Skip to main content

An easy-to-use reinforcement learning library for research and education

Project description

A Reinforcement Learning Library for Research and Education

pytest Documentation Status contributors Codacy codecov

PyPI PyPI - Python Version PyPI - Wheel PyPI - Status PyPI - Downloads

Try it on Google Colab! Open In Colab


What is rlberry?

Writing reinforcement learning algorithms is fun! But after the fun, we have lots of boring things to implement: run our agents in parallel, average and plot results, optimize hyperparameters, compare to baselines, create tricky environments etc etc!

rlberry is a Python library that makes your life easier by doing all these things with a few lines of code, so that you can spend most of your time developing agents. rlberry also provides implementations of several RL agents, benchmark environments and many other useful tools.

Check our getting started section!

Getting started

In our documentation, you will find a quick tutorial to the library.

Also, we provide a handful of notebooks on Google colab as examples to show you how to use rlberry:

Content Description Link
Introduction to rlberry How to create an agent, optimize its hyperparameters and compare to a baseline. Open In Colab
Evaluating and optimizing agents Train a REINFORCE agent and optimize its hyperparameters Open In Colab

Citing rlberry

If you use rlberry in scientific publications, we would appreciate citations using the following Bibtex entry:

@misc{rlberry,
author = {Domingues, Omar Darwiche and Flet-Berliac, Yannis and Leurent, Edouard and M{\'e}nard, Pierre and Shang, Xuedong and Valko, Michal},
title = {{rlberry - A Reinforcement Learning Library for Research and Education}},
year = {2021},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/rlberry-py/rlberry}}
}

Tests

To run tests, install test dependencies with pip install -e .[test] and run pytest.

To check coverage, install test dependencies and run

$ cd scripts
$ bash run_testscov.sh

and coverage report in cov_html/index.html.

Contributing

Want to contribute to rlberry? Please check our contribution guidelines. A list of interesting TODO's will be available soon. If you want to add any new agents or environments, do not hesitate to open an issue!

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for rlberry, version 0.1
Filename, size File type Python version Upload date Hashes
Filename, size rlberry-0.1-py3-none-any.whl (213.1 kB) File type Wheel Python version py3 Upload date Hashes View
Filename, size rlberry-0.1.tar.gz (124.5 kB) File type Source Python version None Upload date Hashes View

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring DigiCert DigiCert EV certificate Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page