mirror of
https://github.com/wassname/ray.git
synced 2026-08-17 11:25:34 +08:00
Sven
f1b56fa5ee
* Unifying the code for PGTrainer/Policy wrt tf vs torch. Adding loss function test cases for the PGAgent (confirm equivalence of tf and torch). * Fix LINT line-len errors. * Fix LINT errors. * Fix `tf_pg_policy` imports (formerly: `pg_policy`). * Rename tf_pg_... into pg_tf_... following <alg>_<framework>_... convention, where ...=policy/loss/agent/trainer. Retire `PGAgent` class (use PGTrainer instead). * - Move PG test into agents/pg/tests directory. - All test cases will be located near the classes that are tested and then built into the Bazel/Travis test suite. * Moved post_process_advantages into pg.py (from pg_tf_policy.py), b/c the function is not a tf-specific one. * Fix remaining import errors for agents/pg/... * Fix circular dependency in pg imports. * Add pg tests to Jenkins test suite.
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.)