# leggedrobotics/rsl_rl

A fast and simple implementation of learning algorithms for robotics.

Repository: https://github.com/leggedrobotics/rsl_rl
Canonical: https://ross.abutalabs.com/products/rsl_rl
Homepage: https://leggedrobotics.github.io/rsl_rl/
Language: Python
License: NOASSERTION
License Family: other
Last push: 2026-08-26T14:46:56+00:00

## Health v2 (maintenance only)
Score: 97/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 99, release rhythm 93, longevity 100
- inputs: {"age_days": 1780, "days_push": 7, "days_rel": 49, "gap_med": 16.5, "n_releases_24m": 29}
- flags: no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2916, forks 666 (observed 2026-08-28T04:07:29.991620+00:00)

## What it is
RSL-RL is a GPU-accelerated, lightweight reinforcement learning library for robotics research, featuring algorithms like PPO and Student-Teacher Distillation. It is designed for rapid prototyping with a minimal, readable codebase and supports multi-GPU training, and is used by Isaac Lab, Legged Gym, mjLab, and MuJoCo Playground.

## Use cases
- train RL policies for legged robots
- run PPO training on GPU for robotics simulations
- perform student-teacher distillation for robot learning
- prototype new robot learning algorithms quickly
- train policies in Isaac Lab or Legged Gym
- scale robot learning training across multiple GPUs

## When to choose
- doing robotics RL research with Isaac Lab, Legged Gym, mjLab, or MuJoCo Playground
- you want a minimal, hackable RL codebase instead of a large framework
- you need multi-GPU, high-throughput PPO training for simulated robots

## When to avoid
- you need general-purpose RL beyond robotics (e.g., Atari, game benchmarks)
- you need a broad algorithm zoo rather than PPO-centric methods
- your project does not use GPU-accelerated simulation environments

## Facets
- artifact type: library
- maturity: active
- function: reinforcement-learning, machine-learning, gpu-computing, simulation
- domain: robotics, reinforcement-learning, machine-learning, simulation
- platform: python, cross-platform
- tags: ppo, student-teacher-distillation, legged-robots, isaac-lab, mujoco, robot-learning, multi-gpu, gpu, linux

## Member repositories
- leggedrobotics/rsl_rl (main) score 97

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:29.991620+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T18:48:12.529807+00:00, confidence not recorded.
  - readme: https://github.com/leggedrobotics/rsl_rl (fetched 2026-08-28T04:07:29.991620+00:00, sha 059b376d265b)
  - homepage: https://leggedrobotics.github.io/rsl_rl/ (fetched 2026-08-29T09:49:19.856589+00:00, sha 15177998e726)
- Data as of 2026-08-30T08:39:29.467469+00:00.