RSL RL

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.

⚡ The algorithms branch supports additional algorithms (SAC, DDPG, DSAC, and more)!

Only PPO is implemented for now. More algorithms will be added later. Contributions are welcome.

Maintainer: David Hoeller and Nikita Rudin
Affiliation: Robotic Systems Lab, ETH Zurich & NVIDIA
Contact: rudinn@ethz.ch

Setup

Following are the instructions to setup the repository for your workspace:

git clone https://github.com/leggedrobotics/rsl_rl
cd rsl_rl
pip install -e .

The framework 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 the PPO, please check: dummy_config.yaml file.

Contribution Guidelines

For documentation, we adopt the Google Style Guide for docstrings. We use Sphinx for generating the documentation. Please make sure that your code is well-documented and follows the guidelines.

We use the following tools for maintaining code quality:

• pre-commit: Runs a list of formatters and linters over the codebase.
• black: The uncompromising code formatter.
• flake8: A wrapper around PyFlakes, pycodestyle, and McCabe complexity checker.

Please check here for instructions to set these up. To run over the entire repository, please execute the following command in the terminal:

# for installation (only once)
pre-commit install
# for running
pre-commit run --all-files

Useful Links

Environment repositories using the framework:

• Legged-Gym (built on top of NVIDIA Isaac Gym): https://leggedrobotics.github.io/legged_gym/
• Orbit (built on top of NVIDIA Isaac Sim): https://isaac-orbit.github.io/