Reinforcement learning for graph theory, Parallelizing Wagner's approach
This work addresses a specific problem in graph theory for researchers, but it is incremental as it builds on prior methods.
The paper tackles the problem of constructing counterexamples for conjectured bounds on the spectral radius of graph Laplacians by applying reinforcement learning, achieving results through parallel training and a redefined action space.
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.