{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/6"}],"enrichment":{"capability":"RSL-RL is a GPU-accelerated reinforcement learning library for robotics research, providing implementations of algorithms like PPO with multi-GPU training support and a minimal, extensible codebase.","skillfed_tags":["robotics","reinforcement-learning","gpu-accelerated"],"use_cases":["Training locomotion policies for legged robots using PPO in Isaac Sim or Isaac Gym environments","Prototyping new RL algorithms without modifying a large framework codebase","Scaling training across multiple GPUs for faster policy convergence in robotics tasks","Integrating RL training into existing robot learning pipelines via Isaac Lab or Legged Gym","Exporting trained policies to ONNX format for deployment on robot hardware"],"what_it_does":"RSL-RL is a lightweight reinforcement learning library designed specifically for robotics research, built on PyTorch with native GPU acceleration and multi-GPU training support. It provides implementations of common RL algorithms\u2014notably PPO and Student-Teacher Distillation\u2014in a minimal, readable codebase that prioritizes rapid prototyping over the complexity of larger frameworks. The library is used as the training backend for Isaac Lab, Legged Gym, mjlab, and MuJoCo Playground.\n\nThe package depends on torch, torchvision, tensordict, numpy, tensorboard, GitPython, onnx, and onnxscript. It requires Python 3.9 or later and is distributed as a pure Python wheel, making installation straightforward. The library is actively maintained (latest release 30 days old, 2885 GitHub stars) and has no known security vulnerabilities.","worth_installing":"Yes, if you are doing robotics research with PyTorch and need a lightweight, actively maintained RL training library. The low install friction, permissive license, active maintenance, and zero known vulnerabilities make it a solid choice. Install it if you are working with Isaac Lab, Legged Gym, or similar robot learning environments; otherwise, verify that its algorithm implementations and API match your specific needs."},"id":"rsl-rl-lib","links":{"html":"https://skillfed.io/packages/rsl-rl-lib","md":"https://skillfed.io/packages/rsl-rl-lib.md","pypi":"https://pypi.org/project/rsl-rl-lib/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-07-15","license_spdx":"BSD-3-Clause","license_treatment":"permissive","name":"rsl-rl-lib","python_support":"supports_current","summary":"Fast and simple RL algorithms implemented in PyTorch"},"popularity":{"monthly_downloads":369645,"position":7186,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"5.4.2"}