Metadata-Version: 2.1
Name: rlberry
Version: 0.7.0
Summary: An easy-to-use reinforcement learning library for research and education
Home-page: https://github.com/rlberry-py
License: LICENSE
Author: Omar Darwiche Domingues, Yannis Flet-Berliac, Edouard Leurent, Pierre Menard, Xuedong Shang
Requires-Python: >=3.9,<3.13
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: License :: Other/Proprietary License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
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Project-URL: Repository, https://github.com/rlberry-py
Description-Content-Type: text/markdown

<!-- Logo -->
<p align="center">
   <img src="https://raw.githubusercontent.com/rlberry-py/rlberry/main/assets/logo_wide.svg" width="50%">
</p>


<!-- Short description -->
<p align="center">
   A Reinforcement Learning Library for Research and Education
</p>

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   <a href="https://pypi.org/project/rlberry/">
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<p align="center">
   <a href="https://colab.research.google.com/github/rlberry-py/notebooks/blob/main/introduction_to_rlberry.ipynb">
      <b>Try it on Google Colab!</b>
      <img alt="Open In Colab" src="https://colab.research.google.com/assets/colab-badge.svg">
   </a>
</p>

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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.

## Installation

Install the latest version for a stable release.

```bash
pip install rlberry
```

The documentation includes more [installation instructions](https://rlberry-py.github.io/rlberry/installation.html).


## Getting started

In our [documentation](https://rlberry-py.github.io/rlberry/), you will find [quick starts](https://rlberry-py.github.io/rlberry/basics/quick_start_rl/quickstart.html#quick-start) to the library and a [user guide](https://rlberry-py.github.io/rlberry/user_guide.html) with a few tutorials on using rlberry. See also the [stable documentation](https://rlberry-py.github.io/rlberry/stable/) for the documentation corresponding to the last release.

Also, we provide a handful of notebooks on [Google colab](https://colab.research.google.com/) 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.| <a href="https://colab.research.google.com/github/rlberry-py/notebooks/blob/main/introduction_to_rlberry.ipynb"><img alt="Open In Colab" src="https://colab.research.google.com/assets/colab-badge.svg"></a>|
| Evaluating and optimizing agents | Train a REINFORCE agent and optimize its hyperparameters|  <a href="https://colab.research.google.com/github/rlberry-py/notebooks/blob/main/rlberry_evaluate_and_optimize_agent.ipynb"><img alt="Open In Colab" src="https://colab.research.google.com/assets/colab-badge.svg"></a>

## Changelog

See the [changelog](https://rlberry-py.github.io/rlberry/changelog.html) for a history of the chages made to rlberry.

## Citing rlberry

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

```bibtex
@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}
}
```


## Development notes

The modules listed below are experimental at the moment, that is, they are not thoroughly tested and are susceptible to evolve.

* `None`: currently, nothing is experimental here

## About us
This project was initiated and is actively maintained by [INRIA SCOOL team](https://team.inria.fr/scool/).
More information [here](https://rlberry-py.github.io/rlberry/stable/about.html).

## Contributing

Want to contribute to `rlberry`? Please check [our contribution guidelines](https://rlberry-py.github.io/rlberry/stable/contributing.html). **If you want to add any new agents or environments, do not hesitate
to [open an issue](https://github.com/rlberry-py/rlberry/issues/new/choose)!**

