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gym-anm

A framework to design Reinforcement Learning environments that model Active Network Management (ANM) tasks in electricity distribution networks.

https://github.com/robinhenry/gym-anm

Science Score: 39.0%

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    Found 8 DOI reference(s) in README
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    Low similarity (18.7%) to scientific vocabulary

Keywords

active-network-management control electricity-networks gym-environments reinforcement-learning
Last synced: 5 months ago · JSON representation

Repository

A framework to design Reinforcement Learning environments that model Active Network Management (ANM) tasks in electricity distribution networks.

Basic Info
Statistics
  • Stars: 160
  • Watchers: 5
  • Forks: 35
  • Open Issues: 0
  • Releases: 9
Topics
active-network-management control electricity-networks gym-environments reinforcement-learning
Created over 6 years ago · Last pushed over 1 year ago
Metadata Files
Readme Contributing License Code of conduct

README.md

Gym-ANM

Downloads Documentation Status codecov Checks License: 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 Gymnasium 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-anm an ideal starting point for RL students and researchers looking to enter the field.
  • The environments (tasks) generated by gym-anm follow the Gymnasium 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 3.10+ and can run on Linux, MaxOS, and Windows. Some rendering features may not work properly on Windows (not tested).

If you need Python 3.8 or 3.9, you can use gym-anm < 2.0.

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 Gymnasium documentation. ``` import gymnasium as 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: alt text

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.

Owner

  • Name: Robin Henry
  • Login: robinhenry
  • Kind: user
  • Location: Oxford, UK
  • Company: Habitat Energy

Research engineer interested in disruptive technology. Currently Data Scientist @ Habitat Energy.

GitHub Events

Total
  • Release event: 1
  • Watch event: 25
  • Delete event: 1
  • Push event: 21
  • Pull request event: 5
  • Fork event: 3
  • Create event: 4
Last Year
  • Release event: 1
  • Watch event: 25
  • Delete event: 1
  • Push event: 21
  • Pull request event: 5
  • Fork event: 3
  • Create event: 4

Committers

Last synced: 7 months ago

All Time
  • Total Commits: 235
  • Total Committers: 3
  • Avg Commits per committer: 78.333
  • Development Distribution Score (DDS): 0.021
Past Year
  • Commits: 8
  • Committers: 1
  • Avg Commits per committer: 8.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
Robin Henry r****2@g****m 230
robin henry r****y@v****e 4
Satya Prakash Dash s****h@g****m 1
Committer Domains (Top 20 + Academic)
vega.ulg.ac.be: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 10
  • Total pull requests: 11
  • Average time to close issues: 3 months
  • Average time to close pull requests: 2 days
  • Total issue authors: 7
  • Total pull request authors: 2
  • Average comments per issue: 1.5
  • Average comments per pull request: 0.0
  • Merged pull requests: 10
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 0
  • Pull requests: 2
  • Average time to close issues: N/A
  • Average time to close pull requests: 6 days
  • Issue authors: 0
  • Pull request authors: 1
  • Average comments per issue: 0
  • Average comments per pull request: 0.0
  • Merged pull requests: 1
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • Kim-369 (2)
  • sifatron (2)
  • sprakashdash (1)
  • robinhenry (1)
  • pablo-ta (1)
  • diegofz (1)
  • lordmuck2020 (1)
Pull Request Authors
  • robinhenry (12)
  • halduaij (1)
  • sprakashdash (1)
Top Labels
Issue Labels
stale (2) enhancement (1) bug (1)
Pull Request Labels

Packages

  • Total packages: 1
  • Total downloads:
    • pypi 81 last-month
  • Total dependent packages: 1
  • Total dependent repositories: 3
  • Total versions: 11
  • Total maintainers: 1
pypi.org: gym-anm

A framework to build Reinforcement Learning environments for Active Network Management tasks in electricity networks.

  • Versions: 11
  • Dependent Packages: 1
  • Dependent Repositories: 3
  • Downloads: 81 Last month
Rankings
Downloads: 3.1%
Dependent packages count: 3.2%
Average: 5.9%
Stargazers count: 6.5%
Forks count: 7.4%
Dependent repos count: 9.1%
Maintainers (1)
robinhenry
Last synced: 6 months ago

Dependencies

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pyproject.toml pypi
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