A Comparative Study of Graph Representations for GNN-Based Power Grid Control in L2RPN

arXiv:2609.02538 · cs.LG · Submitted 2026-09-02 · Read on arXiv

cs.LG

Submitted: 2026-09-02

Updated: 2026-09-02

Code: https://github.com/KIT-IAI-DRACOS/L2RPNGraphReprComparison

Terminology

• Reinforcement Learning — 11× in this paper · explained in 4 episodes
• Graph Construction — 5× in this paper · explained in 3 episodes
• Abstraction — 3× in this paper · explained in 1 episode
• Action Space — 3× in this paper · explained in 1 episode
• Actor — 2× in this paper · explained in 3 episodes
• Deep Reinforcement Learning — 2× in this paper · explained in 2 episodes
• GNNs — 2× in this paper · explained in 2 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Aggregation — 1× in this paper · explained in 1 episode
• Benchmark — 1× in this paper · explained in 2 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Graph Neural Networks (GNNs) — 1× in this paper · explained in 7 episodes
• Heterogeneous Graph — 1× in this paper · explained in 2 episodes
• Power Transfer Distribution Factors — 1× in this paper
• Reinforcement Learning (RL) — 1× in this paper · explained in 25 episodes

Sources

• Power Grid Congestion Management via Topology Optimization with AlphaZero
• Hierarchical Reinforcement Learning for Power Network Topology Control
• Multi-Agent Reinforcement Learning for Power Grid Topology Optimization
• Graph Neural Networks for Transmission Grid Topology Control: Busbar Information Asymmetry and Heterogeneous Representations
• Graph-Enhanced Model-Free Reinforcement Learning Agents for Efficient Power Grid Topological Control
• Semi-Supervised Classification with Graph Convolutional Networks
• Proximal Policy Optimization Algorithms
• RLlib: Abstractions for Distributed Reinforcement Learning

Related papers

• Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation
• AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learning
• Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation
• Convergence issues in Relational Concept Analysis based on AOC-posets
• Beliefs Beyond Posteriors: Local-Consistency Optimisation for Bayesian Neural Networks
• Understanding Diffusion Models via Ratio-Based Function Approximation with SignReLU Networks