79 lines
2.3 KiB
Markdown
79 lines
2.3 KiB
Markdown
# RSL RL
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Fast and simple implementation of RL algorithms, designed to run fully on GPU.
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Currently, the following algorithms are implemented:
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- Distributed Distributional DDPG (D4PG)
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- Deep Deterministic Policy Gradient (DDPG)
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- Distributional PPO (DPPO)
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- Distributional Soft Actor Critic (DSAC)
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- Proximal Policy Optimization (PPO)
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- Soft Actor Critic (SAC)
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- Twin Delayed DDPG (TD3)
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**Maintainer**: David Hoeller, Nikita Rudin <br/>
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**Affiliation**: Robotic Systems Lab, ETH Zurich & NVIDIA <br/>
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**Contact**: Nikita Rudin (rudinn@ethz.ch), Lukas Schneider (lukas@luschneider.com)
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## Citation
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If you use our code in your research, please cite us:
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```
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@misc{schneider2023learning,
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archivePrefix={arXiv},
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author={Lukas Schneider and Jonas Frey and Takahiro Miki and Marco Hutter},
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eprint={2309.14246},
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primaryClass={cs.RO}
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title={Learning Risk-Aware Quadrupedal Locomotion using Distributional Reinforcement Learning},
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year={2023},
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}
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```
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## Installation
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To install the package, run the following command in the root directory of the repository:
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```bash
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$ pip3 install -e .
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```
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Examples can be run from the `examples/` directory.
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The example directory also include hyperparameters tuned for some gym environments.
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These are automatically loaded when running the example.
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Videos of the trained policies are periodically saved to the `videos/` directory.
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```bash
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$ python3 examples/example.py
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```
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To run gym mujoco environments, you need a working installation of the mujoco simulator and [mujoco_py](https://github.com/openai/mujoco-py).
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## Tests
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The repository contains a set of tests to ensure that the algorithms are working as expected.
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To run the tests, simply execute:
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```bash
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$ cd tests/ && python -m unittest
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```
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## Documentation
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To generate documentation, run the following command in the root directory of the repository:
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```bash
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$ pip3 install sphinx sphinx-rtd-theme
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$ sphinx-apidoc -o docs/source . ./examples
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$ cd docs/ && make html
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```
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## Contribution Guidelines
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We use [`black`](https://github.com/psf/black) formatter for formatting the python code.
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You should [configure `black` with VSCode](https://dev.to/adamlombard/how-to-use-the-black-python-code-formatter-in-vscode-3lo0) or you can manually format files with:
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```bash
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$ pip install black
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$ black --line-length 120 .
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```
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