mlsanityData to 14 Oct 2026

Graph-Enhanced Model-Free Reinforcement Learning Agents for Efficient Power Grid Topological Control

arXiv 2503.20688 ↗First posted 26 Mar 2025cs.AIcited by 2

On the map: Deep reinforcement learning methods → RL for energy, networks and operations

Abstract (arXiv)

The increasing complexity of power grid management, driven by the emergence of prosumers and the demand for cleaner energy solutions, has needed innovative approaches to ensure stability and efficiency. This paper presents a novel approach within the model-free framework of reinforcement learning, aimed at optimizing power network operations without prior expert knowledge. We introduce a masked topological action space, enabling agents to explore diverse strategies for cost reduction while maintaining reliable service using the state logic as a guide for choosing proper actions. Through extensive experimentation across 20 different scenarios in a simulated 5-substation environment, we demonstrate that our approach achieves a consistent reduction in power losses, while ensuring grid stability against potential blackouts. The results underscore the effectiveness of combining dynamic observation formalization with opponent-based training, showing a viable way for autonomous management solutions in modern energy systems or even for building a foundational model for this field.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 13 of its 23 references that are in the map. First posted on arXiv 26 Mar 2025.

Builds on

13 of 23 references.

References · 13

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  3. 2017Graph Attention Networkscited by 8,923
  4. 2019Mastering Atari, Go, Chess and Shogi by Planning with a Learned Modelcited by 1,353
  5. 2015Dueling Network Architectures for Deep Reinforcement Learningcited by 785
  6. 2017Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithmcited by 734
  7. 2020A Generalization of Transformer Networks to Graphscited by 661
  8. 2017Residual Gated Graph ConvNetscited by 346
  9. 2020A Closer Look at Invalid Action Masking in Policy Gradient Algorithmscited by 171
  10. 2021Action Set Based Policy Optimization for Safe Power Grid Managementcited by 21
  11. 2022Power Grid Congestion Management via Topology Optimization with AlphaZerocited by 20
  12. 2021Learning to run a power network with trustcited by 17
1 more
  1. 2019AI-Based Autonomous Line Flow Control via Topology Adjustment for Maximizing Time-Series ATCscited by 15

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026Interpretable Policy Distillation for Power Grid Topology Controlcited by 0
  2. 2026A Comparative Study of Graph Representations for GNN-Based Power Grid Control in L2RPNcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Learning to Run Power Networks: Effective AlphaZero-inspired Topological Control
  2. 2025Learning Topology Actions for Power Grid Control: A Graph-Based Soft-Label Imitation Learning Approach
  3. 2024Graph Reinforcement Learning for Power Grids: A Comprehensive Survey
  4. 2024HUGO -- Highlighting Unseen Grid Options: Combining Deep Reinforcement Learning with a Heuristic Target Topology Approach
  5. 2025Power Grid Control with Graph-Based Distributed Reinforcement Learning
  6. 2026Physics Informed Reinforcement Learning with Gibbs Priors for Topology Control in Power Grids
  7. 2025Towards Efficient Multi-Objective Optimisation for Real-World Power Grid Topology Control
  8. 2025Optimizing Power Grid Topologies with Reinforcement Learning: A Survey of Methods and Challenges