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:

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

Documentation is provided online at 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 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

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/.

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 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.

hbh22110182/gym-anm