RLlib: Scalable Reinforcement Learning

RLlib is an open-source library for reinforcement learning that offers both high scalability and a unified API for a variety of applications.

For an overview of RLlib, see the documentation.

If you've found RLlib useful for your research, you can cite the paper 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}
}

Development Install

You can develop RLlib locally without needing to compile Ray by using the 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 and have the latest wheel installed.)