Projecting Latent RL Actions: Towards Generalizable and Scalable Graph Combinatorial Optimization
The paper introduces a novel approach to graph combinatorial optimization using reinforcement learning. It addresses challenges in generalization and scalability by utilizing projection agents that operate in a continuous action embedding space. The proposed method demonstrates significant improvements in inference speed and generalization performance compared to existing solutions.
- ▪Graph combinatorial optimization has gained interest due to its applications in NP-hard problems.
- ▪The new approach achieves up to 16.2 times faster inference and 40% better generalization than current methods.
- ▪The authors released LaGCO-RL, a Python library to support the new approach and enhance reproducibility.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.19721 |
| Publication time | Wed, 20 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-20T04:04:59.484Z |
| Last seen | 2026-05-20T04:04:59.484Z |
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Computer Science > Artificial Intelligence arXiv:2605.19721 (cs) [Submitted on 19 May 2026] Title:Projecting Latent RL Actions: Towards Generalizable and Scalable Graph Combinatorial Optimization Authors:Franco Terranova (UL, LORIA, Inria), Guillermo Bernardez (UC Santa Barbara), Albert Cabellos-Aparicio (UPC), Nina Miolane (UC Santa Barbara), Abdelkader Lahmadi (LORIA, UL, Inria) View a PDF of the paper titled Projecting Latent RL Actions: Towards Generalizable and Scalable Graph Combinatorial Optimization, by Franco Terranova (UL and 8 other authors View PDF Abstract:Graph combinatorial optimization (GCO) has attracted growing interest, as many NP-hard problems naturally admit graph formulations, yet their combinatorial explosion renders exact methods computationally intractable.
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