# Learning to run a Power Network Challenge: a Retrospective Analysis

arXiv: https://arxiv.org/abs/2103.03104 · HTML: https://mlsanity.com/paper/2103.03104/

## Key facts

- Cited by 55 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 37 references that are in the map.
- First posted on arXiv 2 Mar 2021.

## Builds on (most cited first, 18 of 18)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Dueling Network Architectures for Deep Reinforcement Learning](https://mlsanity.com/paper/1511.06581/) (2015)
- [Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm](https://mlsanity.com/paper/1712.01815/) (2017)
- [Agent57: Outperforming the Atari Human Benchmark](https://mlsanity.com/paper/2003.13350/) (2020)
- [pandapower - an Open Source Python Tool for Convenient Modeling, Analysis and Optimization of Electric Power Systems](https://mlsanity.com/paper/1709.06743/) (2017)
- [The Power Grid Library for Benchmarking AC Optimal Power Flow Algorithms](https://mlsanity.com/paper/1908.02788/) (2019)
- [Safe and Nested Subgame Solving for Imperfect-Information Games](https://mlsanity.com/paper/1705.02955/) (2017)
- [Learning to run a power network challenge for training topology controllers](https://mlsanity.com/paper/1912.04211/) (2019)
- [Flatland-RL : Multi-Agent Reinforcement Learning on Trains](https://mlsanity.com/paper/2012.05893/) (2020)
- [Adaptive Power System Emergency Control using Deep Reinforcement Learning](https://mlsanity.com/paper/1903.03712/) (2019)
- [Reinforcement Learning for Electricity Network Operation](https://mlsanity.com/paper/2003.07339/) (2020)
- [AI-Based Autonomous Line Flow Control via Topology Adjustment for Maximizing Time-Series ATCs](https://mlsanity.com/paper/1911.04263/) (2019)
- [Exploring grid topology reconfiguration using a simple deep reinforcement learning approach](https://mlsanity.com/paper/2011.13465/) (2020)
- [Efficient and Robust Reinforcement Learning with Uncertainty-based Value Expansion](https://mlsanity.com/paper/1912.05328/) (2019)
- [Artificial Intelligence for Prosthetics - challenge solutions](https://mlsanity.com/paper/1902.02441/) (2019)
- [An Efficient Asynchronous Method for Integrating Evolutionary and Gradient-based Policy Search](https://mlsanity.com/paper/2012.05417/) (2020)
- [pymgrid: An Open-Source Python Microgrid Simulator for Applied Artificial Intelligence Research](https://mlsanity.com/paper/2011.08004/) (2020)
- [Adversarial Training for a Continuous Robustness Control Problem in Power Systems](https://mlsanity.com/paper/2012.11390/) (2020)

## Built on by (most cited first, 30 of 55)

- [Application-Driven Innovation in Machine Learning](https://mlsanity.com/paper/2403.17381/) (2024)
- [Power Grid Congestion Management via Topology Optimization with AlphaZero](https://mlsanity.com/paper/2211.05612/) (2022)
- [PowRL: A Reinforcement Learning Framework for Robust Management of Power Networks](https://mlsanity.com/paper/2212.02397/) (2022)
- [Learning to run a power network with trust](https://mlsanity.com/paper/2110.12908/) (2021)
- [Managing power grids through topology actions: A comparative study between advanced rule-based and reinforcement learning agents](https://mlsanity.com/paper/2304.00765/) (2023)
- [PowerGym: A Reinforcement Learning Environment for Volt-Var Control in Power Distribution Systems](https://mlsanity.com/paper/2109.03970/) (2021)
- [A Multi-scale Time-series Dataset with Benchmark for Machine Learning in Decarbonized Energy Grids](https://mlsanity.com/paper/2110.06324/) (2021)
- [Hierarchical Reinforcement Learning for Power Network Topology Control](https://mlsanity.com/paper/2311.02129/) (2023)
- [HUGO -- Highlighting Unseen Grid Options: Combining Deep Reinforcement Learning with a Heuristic Target Topology Approach](https://mlsanity.com/paper/2405.00629/) (2024)
- [RL2Grid: Benchmarking Reinforcement Learning in Power Grid Operations](https://mlsanity.com/paper/2503.23101/) (2025)
- [Reinforcement learning for Energies of the future and carbon neutrality: a Challenge Design](https://mlsanity.com/paper/2207.10330/) (2022)
- [Graph Reinforcement Learning for Power Grids: A Comprehensive Survey](https://mlsanity.com/paper/2407.04522/) (2024)
- [Massively Digitized Power Grid: Opportunities and Challenges of Use-inspired AI](https://mlsanity.com/paper/2205.05180/) (2022)
- [A Graph Policy Network Approach for Volt-Var Control in Power Distribution Systems](https://mlsanity.com/paper/2109.12073/) (2021)
- [Enhancing Safety in Reinforcement Learning with Human Feedback via Rectified Policy Optimization](https://mlsanity.com/paper/2410.19933/) (2024)
- [Centrally Coordinated Multi-Agent Reinforcement Learning for Power Grid Topology Control](https://mlsanity.com/paper/2502.08681/) (2025)
- [Reinforcement learning with combinatorial actions for coupled restless bandits](https://mlsanity.com/paper/2503.01919/) (2025)
- [CommonPower: A Framework for Safe Data-Driven Smart Grid Control](https://mlsanity.com/paper/2406.03231/) (2024)
- [Soft Actor-Critic With Integer Actions](https://mlsanity.com/paper/2109.08512/) (2021)
- [Blackout Mitigation via Physics-guided RL](https://mlsanity.com/paper/2401.09640/) (2024)
- [Imitation Learning for Intra-Day Power Grid Operation through Topology Actions](https://mlsanity.com/paper/2407.19865/) (2024)
- [State and Action Factorization in Power Grids](https://mlsanity.com/paper/2409.04467/) (2024)
- [InvestESG: A multi-agent reinforcement learning benchmark for studying climate investment as a social dilemma](https://mlsanity.com/paper/2411.09856/) (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)
- [Graph Neural Networks for Transmission Grid Topology Control: Busbar Information Asymmetry and Heterogeneous Representations](https://mlsanity.com/paper/2501.07186/) (2025)
- [Results and Retrospective Analysis of the CODS 2025 AssetOpsBench Challenge](https://mlsanity.com/paper/2605.08518/) (2026)
- [Reinforcement Learning for Resilient Power Grids](https://mlsanity.com/paper/2212.04069/) (2022)
- [Reinforcement Learning Based Power Grid Day-Ahead Planning and AI-Assisted Control](https://mlsanity.com/paper/2302.07654/) (2023)
- [RL for Mitigating Cascading Failures: Targeted Exploration via Sensitivity Factors](https://mlsanity.com/paper/2411.18050/) (2024)

## Abstract (arXiv)

Power networks, responsible for transporting electricity across large geographical regions, are complex infrastructures on which modern life critically depend. Variations in demand and production profiles, with increasing renewable energy integration, as well as the high voltage network technology, constitute a real challenge for human operators when optimizing electricity transportation while avoiding blackouts. Motivated to investigate the potential of AI methods in enabling adaptability in power network operation, we have designed a L2RPN challenge to encourage the development of reinforcement learning solutions to key problems present in the next-generation power networks. The NeurIPS 2020 competition was well received by the international community attracting over 300 participants worldwide. The main contribution of this challenge is our proposed comprehensive 'Grid2Op' framework, and associated benchmark, which plays realistic sequential network operations scenarios. The Grid2Op framework, which is open-source and easily re-usable, allows users to define new environments with its companion GridAlive ecosystem. Grid2Op relies on existing non-linear physical power network simulators and let users create a series of perturbations and challenges that are representative of two important problems: a) the uncertainty resulting from the increased use of unpredictable renewable energy sources, and b) the robustness required with contingent line disconnections. In this paper, we give the competition highlights. We present the benchmark suite and analyse the winning solutions, including one super-human performance demonstration. We propose our organizational insights for a successful competition and conclude on open research avenues. Given the challenge success, we expect our work will foster research to create more sustainable solutions for power network operations.