DeepAI AI Chat
Log In Sign Up

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

by   Robin Henry, et al.
University of Liège

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.


page 1

page 2

page 3

page 4


Gym-ANM: Reinforcement Learning Environments for Active Network Management Tasks in Electricity Distribution Systems

Active network management (ANM) of electricity distribution networks inc...

pymgrid: An Open-Source Python Microgrid Simulator for Applied Artificial Intelligence Research

Microgrids, self contained electrical grids that are capable of disconne...

Benchmarking Model-Based Reinforcement Learning

Model-based reinforcement learning (MBRL) is widely seen as having the p...

Behaviour Suite for Reinforcement Learning

This paper introduces the Behaviour Suite for Reinforcement Learning, or...

Reinforcement learning for Energies of the future and carbon neutrality: a Challenge Design

Current rapid changes in climate increase the urgency to change energy p...

pyRDDLGym: From RDDL to Gym Environments

We present pyRDDLGym, a Python framework for auto-generation of OpenAI G...