mlsanityData to 7 Oct 2026

HUGO -- Highlighting Unseen Grid Options: Combining Deep Reinforcement Learning with a Heuristic Target Topology Approach

arXiv 2405.00629 ↗First posted 1 May 2024cs.LG cs.AIcited by 10

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Abstract (arXiv)

With the growth of Renewable Energy (RE) generation, the operation of power grids has become increasingly complex. One solution could be automated grid operation, where Deep Reinforcement Learning (DRL) has repeatedly shown significant potential in Learning to Run a Power Network (L2RPN) challenges. However, only individual actions at the substation level have been subjected to topology optimization by most existing DRL algorithms. In contrast, we propose a more holistic approach by proposing specific Target Topologies (TTs) as actions. These topologies are selected based on their robustness. As part of this paper, we present a search algorithm to find the TTs and upgrade our previously developed DRL agent CurriculumAgent (CAgent) to a novel topology agent. We compare the upgrade to the previous CAgent and can increase their L2RPN score significantly by 10%. Further, we achieve a 25% better median survival time with our TTs included. Later analysis shows that almost all TTs are close to the base topology, explaining their robustness

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 21 of its 40 references that are in the map. First posted on arXiv 1 May 2024.

Builds on

21 of 40 references.

References · 21

  1. 2017Proximal Policy Optimization Algorithmscited by 14,441
  2. 2016Asynchronous Methods for Deep Reinforcement Learningcited by 2,218
  3. 2019Mastering Atari, Go, Chess and Shogi by Planning with a Learned Modelcited by 1,327
  4. 2019Dota 2 with Large Scale Deep Reinforcement Learningcited by 1,084
  5. 2017Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithmcited by 723
  6. 2020Learning Quadrupedal Locomotion over Challenging Terraincited by 494
  7. 2018BOHB: Robust and Efficient Hyperparameter Optimization at Scalecited by 466
  8. 2017Population Based Training of Neural Networkscited by 351
  9. 2017pandapower - an Open Source Python Tool for Convenient Modeling, Analysis and Optimization of Electric Power Systemscited by 79
  10. 2021Learning to run a Power Network Challenge: a Retrospective Analysiscited by 52
  11. 2019Learning to run a power network challenge for training topology controllerscited by 33
  12. 2020Reinforcement Learning for Electricity Network Operationcited by 20
9 more
  1. 2022Power Grid Congestion Management via Topology Optimization with AlphaZerocited by 20
  2. 2021Action Set Based Policy Optimization for Safe Power Grid Managementcited by 18
  3. 2022PowRL: A Reinforcement Learning Framework for Robust Management of Power Networkscited by 17
  4. 2020Exploring grid topology reconfiguration using a simple deep reinforcement learning approachcited by 15
  5. 2023Managing power grids through topology actions: A comparative study between advanced rule-based and reinforcement learning agentscited by 15
  6. 2019AI-Based Autonomous Line Flow Control via Topology Adjustment for Maximizing Time-Series ATCscited by 14
  7. 2023Hierarchical Reinforcement Learning for Power Network Topology Controlcited by 11
  8. 2020Adversarial Training for a Continuous Robustness Control Problem in Power Systemscited by 8
  9. 2022Reinforcement learning for Energies of the future and carbon neutrality: a Challenge Designcited by 8

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 4; 2025: 3; 2026: 3.

10 papers.

  1. 2024Graph Reinforcement Learning for Power Grids: A Comprehensive Surveycited by 6
  2. 2025Centrally Coordinated Multi-Agent Reinforcement Learning for Power Grid Topology Controlcited by 5
  3. 2024Imitation Learning for Intra-Day Power Grid Operation through Topology Actionscited by 3
  4. 2024State and Action Factorization in Power Gridscited by 3
  5. 2024Fault Detection for agents on power grid topology optimization: A Comprehensive analysiscited by 2
  6. 2025Learning Topology Actions for Power Grid Control: A Graph-Based Soft-Label Imitation Learning Approachcited by 2
  7. 2026Audited Selective Verification for Risk-Controlled N-1 Thermal Contingency Screening under Deployment Shiftcited by 1
  8. 2025Power Grid Control with Graph-Based Distributed Reinforcement Learningcited by 0
  9. 2026Physics Informed Reinforcement Learning with Gibbs Priors for Topology Control in Power Gridscited by 0
  10. 2026Interpretable Policy Distillation for Power Grid Topology Controlcited by 0