# Learning to run a power network with trust

arXiv: https://arxiv.org/abs/2110.12908 · HTML: https://mlsanity.com/paper/2110.12908/

## Key facts

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

## Builds on (most cited first, 14 of 14)

- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Human-Centered Artificial Intelligence: Reliable, Safe & Trustworthy](https://mlsanity.com/paper/2002.04087/) (2020)
- [Formalizing Trust in Artificial Intelligence: Prerequisites, Causes and Goals of Human Trust in AI](https://mlsanity.com/paper/2010.07487/) (2020)
- [Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims](https://mlsanity.com/paper/2004.07213/) (2020)
- [The relationship between trust in AI and trustworthy machine learning technologies](https://mlsanity.com/paper/1912.00782/) (2019)
- [Learning to run a Power Network Challenge: a Retrospective Analysis](https://mlsanity.com/paper/2103.03104/) (2021)
- [Hybrid Intelligence](https://mlsanity.com/paper/2105.00691/) (2021)
- [Evaluation of Human-AI Teams for Learned and Rule-Based Agents in Hanabi](https://mlsanity.com/paper/2107.07630/) (2021)
- [Learning to run a power network challenge for training topology controllers](https://mlsanity.com/paper/1912.04211/) (2019)
- [Adaptive Power System Emergency Control using Deep Reinforcement Learning](https://mlsanity.com/paper/1903.03712/) (2019)
- [Action Set Based Policy Optimization for Safe Power Grid Management](https://mlsanity.com/paper/2106.15200/) (2021)
- [Convex Relaxation of Optimal Power Flow, Part I: Formulations and Equivalence](https://mlsanity.com/paper/1405.0766/) (2014)
- [AI-Based Autonomous Line Flow Control via Topology Adjustment for Maximizing Time-Series ATCs](https://mlsanity.com/paper/1911.04263/) (2019)
- [Adversarial Training for a Continuous Robustness Control Problem in Power Systems](https://mlsanity.com/paper/2012.11390/) (2020)

## Built on by (most cited first, 17 of 17)

- [Power Grid Congestion Management via Topology Optimization with AlphaZero](https://mlsanity.com/paper/2211.05612/) (2022)
- [Managing power grids through topology actions: A comparative study between advanced rule-based and reinforcement learning agents](https://mlsanity.com/paper/2304.00765/) (2023)
- [Hierarchical Reinforcement Learning for Power Network Topology Control](https://mlsanity.com/paper/2311.02129/) (2023)
- [RL2Grid: Benchmarking Reinforcement Learning in Power Grid Operations](https://mlsanity.com/paper/2503.23101/) (2025)
- [Graph Reinforcement Learning for Power Grids: A Comprehensive Survey](https://mlsanity.com/paper/2407.04522/) (2024)
- [Centrally Coordinated Multi-Agent Reinforcement Learning for Power Grid Topology Control](https://mlsanity.com/paper/2502.08681/) (2025)
- [Imitation Learning for Intra-Day Power Grid Operation through Topology Actions](https://mlsanity.com/paper/2407.19865/) (2024)
- [Fault Detection for agents on power grid topology optimization: A Comprehensive analysis](https://mlsanity.com/paper/2406.16426/) (2024)
- [Reinforcement Learning for Sustainable Energy: A Survey](https://mlsanity.com/paper/2407.18597/) (2024)
- [Learning Topology Actions for Power Grid Control: A Graph-Based Soft-Label Imitation Learning Approach](https://mlsanity.com/paper/2503.15190/) (2025)
- [Graph-Enhanced Model-Free Reinforcement Learning Agents for Efficient Power Grid Topological Control](https://mlsanity.com/paper/2503.20688/) (2025)
- [A Conceptual Framework for AI-based Decision Systems in Critical Infrastructures](https://mlsanity.com/paper/2504.16133/) (2025)
- [Reinforcement Learning Based Power Grid Day-Ahead Planning and AI-Assisted Control](https://mlsanity.com/paper/2302.07654/) (2023)
- [Multi-Objective Reinforcement Learning for Power Grid Topology Control](https://mlsanity.com/paper/2502.00040/) (2025)
- [Machine Learning for Physical Simulation Challenge Results and Retrospective Analysis: Power Grid Use Case](https://mlsanity.com/paper/2505.01156/) (2025)
- [Learning to Run Power Networks: Effective AlphaZero-inspired Topological Control](https://mlsanity.com/paper/2608.14114/) (2026)
- [Multi-Agent Reinforcement Learning for Power Grid Topology Optimization](https://mlsanity.com/paper/2310.02605/) (2023)

## 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.