mlsanityData to 7 Oct 2026

Learning to run a power network with trust

arXiv 2110.12908 ↗First posted 21 Oct 2021cs.AI cs.HC cs.LGcited by 17

Not on the map. Why?

Abstract (arXiv)

Artificial agents are promising for real-time power network operations, particularly, to compute remedial actions for congestion management. However, due to high reliability requirements, purely autonomous agents will not be deployed any time soon and operators will be in charge of taking action for the foreseeable future. Aiming at designing assistant for operators, we instead consider humans in the loop and propose an original formulation. We first advance an agent with the ability to send to the operator alarms ahead of time when the proposed actions are of low confidence. We further model the operator's available attention as a budget that decreases when alarms are sent. We present the design and results of our competition "Learning to run a power network with trust" in which we evaluate our formulation and benchmark the ability of submitted agents to send relevant alarms while operating the network to their best.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). Builds on 14 of its 35 references that are in the map. First posted on arXiv 21 Oct 2021.

Builds on

14 of 35 references.

References · 14

  1. 2019Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AIcited by 1,543
  2. 2020Human-Centered Artificial Intelligence: Reliable, Safe & Trustworthycited by 182
  3. 2020Formalizing Trust in Artificial Intelligence: Prerequisites, Causes and Goals of Human Trust in AIcited by 166
  4. 2020Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claimscited by 163
  5. 2019The relationship between trust in AI and trustworthy machine learning technologiescited by 78
  6. 2021Hybrid Intelligencecited by 55
  7. 2021Learning to run a Power Network Challenge: a Retrospective Analysiscited by 52
  8. 2021Evaluation of Human-AI Teams for Learned and Rule-Based Agents in Hanabicited by 37
  9. 2019Learning to run a power network challenge for training topology controllerscited by 33
  10. 2019Adaptive Power System Emergency Control using Deep Reinforcement Learningcited by 26
  11. 2021Action Set Based Policy Optimization for Safe Power Grid Managementcited by 18
  12. 2014Convex Relaxation of Optimal Power Flow, Part I: Formulations and Equivalencecited by 17
2 more
  1. 2019AI-Based Autonomous Line Flow Control via Topology Adjustment for Maximizing Time-Series ATCscited by 14
  2. 2020Adversarial Training for a Continuous Robustness Control Problem in Power Systemscited by 8

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 4; 2024: 4; 2025: 7; 2026: 1.

17 papers.

Sort:

By area:

  1. 2022Power Grid Congestion Management via Topology Optimization with AlphaZerocited by 20
  2. 2023Managing power grids through topology actions: A comparative study between advanced rule-based and reinforcement learning agentscited by 15
  3. 2023Hierarchical Reinforcement Learning for Power Network Topology Controlcited by 11
  4. 2025RL2Grid: Benchmarking Reinforcement Learning in Power Grid Operationscited by 8
  5. 2024Graph Reinforcement Learning for Power Grids: A Comprehensive Surveycited by 6
  6. 2025Centrally Coordinated Multi-Agent Reinforcement Learning for Power Grid Topology Controlcited by 5
  7. 2024Imitation Learning for Intra-Day Power Grid Operation through Topology Actionscited by 3
  8. 2024Fault Detection for agents on power grid topology optimization: A Comprehensive analysiscited by 2
  9. 2024Reinforcement Learning for Sustainable Energy: A Surveycited by 2
  10. 2025Learning Topology Actions for Power Grid Control: A Graph-Based Soft-Label Imitation Learning Approachcited by 2
  11. 2025Graph-Enhanced Model-Free Reinforcement Learning Agents for Efficient Power Grid Topological Controlcited by 2
  12. 2025A Conceptual Framework for AI-based Decision Systems in Critical Infrastructurescited by 2
5 more
  1. 2023Reinforcement Learning Based Power Grid Day-Ahead Planning and AI-Assisted Controlcited by 1
  2. 2025Multi-Objective Reinforcement Learning for Power Grid Topology Controlcited by 1
  3. 2025Machine Learning for Physical Simulation Challenge Results and Retrospective Analysis: Power Grid Use Casecited by 1
  4. 2026Learning to Run Power Networks: Effective AlphaZero-inspired Topological Controlcited by 1
  5. 2023Multi-Agent Reinforcement Learning for Power Grid Topology Optimizationcited by 0