arXiv Machine Learning June 16, 2026 By Timo Brand, Henry F\"orster, Stephen Kobourov, Daniel Kohrt, Robin Schukrafft, Markus Wallinger, Johannes Zink

Using Reinforcement Learning to Optimize the Global and Local Crossing Number

Read the original on arXiv Machine Learning →

arXiv:2509. 06108v2 Announce Type: replace-cross Abstract: Graph drawing concerns the algorithmic visualization of graphs.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv AI

Jun 16

RIDGECUT: Learning Graph Partitioning with Rings and Wedges

arXiv:2505. 13986v4 Announce Type: replace-cross Abstract: Reinforcement learning (RL) has shown promise for combinatorial optimization problems on graphs by learning heuristics that generalize across instances.

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arXiv AI

Jun 29

Understanding Rollout Error in Graph World Models

arXiv:2606. 27780v1 Announce Type: new Abstract: World models are often used for planning by rolling learned dynamics forward.

By Xinyuan Song, Zekun Cai

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arXiv AI

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Learning from Local Walks on Dynamic Graphs with Bandit Feedback

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arXiv AI

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Tackling GNARLy Problems: Graph Neural Algorithmic Reasoning Reimagined through Reinforcement Learning

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reinforcement-learning

arXiv Machine Learning

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Maximising the Set-Piece Return: Optimising Football Corner Tactics with Graph Reinforcement Learning

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arXiv Machine Learning

Jun 10

Geometry-Aware Reinforcement Learning for 2D Irregular Nesting

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By Auguste Lehuger, Guillaume Henon-Just

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Related stories

RIDGECUT: Learning Graph Partitioning with Rings and Wedges

Understanding Rollout Error in Graph World Models

Learning from Local Walks on Dynamic Graphs with Bandit Feedback

Tackling GNARLy Problems: Graph Neural Algorithmic Reasoning Reimagined through Reinforcement Learning

Maximising the Set-Piece Return: Optimising Football Corner Tactics with Graph Reinforcement Learning

Geometry-Aware Reinforcement Learning for 2D Irregular Nesting