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Sven Mika
5ac5ac9560
* WIP. * Fix float32 conversion in OneHot preprocessor (would cause float64 in eager, then NN-matmul-failure). Add proper seq-len + state-in construction in eager_tf_policy.py::_compute_gradients(). * LINT. * eager_tf_policy.py: Only set samples["seq_lens"] if RNN. Otherwise, eager-tracing will throw flattened-dict key-mismatch error. * Move issue code to examples folder. Co-authored-by: Eric Liang <ekhliang@gmail.com>
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.)