Awesome

RL for MuJoCo

This package contains implementations of various RL algorithms for continuous control tasks simulated with MuJoCo.

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) 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 and other members of the Movement Control Lab, University of Washington Seattle.