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Matthew A. WrightandEric Liang
e3c9f7e83a
* custom action dist wip * Test case for custom action dist * ActionDistribution.get_parameter_shape_for_action_space pattern * Edit exception message to also suggest using a custom action distribution * Clean up ModelCatalog.get_action_dist * Pass model config to ActionDistribution constructors * Update custom action distribution test case * Name fix * Autoformatter * parameter shape static methods for torch distributions * Fix docstring * Generalize fake array for graph initialization * Fix action dist constructors * Correct parameter shape static methods for multicategorical and gaussian * Make suggested changes to custom action dist's * Correct instances of not passing model config to action dist * Autoformatter * fix tuple distribution constructor * bugfix
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 <https://github.com/ray-project/ray>__ and have the latest wheel installed.)