rlberry 0.7.2

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

A Reinforcement Learning Library for Research and Education

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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.

We provide you a number of tools to help you achieve reproducibility, statistically comparisons of RL agents, and nice visualization.

Installation

Install the latest version for a stable release.

pip install rlberry

The documentation includes more installation instructions.

Getting started

In our dev documentation, you will find quick starts to the library and a user guide with a few tutorials on using rlberry, and some examples. See also the stable documentation for the documentation corresponding to the last release.

Changelog

See the changelog for a history of the chages made to rlberry.

Other rlberry projects

rlberry-scool : It’s the repository used for teaching purposes. These are mainly basic agents and environments, in a version that makes it easier for students to learn.

rlberry-research : It’s the repository where our research team keeps some agents, environments, or tools compatible with rlberry. It’s a permanent “work in progress” repository, and some code may be not maintained anymore.

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},
    doi = {10.5281/zenodo.5544540},
    month = {10},
    title = {{rlberry - A Reinforcement Learning Library for Research and Education}},
    url = {https://github.com/rlberry-py/rlberry},
    year = {2021}
}

About us

This project was initiated and is actively maintained by INRIA SCOOL team. More information here.

Contributing

Want to contribute to rlberry? Please check our contribution guidelines. If you want to add any new agents or environments, do not hesitate to open an issue!