Paper
A Comparative Study of Graph Representations for GNN-Based Power Grid Control in L2RPN
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 g · Authors: Adrian Degenkolb, Qiong Huang, Benjamin Schäfer · Published 2026-09-02 · arXiv:2609.02538
- Published
- 2026-09-02
- Authors
- 3
- arXiv
- 2609.02538
- Category
- cs.LG
Abstract
arXiv:2609.02538
Adrian Degenkolb, Qiong Huang, Benjamin Schäfer
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.