ray/python/ray/rllib
Eric Liang 8b76bab25c
[rllib] docs for td3 (#3381)
* td3 doc

* Update rllib-env.rst
2018-11-22 13:36:47 -08:00
..
agents Enable Twin Delayed DDPG for RLlib DDPG agent (#3353) 2018-11-21 20:03:20 -08:00
env [rllib] Rename ServingEnv => ExternalEnv (#3302) 2018-11-12 16:31:27 -08:00
evaluation Enable Twin Delayed DDPG for RLlib DDPG agent (#3353) 2018-11-21 20:03:20 -08:00
examples [rllib] Clean up agent resource configurations (#3296) 2018-11-13 18:00:03 -08:00
models [rllib] Add test for multi-agent support and fix IMPALA multi-agent (#3289) 2018-11-14 14:14:07 -08:00
optimizers [rllib] Update multi-gpu impala numbers (#3327) 2018-11-19 20:55:27 -08:00
test Don't setpgid() on actors (#3347) 2018-11-19 17:35:26 -08:00
tuned_examples [rllib] docs for td3 (#3381) 2018-11-22 13:36:47 -08:00
utils [rllib] Rename ServingEnv => ExternalEnv (#3302) 2018-11-12 16:31:27 -08:00
__init__.py [rllib] Clean up agent resource configurations (#3296) 2018-11-13 18:00:03 -08:00
asv.conf.json [rllib][asv] Support ASV for RLlib (#2304) 2018-06-28 17:20:09 -07:00
README.md [rllib] Fix stats collection and some docs bugs since the refactoring (#2361) 2018-07-07 13:29:20 -07:00
rollout.py [rllib] rollout.py should reduce num workers (#3263) 2018-11-09 12:29:16 -08:00
scripts.py [rllib] format with yapf (#2427) 2018-07-19 15:30:36 -07:00
train.py [minor] Change chunk already exists to DEBUG, add flags for rllib multi node testing (#3228) 2018-11-08 00:04:20 -08:00

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.

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}
}