A Reinforcement Learning Library for Research and Education
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. |  |
| Evaluating and optimizing agents | Train a REINFORCE agent and optimize its hyperparameters | |
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.5223308},
month = {8},
title = {{rlberry - A Reinforcement Learning Library for Research and Education}},
url = {https://github.com/rlberry-py/rlberry},
year = {2021}
}Tests
$ pip install pytest
$ pip install pytest-cov
$ pip install -e .[jax_agents]
$ pip install -e .[torch_agents]
$ cd scripts
$ bash run_testscov.shand 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!