{"ID":2876004,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.01607","arxiv_id":"2509.01607","title":"Reinforcement learning for graph theory, Parallelizing Wagner's approach","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.","short_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 redefin...","url_abs":"https://arxiv.org/abs/2509.01607","url_pdf":"https://arxiv.org/pdf/2509.01607v1","authors":"[\"Alix Bouffard\",\"Jane Breen\"]","published":"2025-09-01T16:37:17Z","proceeding":"math.CO","tasks":"[\"math.CO\",\"cs.LG\"]","methods":"[\"Reinforcement Learning\"]","has_code":false}