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Reinforcement learning algorithms for MuJoCo tasks
| Date | Stars |
|---|---|
| 2026-07-25 | 467 |
| 2026-07-28 | 467 |
| 2026-07-30 | 467 |
| 2026-07-31 | 467 |
| 2026-08-01 | 468 |
| 2026-08-06 | 468 |
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# RL for MuJoCo
This package contains implementations of various RL algorithms for continuous control tasks simulated with [MuJoCo.](http://www.mujoco.org/)
# Installation
The main package dependencies are `MuJoCo`, `python=3.7`, `gym>=0.13`, `mujoco-py>=2.0`, and `pytorch>=1.0`. See `setup/README.md` ([link](https://github.com/aravindr93/mjrl/tree/master/setup#installation)) for detailed install instructions.
# Bibliography
If you find the package useful, please cite the following papers.
```
@INPROCEEDINGS{Rajeswaran-NIPS-17,
AUTHOR = {Aravind Rajeswaran and Kendall Lowrey and Emanuel Todorov and Sham Kakade},
TITLE = "{Towards Generalization and Simplicity in Continuous Control}",
BOOKTITLE = {NIPS},
YEAR = {2017},
}
@INPROCEEDINGS{Rajeswaran-RSS-18,
AUTHOR = {Aravind Rajeswaran AND Vikash Kumar AND Abhishek Gupta AND
Giulia Vezzani AND John Schulman AND Emanuel Todorov AND Sergey Levine},
TITLE = "{Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations}",
BOOKTITLE = {Proceedings of Robotics: Science and Systems (RSS)},
YEAR = {2018},
}
```
# Credits
This package is maintained by [Aravind Rajeswaran](http://homes.cs.washington.edu/~aravraj/) and other members of the [Movement Control Lab,](http://homes.cs.washington.edu/~todorov/) University of Washington Seattle.
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:d1bb6e60bda7eb5c, topic:robotics, topic:simulation, readme:robotics
matched fp:d1bb6e60bda7eb5c, topic:reinforcement-learning, desc:reinforcement learning, readme:reinforcement learning