Python Apache-2.0

mjrl

Reinforcement learning algorithms for MuJoCo tasks

A

aravindr93

Dernière activité 20 juil. 2026
aravindr93/mjrl

469

étoiles

110

forks

22

issues ouvertes

mujocoreinforcement-learningroboticssimulation

Ce README est souvent en anglais.

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.

Projets similaires

Google DeepMind's software stack for physics-based simulation and Reinforcement Learning environments, using MuJoCo.

Pythonartificial-intelligencedeep-learningmachine-learning
Ggoogle-deepmind
4,7 k étoiles767

A collection of robotics simulation environments for reinforcement learning

Pythond4rlgymnasiummujoco
FFarama-Foundation
2 k étoiles150

Isaac Lab API, powered by MuJoCo-Warp, for RL and robotics research

Pythonisaaclabmujocomujoco-warp
Mmujocolab
3,1 k étoiles544