies reinforcement learning to construct counterexamples concerning conjectured bounds on the spectral radius of the Laplacian matrix of a graph. We expand upon the re-implementation of Wagner's approach by Stevanovic et al. with the ability to train numerous unique models simultaneously and a novel redefining of the action space to adjust the influence of the current local optimum on the learning process.
