base on Fast and simple implementation of RL algorithms, designed to run fully on GPU. # RSL RL A fast and simple implementation of RL algorithms, designed to run fully on GPU. This code is an evolution of `rl-pytorch` provided with NVIDIA's Isaac Gym. Environment repositories using the framework: * **`Isaac Lab`** (built on top of NVIDIA Isaac Sim): https://github.com/isaac-sim/IsaacLab * **`Legged-Gym`** (built on top of NVIDIA Isaac Gym): https://leggedrobotics.github.io/legged_gym/ The main branch supports **PPO** and **Student-Teacher Distillation** with additional features from our research. These include: * [Random Network Distillation (RND)](https://proceedings.mlr.press/v229/schwarke23a.html) - Encourages exploration by adding a curiosity driven intrinsic reward. * [Symmetry-based Augmentation](https://arxiv.org/abs/2403.04359) - Makes the learned behaviors more symmetrical. We welcome contributions from the community. Please check our contribution guidelines for more information. **Maintainer**: Mayank Mittal and Clemens Schwarke <br/> **Affiliation**: Robotic Systems Lab, ETH Zurich & NVIDIA <br/> **Contact**: cschwarke@ethz.ch > **Note:** The `algorithms` branch supports additional algorithms (SAC, DDPG, DSAC, and more). However, it isn't currently actively maintained. ## Setup The package can be installed via PyPI with: ```bash pip install rsl-rl-lib ``` or by cloning this repository and installing it with: ```bash git clone https://github.com/leggedrobotics/rsl_rl cd rsl_rl pip install -e . ``` The package supports the following logging frameworks which can be configured through `logger`: * Tensorboard: https://www.tensorflow.org/tensorboard/ * Weights & Biases: https://wandb.ai/site * Neptune: https://docs.neptune.ai/ For a demo configuration of PPO, please check the [dummy_config.yaml](config/dummy_config.yaml) file. ## Contribution Guidelines For documentation, we adopt the [Google Style Guide](https://sphinxcontrib-napoleon.readthedocs.io/en/latest/example_google.html) for docstrings. Please make sure that your code is well-documented and follows the guidelines. We use the following tools for maintaining code quality: - [pre-commit](https://pre-commit.com/): Runs a list of formatters and linters over the codebase. - [black](https://black.readthedocs.io/en/stable/): The uncompromising code formatter. - [flake8](https://flake8.pycqa.org/en/latest/): A wrapper around PyFlakes, pycodestyle, and McCabe complexity checker. Please check [here](https://pre-commit.com/#install) for instructions to set these up. To run over the entire repository, please execute the following command in the terminal: ```bash # for installation (only once) pre-commit install # for running pre-commit run --all-files ``` ## Citing **We are working on writing a white paper for this library.** Until then, please cite the following work if you use this library for your research: ```text @InProceedings{rudin2022learning, title = {Learning to Walk in Minutes Using Massively Parallel Deep Reinforcement Learning}, author = {Rudin, Nikita and Hoeller, David and Reist, Philipp and Hutter, Marco}, booktitle = {Proceedings of the 5th Conference on Robot Learning}, pages = {91--100}, year = {2022}, volume = {164}, series = {Proceedings of Machine Learning Research}, publisher = {PMLR}, url = {https://proceedings.mlr.press/v164/rudin22a.html}, } ``` If you use the library with curiosity-driven exploration (random network distillation), please cite: ```text @InProceedings{schwarke2023curiosity, title = {Curiosity-Driven Learning of Joint Locomotion and Manipulation Tasks}, author = {Schwarke, Clemens and Klemm, Victor and Boon, Matthijs van der and Bjelonic, Marko and Hutter, Marco}, booktitle = {Proceedings of The 7th Conference on Robot Learning}, pages = {2594--2610}, year = {2023}, volume = {229}, series = {Proceedings of Machine Learning Research}, publisher = {PMLR}, url = {https://proceedings.mlr.press/v229/schwarke23a.html}, } ``` If you use the library with symmetry augmentation, please cite: ```text @InProceedings{mittal2024symmetry, author={Mittal, Mayank and Rudin, Nikita and Klemm, Victor and Allshire, Arthur and Hutter, Marco}, booktitle={2024 IEEE International Conference on Robotics and Automation (ICRA)}, title={Symmetry Considerations for Learning Task Symmetric Robot Policies}, year={2024}, pages={7433-7439}, doi={10.1109/ICRA57147.2024.10611493} } ``` ", Assign "at most 3 tags" to the expected json: {"id":"12518","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"