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https://github.com/robinhenry/gym-anm
Design Reinforcement Learning environments that model Active Network Management (ANM) tasks in electricity distribution networks.
https://github.com/robinhenry/gym-anm
electricity-networks gym-environments reinforcement-learning
Last synced: 3 months ago
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Design Reinforcement Learning environments that model Active Network Management (ANM) tasks in electricity distribution networks.
- Host: GitHub
- URL: https://github.com/robinhenry/gym-anm
- Owner: robinhenry
- License: mit
- Created: 2019-07-17T23:19:13.000Z (over 5 years ago)
- Default Branch: main
- Last Pushed: 2023-06-18T12:25:28.000Z (over 1 year ago)
- Last Synced: 2024-06-11T17:49:46.433Z (5 months ago)
- Topics: electricity-networks, gym-environments, reinforcement-learning
- Language: Python
- Homepage: https://gym-anm.readthedocs.io/en/latest/
- Size: 6.92 MB
- Stars: 130
- Watchers: 6
- Forks: 31
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE.md
- Code of conduct: CODE_OF_CONDUCT.md
Awesome Lists containing this project
- open-sustainable-technology - Gym-ANM - Design Reinforcement Learning environments that model Active Network Management tasks in electricity distribution networks. (Energy Systems / Grid Management and Microgrid)
README
# Gym-ANM
[](https://pepy.tech/project/gym-anm)
[](https://gym-anm.readthedocs.io/en/latest/)
[](https://codecov.io/gh/robinhenry/gym-anm)
[](https://github.com/robinhenry/gym-anm/actions/workflows/ci_checks.yml)
[](https://opensource.org/licenses/MIT)
`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](https://github.com/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](https://doi.org/10.1016/j.egyai.2021.100092)
* [Gym-ANM: Open-source software to leverage reinforcement learning for power system management in research and education](https://doi.org/10.1016/j.simpa.2021.100092)
## 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](https://github.com/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/](https://gym-anm.readthedocs.io/en/latest/).
## Installation
### Requirements
`gym-anm` requires Python 3.8+ and can run on Linux, MaxOS, and Windows. Some rendering features may not work properly
on Windows (not tested).
We recommend installing `gym-anm` in a Python environment (e.g., [virtualenv](https://virtualenv.pypa.io/en/latest/)
or [conda](https://conda.io/en/latest/#)).
### 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](https://github.com/openai/gym).
```
import gym
import time
def run():
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.
env.close()
if __name__ == '__main__':
run()
```
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/](examples).
## Testing the installation
All unit tests in `gym-anm` can be ran from the project root directory with:
```
python -m pytest tests
```
## Contributing
Contributions are always welcome! Please read the [contribution guidelines](CONTRIBUTING.md) 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](https://www.robinxhenry.com/).
## License
This project is licensed under the MIT License - see the [LICENSE.md](LICENSE.md) file for details.