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

arXiv: https://arxiv.org/abs/2105.08846 · HTML: https://mlsanity.com/paper/2105.08846/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 24 references that are in the map.
- First posted on arXiv 18 May 2021.

## Builds on (most cited first, 6 of 6)

- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Deep Reinforcement Learning for Robotic Manipulation with Asynchronous Off-Policy Updates](https://mlsanity.com/paper/1610.00633/) (2016)
- [Deep Reinforcement Learning framework for Autonomous Driving](https://mlsanity.com/paper/1704.02532/) (2017)
- [pandapower - an Open Source Python Tool for Convenient Modeling, Analysis and Optimization of Electric Power Systems](https://mlsanity.com/paper/1709.06743/) (2017)
- [Scalable End-to-End Autonomous Vehicle Testing via Rare-event Simulation](https://mlsanity.com/paper/1811.00145/) (2018)
- [Gym-ANM: Reinforcement Learning Environments for Active Network Management Tasks in Electricity Distribution Systems](https://mlsanity.com/paper/2103.07932/) (2021)

## Built on by (most cited first, 6 of 6)

- [RL2Grid: Benchmarking Reinforcement Learning in Power Grid Operations](https://mlsanity.com/paper/2503.23101/) (2025)
- [CommonPower: A Framework for Safe Data-Driven Smart Grid Control](https://mlsanity.com/paper/2406.03231/) (2024)
- [Reinforcement Learning for Sustainable Energy: A Survey](https://mlsanity.com/paper/2407.18597/) (2024)
- [Gym-TORAX: Open-source software for integrating reinforcement learning with plasma control simulators in tokamak research](https://mlsanity.com/paper/2510.11283/) (2025)
- [Distributed Ensembles of Reinforcement Learning Agents for Electricity Control](https://mlsanity.com/paper/2208.14338/) (2022)
- [TreeC: a method to generate interpretable energy management systems using a metaheuristic algorithm](https://mlsanity.com/paper/2304.08310/) (2023)

## 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.