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Kristian Hartikainen
13fb9fe3db
* Add base for Soft Actor-Critic * Pick changes from old SAC branch * Update sac.py * First implementation of sac model * Remove unnecessary SAC imports * Prune unnecessary noise and exploration code * Implement SAC model and use that in SAC policy * runs but doesn't learn * clear state * fix batch size * Add missing alpha grads and vars * -200 by 2k timesteps * doc * lazy squash * one file * ignore tfp * revert done
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}
}