gym-anm is a framework for designing reinforcement learning (RL) environments that model Active Network Management (ANM) tasks in electricity distribution networks. It is built on top of the OpenAI Gym toolkit.

The gym-anm framework was designed with one goal in mind: bridge the gap between research in RL and in the management of power systems. We attempt to do this by providing RL researchers with an easy-to-work-with library of environments that model decision-making tasks in power grids.


Key features

  • Very little background in electricity systems modelling it required. This makes gym-anm an ideal starting point for RL students and researchers looking to enter the field.
  • The environments (tasks) generated by gym-anm follow the OpenAI Gym framework, with which a large part of the RL community is already familiar.
  • The flexibility of gym-anm, with its different customizable components, makes it a suitable framework to model a wide range of ANM tasks, from simple ones that can be used for educational purposes, to complex ones designed to conduct advanced research.


Documentation is provided online at



gym-anm requires Python 3.7+ and can run on Linux, MaxOS, and Windows.

We recommend installing gym-anm in a Python environment (e.g., virtualenv or conda).

Using pip

Using pip (preferably after activating your virtual environment):

pip install gym-anm

Building from source

Alternatively, you can build gym-anm directly from source:

git clone
cd gym-anm
pip install -e .


The following code snippet illustrates how gym-anm environments can be used. In this example, actions are randomly sampled from the action space of the environment ANM6Easy-v0. For more information about the agent-environment interface, see the official OpenAI Gym documentation.

import gym
import time

env = gym.make('gym_anm:ANM6Easy-v0')
o = env.reset()

for i in range(100):
    a = env.action_space.sample()
    o, r, done, info = env.step(a)
    time.sleep(0.5)  # otherwise the rendering is too fast for the human eye.

The above code would render the environment in your default web browser as shown in the image below:


Additional example scripts can be found in examples/.

Testing the installation

All unit tests in gym-anm can be ran from the project root directory with:

python -m tests


Contributions are always welcome! Please read the contribution guidelines first.

Citing the project

All publications derived from the use of gym-anm should cite the following two 2021 papers:

    title = {Gym-ANM: Reinforcement learning environments for active network management tasks in electricity distribution systems},
    journal = {Energy and AI},
    volume = {5},
    pages = {100092},
    year = {2021},
    issn = {2666-5468},
    doi = {},
    author = {Robin Henry and Damien Ernst},

    title = {Gym-ANM: Open-source software to leverage reinforcement learning for power system management in research and education},
    journal = {Software Impacts},
    volume = {9},
    pages = {100092},
    year = {2021},
    issn = {2665-9638},
    doi = {},
    author = {Robin Henry and Damien Ernst}