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30 lines
1.5 KiB
Markdown
30 lines
1.5 KiB
Markdown
RLlib: Scalable Reinforcement Learning
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======================================
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RLlib is an open-source library for reinforcement learning that offers both high scalability and a unified API for a variety of applications.
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For an overview of RLlib, see the [documentation](http://docs.ray.io/en/master/rllib.html).
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If you've found RLlib useful for your research, you can cite the [paper](https://arxiv.org/abs/1712.09381) as follows:
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```
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@inproceedings{liang2018rllib,
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Author = {Eric Liang and
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Richard Liaw and
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Robert Nishihara and
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Philipp Moritz and
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Roy Fox and
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Ken Goldberg and
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Joseph E. Gonzalez and
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Michael I. Jordan and
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Ion Stoica},
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Title = {{RLlib}: Abstractions for Distributed Reinforcement Learning},
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Booktitle = {International Conference on Machine Learning ({ICML})},
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Year = {2018}
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}
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```
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Development Install
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-------------------
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You can develop RLlib locally without needing to compile Ray by using the [setup-dev.py](https://github.com/ray-project/ray/blob/master/python/ray/setup-dev.py) script. This sets up links between the ``rllib`` dir in your git repo and the one bundled with the ``ray`` package. When using this script, make sure that your git branch is in sync with the installed Ray binaries (i.e., you are up-to-date on [master](https://github.com/ray-project/ray) and have the latest [wheel](https://docs.ray.io/en/master/installation.html) installed.)
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