mlsanityData to 14 Oct 2026

Centrally Coordinated Multi-Agent Reinforcement Learning for Power Grid Topology Control

arXiv 2502.08681 ↗First posted 12 Feb 2025cs.MA cs.AI cs.LGcited by 5

Not on the map. Why?

Abstract (arXiv)

Power grid operation is becoming more complex due to the increase in generation of renewable energy. The recent series of Learning To Run a Power Network (L2RPN) competitions have encouraged the use of artificial agents to assist human dispatchers in operating power grids. However, the combinatorial nature of the action space poses a challenge to both conventional optimizers and learned controllers. Action space factorization, which breaks down decision-making into smaller sub-tasks, is one approach to tackle the curse of dimensionality. In this study, we propose a centrally coordinated multi-agent (CCMA) architecture for action space factorization. In this approach, regional agents propose actions and subsequently a coordinating agent selects the final action. We investigate several implementations of the CCMA architecture, and benchmark in different experimental settings against various L2RPN baseline approaches. The CCMA architecture exhibits higher sample efficiency and superior final performance than the baseline approaches. The results suggest high potential of the CCMA approach for further application in higher-dimensional L2RPN as well as real-world power grid settings.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 22 of its 47 references that are in the map. First posted on arXiv 12 Feb 2025.

Builds on

22 of 47 references.

References · 22

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
  3. 2019Mastering Atari, Go, Chess and Shogi by Planning with a Learned Modelcited by 1,353
  4. 2021The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Gamescited by 942
  5. 2018Tune: A Research Platform for Distributed Model Selection and Trainingcited by 395
  6. 2016Deep Reinforcement Learning for Robotic Manipulation with Asynchronous Off-Policy Updatescited by 353
  7. 2018Learning to Drive in a Daycited by 273
  8. 2021Learning to run a Power Network Challenge: a Retrospective Analysiscited by 55
  9. 2017RLlib: Abstractions for Distributed Reinforcement Learningcited by 54
  10. 2014Near-optimal Reinforcement Learning in Factored MDPscited by 41
  11. 2022Power Grid Congestion Management via Topology Optimization with AlphaZerocited by 20
  12. 2017Transforming Cooling Optimization for Green Data Center via Deep Reinforcement Learningcited by 18
10 more
  1. 2022PowRL: A Reinforcement Learning Framework for Robust Management of Power Networkscited by 18
  2. 2021Learning to run a power network with trustcited by 17
  3. 2023Managing power grids through topology actions: A comparative study between advanced rule-based and reinforcement learning agentscited by 16
  4. 2019AI-Based Autonomous Line Flow Control via Topology Adjustment for Maximizing Time-Series ATCscited by 15
  5. 2023Hierarchical Reinforcement Learning for Power Network Topology Controlcited by 11
  6. 2024HUGO -- Highlighting Unseen Grid Options: Combining Deep Reinforcement Learning with a Heuristic Target Topology Approachcited by 10
  7. 2022Reinforcement learning for Energies of the future and carbon neutrality: a Challenge Designcited by 8
  8. 2024Imitation Learning for Intra-Day Power Grid Operation through Topology Actionscited by 3
  9. 2024State and Action Factorization in Power Gridscited by 3
  10. 2021Optimal Transmission Topology for Facilitating the Growth of Renewable Power Generationcited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 3.

5 papers.

  1. 2025Learning Topology Actions for Power Grid Control: A Graph-Based Soft-Label Imitation Learning Approachcited by 2
  2. 2025Power Grid Control with Graph-Based Distributed Reinforcement Learningcited by 1
  3. 2026Learning to Run Power Networks: Effective AlphaZero-inspired Topological Controlcited by 1
  4. 2026Harnessing Implicit Cooperation: A Multi-Agent Reinforcement Learning Approach Towards Decentralized Local Energy Marketscited by 0
  5. 2026Urban Power Grid Topology and Hierarchy Identification from Open Datacited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Graph-Enhanced Model-Free Reinforcement Learning Agents for Efficient Power Grid Topological Control
  2. 2024Robust Defense Against Extreme Grid Events Using Dual-Policy Reinforcement Learning Agents
  3. 2025Optimizing Power Grid Topologies with Reinforcement Learning: A Survey of Methods and Challenges
  4. 2025Towards Efficient Multi-Objective Optimisation for Real-World Power Grid Topology Control
  5. 2024Multi-Agent Reinforcement Learning for Energy Networks: Computational Challenges, Progress and Open Problems
  6. 2025RL2Grid: Benchmarking Reinforcement Learning in Power Grid Operations
  7. 2026AutoB2G: Agentic Simulation and Reinforcement Learning for Spatio-Temporal Grid-Interactive Building Control
  8. 2026Action-Graph Policies: Learning Action Co-dependencies in Multi-Agent Reinforcement Learning