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A Comparative Study of Graph Representations for GNN-Based Power Grid Control in L2RPN — arXiv:2609.02538

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.02538
License (as archived): CC0-1.0
Archived: 2026-10-17T04:59:30.275Z
SHA-256 of the archived text: a8f81156d225643aec99fba1cb0fa37e6db31114811e739a3f94de3836a789f5
Stable URL of this page: https://metraipolis.com/documento/dc_55da130c50be

How to cite

A Comparative Study of Graph Representations for GNN-Based Power Grid Control in L2RPN — arXiv:2609.02538 — arXiv (en), https://arxiv.org/abs/2609.02538 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_55da130c50be (Metraipolis archive dc_55da130c50be, sha256 a8f81156d225643aec99fba1cb0fa37e6db31114811e739a3f94de3836a789f5)

BibTeX

@misc{dc55da130c50be,
  title = {A Comparative Study of Graph Representations for GNN-Based Power Grid Control in L2RPN — arXiv:2609.02538},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.02538},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_55da130c50be; sha256 a8f81156d225643aec99fba1cb0fa37e6db31114811e739a3f94de3836a789f5},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.02538. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Graph construction is a critical but underexamined design choice in deep reinforcement learning for power grid control. We present a controlled experimental comparison of different graph representations, including physical topology, electrical-sensitivity, and hybrid variants for topology control in the Learning to Run a Power Network (L2RPN) environment. Our findings indicate that matching graph complexity to task granularity is more important than maximizing representational richness, and highlight the importance of controlled representation studies at scale.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-03 in cs.LG).
Categorie arXiv: Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2609.02538 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-17T04:09:43.328Z dalla pagina https://arxiv.org/catchup/cs.LG/2026-09-03?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).