RLlib: Scalable Reinforcement Learning
======================================

RLlib is an open-source library for reinforcement learning that offers both a collection of reference algorithms and scalable primitives for composing new ones.

For an overview of RLlib, see the [documentation](http://ray.readthedocs.io/en/latest/rllib.html).

If you've found RLlib useful for your research, you can cite the [paper](https://arxiv.org/abs/1712.09381) as follows:

```
@inproceedings{liang2018rllib,
    Author = {Eric Liang and
              Richard Liaw and
              Robert Nishihara and
              Philipp Moritz and
              Roy Fox and
              Ken Goldberg and
              Joseph E. Gonzalez and
              Michael I. Jordan and
              Ion Stoica},
    Title = {{RLlib}: Abstractions for Distributed Reinforcement Learning},
    Booktitle = {International Conference on Machine Learning ({ICML})},
    Year = {2018}
}
```
