rsl_rl
This project provides a quick and efficient implementation of reinforcement learning algorithms optimized for GPU. Initially based on the NVIDIA Isaac GYM `rl-pytorch`, it currently supports PPO with plans to include more algorithms like SAC and DDPG. Managed by researchers from ETH Zurich and NVIDIA's Robotic Systems Lab, the framework facilitates logging via Tensorboard, Weights & Biases, and Neptune. It is intended for researchers expanding reinforcement learning capabilities and promotes community contributions while following the Google Style Guide for documentation. To set up, clone the repository and adhere to the instructions for seamless integration into various environments.
