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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
|