Gym-ANM
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.
Papers:
- Gym-ANM: Reinforcement Learning Environments for Active Network Management Tasks in Electricity Distribution Systems
- Gym-ANM: Open-source software to leverage reinforcement learning for power system management in research and education
Key features
- Very little background in electricity systems modelling it required. This makes
gym-anman ideal starting point for RL students and researchers looking to enter the field. - The environments (tasks) generated by
gym-anmfollow 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
Documentation is provided online at https://gym-anm.readthedocs.io/en/latest/.
Installation
Requirements
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 https://github.com/robinhenry/gym-anm.git
cd gym-anm
pip install -e .
Example
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)
env.render()
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
Contributing
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:
@article{HENRY2021100092,
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 = {https://doi.org/10.1016/j.egyai.2021.100092},
author = {Robin Henry and Damien Ernst},
}
@article{HENRY2021100092,
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 = {https://doi.org/10.1016/j.simpa.2021.100092},
author = {Robin Henry and Damien Ernst}
}
Maintainers
gym-anm is currently maintained by Robin Henry.
License
This project is licensed under the MIT License - see the LICENSE.md file for details.
1 Jun 05, 2022
194 Dec 20, 2022
135 Oct 27, 2022
6 Aug 22, 2022
150 Dec 20, 2022
118 Dec 26, 2022
230 Dec 31, 2022
9 May 25, 2022
5 Aug 14, 2022
0 Apr 12, 2022
262 Dec 31, 2022
9 Dec 21, 2022
4 Nov 18, 2022
133 Jan 05, 2023
23 Oct 17, 2022
42 Dec 18, 2022
8 Jan 04, 2023
7 Oct 14, 2022
258 Dec 29, 2022
5 Feb 14, 2022