| .. _rl-frameworks: | |
| 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: | |
| - `SKRL <https://skrl.readthedocs.io>`__ | |
| - `RSL-RL <https://github.com/leggedrobotics/rsl_rl>`__ | |
| - `RL-Games <https://github.com/Denys88/rl_games>`__ | |
| - `Stable-Baselines3 <https://stable-baselines3.readthedocs.io/en/master/index.html>`__ | |
| Feature Comparison | |
| ------------------ | |
| .. list-table:: | |
| :widths: 20 20 20 20 20 | |
| :header-rows: 1 | |
| * - Feature | |
| - RL-Games | |
| - RSL RL | |
| - SKRL | |
| - Stable Baselines3 | |
| * - Algorithms Included | |
| - PPO, SAC, A2C | |
| - PPO, Distillation | |
| - `Extensive List <https://skrl.readthedocs.io/en/latest/#agents>`__ | |
| - `Extensive List <https://github.com/DLR-RM/stable-baselines3?tab=readme-ov-file#implemented-algorithms>`__ | |
| * - 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 NVIDIA GeForce RTX 4090 and logged the total training time | |
| for 65.5M steps (4096 environments x 32 rollout steps x 500 iterations). | |
| +--------------------+-----------------+ | |
| | RL Library | Time in seconds | | |
| +====================+=================+ | |
| | RL-Games | 201 | | |
| +--------------------+-----------------+ | |
| | SKRL | 201 | | |
| +--------------------+-----------------+ | |
| | RSL RL | 198 | | |
| +--------------------+-----------------+ | |
| | Stable-Baselines3 | 287 | | |
| +--------------------+-----------------+ | |
| Training commands (check for the *'Training time: XXX seconds'* line in the terminal output): | |
| .. code:: bash | |
| python scripts/reinforcement_learning/rl_games/train.py --task Isaac-Humanoid-v0 --max_iterations 500 --headless | |
| python scripts/reinforcement_learning/skrl/train.py --task Isaac-Humanoid-v0 --max_iterations 500 --headless | |
| python scripts/reinforcement_learning/rsl_rl/train.py --task Isaac-Humanoid-v0 --max_iterations 500 --headless | |
| python scripts/reinforcement_learning/sb3/train.py --task Isaac-Humanoid-v0 --max_iterations 500 --headless | |