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arXiv · 2403.18429

Reinforcement learning for graph theory, I. Reimplementation of Wagner's approach

Mohammad Ghebleh·Salem Al-Yakoob·Ali Kanso·Dragan Stevanovic
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Abstract

We reimplement here the recent approach of Adam Zsolt Wagner [arXiv:2104.14516], which applies reinforcement learning to construct (counter)examples in graph theory, in order to make it more readable, more stable and much faster. The presented concepts are illustrated by constructing counterexamples for a number of published conjectured bounds for the Laplacian spectral radius of graphs.

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Mohammad Ghebleh, Salem Al-Yakoob, Ali Kanso, Dragan Stevanovic. 2024-03-30. Reinforcement learning for graph theory, I. Reimplementation of Wagner's approach. https://arxiv.org/abs/2403.18429

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