mlsanityData to 7 Oct 2026

Design and implementation of an environment for Learning to Run a Power Network (L2RPN)

arXiv 2104.04080 ↗First posted 6 Apr 2021cs.LG cs.SY eess.SYcited by 4

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Abstract (arXiv)

This report summarizes work performed as part of an internship at INRIA, in partial requirement for the completion of a master degree in math and informatics. The goal of the internship was to develop a software environment to simulate electricity transmission in a power grid and actions performed by operators to maintain this grid in security. Our environment lends itself to automate the control of the power grid with reinforcement learning agents, assisting human operators. It is amenable to organizing benchmarks, including a challenge in machine learning planned by INRIA and RTE for 2019. Our framework, built on top of open-source libraries, is available at https://github.com/MarvinLer/pypownet. In this report we present intermediary results and its usage in the context of a reinforcement learning game.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 13 of its 29 references that are in the map. First posted on arXiv 6 Apr 2021.

Builds on

13 of 29 references.

References · 13

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 6,979
  3. 2013Playing Atari with Deep Reinforcement Learningcited by 3,211
  4. 2015Empirical Evaluation of Rectified Activations in Convolutional Networkcited by 515
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  8. 2018Deep Hierarchical Reinforcement Learning Algorithm in Partially Observable Markov Decision Processescited by 8
  9. 2017Introducing machine learning for power system operation supportcited by 7
  10. 2018Fast Power system security analysis with Guided Dropoutcited by 6
  11. 2017Online Learning of Power Transmission Dynamicscited by 5
  12. 2017A Bound Strengthening Method for Optimal Transmission Switching in Power Systemscited by 1
1 more
  1. 2018Optimization of computational budget for power system risk assessmentcited by 1

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Cited by year

Citing papers per year: 2021: 2; 2022: 0; 2023: 0; 2024: 2.

4 papers.

  1. 2021Multi-Agent Reinforcement Learning for Active Voltage Control on Power Distribution Networkscited by 52
  2. 2021Curriculum Based Reinforcement Learning of Grid Topology Controllers to Prevent Thermal Cascadingcited by 8
  3. 2024RL-ADN: A High-Performance Deep Reinforcement Learning Environment for Optimal Energy Storage Systems Dispatch in Active Distribution Networkscited by 3
  4. 2024Reinforcement Learning for Sustainable Energy: A Surveycited by 2