Gym-ANM: Open-source software to leverage reinforcement learning for power system management in research and education
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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
- 2013Playing Atari with Deep Reinforcement Learningcited by 3,211
- 2016Deep Reinforcement Learning for Robotic Manipulation with Asynchronous Off-Policy Updatescited by 342
- 2017Deep Reinforcement Learning framework for Autonomous Drivingcited by 224
- 2017pandapower - an Open Source Python Tool for Convenient Modeling, Analysis and Optimization of Electric Power Systemscited by 79
- 2018Scalable End-to-End Autonomous Vehicle Testing via Rare-event Simulationcited by 76
- 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.
- 2025RL2Grid: Benchmarking Reinforcement Learning in Power Grid Operationscited by 8
- 2024CommonPower: A Framework for Safe Data-Driven Smart Grid Controlcited by 3
- 2024Reinforcement Learning for Sustainable Energy: A Surveycited by 2
- 2025Gym-TORAX: Open-source software for integrating reinforcement learning with plasma control simulators in tokamak researchcited by 1
- 2022Distributed Ensembles of Reinforcement Learning Agents for Electricity Controlcited by 0
- 2023TreeC: a method to generate interpretable energy management systems using a metaheuristic algorithmcited by 0
- 2025Gym-TORAX: Open-source software for integrating reinforcement learning with plasma control simulators in tokamak research13 Oct 2025
- 2025RL2Grid: Benchmarking Reinforcement Learning in Power Grid Operations29 Mar 2025
- 2024Reinforcement Learning for Sustainable Energy: A Survey26 Jul 2024
- 2024CommonPower: A Framework for Safe Data-Driven Smart Grid Control5 Jun 2024
- 2023TreeC: a method to generate interpretable energy management systems using a metaheuristic algorithm17 Apr 2023
- 2022Distributed Ensembles of Reinforcement Learning Agents for Electricity Control30 Aug 2022