Reinforcement Learning Library Comparison — Isaac Lab Documentation
Last updated: 12/12/2025
Title: Reinforcement Learning Library Comparison — Isaac Lab Documentation
URL Source: https://isaac-sim.github.io/IsaacLab/main/source/overview/reinforcement-learning/rl_frameworks.html
Published Time: Thu, 11 Sep 2025 17:00:56 GMT
Markdown Content: Reinforcement Learning Library Comparison#
In this section, we provide an overview of the supported reinforcement learning libraries in Isaac Lab, along with performance benchmarks across the libraries.
The supported libraries are:
Feature Comparison#
| Feature | RL-Games | RSL RL | SKRL | Stable Baselines3 |
|---|---|---|---|---|
| Algorithms Included | PPO, SAC, A2C | PPO, Distillation | Extensive List | Extensive List |
| Vectorized Training | Yes | Yes | Yes | No |
| Distributed Training | Yes | Yes | Yes | No |
| ML Frameworks Supported | PyTorch | PyTorch | PyTorch, JAX | PyTorch |
| Multi-Agent Support | PPO | PPO | PPO + Multi-Agent algorithms | External projects support |
| Documentation | Low | Low | Comprehensive | Extensive |
| Community Support | Small Community | Small Community | Small Community | Large Community |
| Available Examples in Isaac Lab | Large | Large | Large | Small |
Training Performance#
We performed training with each RL library on the same Isaac-Humanoid-v0 environment with --headless on a single RTX PRO 6000 GPU using 4096 environments and logged the total training time for 65.5M steps for each RL library.
| RL Library | Time in seconds |
|---|---|
| RL-Games | 207 |
| SKRL | 208 |
| RSL RL | 199 |
| Stable-Baselines3 | 322 |
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