mlsanityData to 14 Oct 2026

Reinforcement learning for graph theory, Parallelizing Wagner's approach

arXiv 2509.01607 ↗First posted 1 Sep 2025math.CO cs.LGcited by 0

On the map: Deep reinforcement learning methods → RL for games and combinatorial optimization

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for games and combinatorial optimization, one of 15 directions in Deep reinforcement learning methods. Builds on 8 of its 19 references that are in the map. First posted on arXiv 1 Sep 2025.

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8 of 19 references.

References · 8

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2016Gaussian Error Linear Units (GELUs)cited by 3,502
  3. 2017Population Based Training of Neural Networkscited by 367
  4. 2021Constructions in combinatorics via neural networkscited by 48
  5. 2020Action Space Shaping in Deep Reinforcement Learningcited by 46
  6. 2024PatternBoost: Constructions in Mathematics with a Little Help from AIcited by 34
  7. 2022A Simple Decentralized Cross-Entropy Methodcited by 5
  8. 2023Tempestas ex machina: A review of machine learning methods for wavefront controlcited by 2

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  1. 2026RLGT: A reinforcement learning framework for extremal graph theory
  2. 2024Graph Reinforcement Learning for Combinatorial Optimization: A Survey and Unifying Perspective
  3. 2024A Systematization of the Wagner Framework: Graph Theory Conjectures and Reinforcement Learning
  4. 2024Refutation of Spectral Graph Theory Conjectures with Search Algorithms)
  5. 2026Impact of Connectivity on Laplacian Representations in Reinforcement Learning
  6. 2026The Laplacian Keyboard: Beyond the Linear Span
  7. 2026Spectral Prioritized Sweeping in Nonstationary Reinforcement Learning
  8. 2026Reinforcement Learning for Graph Generation under a Hard Assortativity Constraint