mlsanityData to 7 Oct 2026

Gym-ANM: Open-source software to leverage reinforcement learning for power system management in research and education

arXiv 2105.08846 ↗First posted 18 May 2021cs.LG cs.SY eess.SYcited by 6

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Abstract (arXiv)

Gym-ANM is a Python package that facilitates the design of reinforcement learning (RL) environments that model active network management (ANM) tasks in electricity networks. Here, we describe how to implement new environments and how to write code to interact with pre-existing ones. We also provide an overview of ANM6-Easy, an environment designed to highlight common ANM challenges. Finally, we discuss the potential impact of Gym-ANM on the scientific community, both in terms of research and education. We hope this package will facilitate collaboration between the power system and RL communities in the search for algorithms to control future energy systems.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 6 of its 24 references that are in the map. First posted on arXiv 18 May 2021.

Builds on

6 of 24 references.

References · 6

  1. 2013Playing Atari with Deep Reinforcement Learningcited by 3,211
  2. 2016Deep Reinforcement Learning for Robotic Manipulation with Asynchronous Off-Policy Updatescited by 342
  3. 2017Deep Reinforcement Learning framework for Autonomous Drivingcited by 224
  4. 2017pandapower - an Open Source Python Tool for Convenient Modeling, Analysis and Optimization of Electric Power Systemscited by 79
  5. 2018Scalable End-to-End Autonomous Vehicle Testing via Rare-event Simulationcited by 76
  6. 2021Gym-ANM: Reinforcement Learning Environments for Active Network Management Tasks in Electricity Distribution Systemscited by 18

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Citing papers per year: 2022: 1; 2023: 1; 2024: 2; 2025: 2.

6 papers.