sb3-contrib
Contrib package of Stable Baselines3, experimental code.
Decision gist · record as of 2026-08-14
Yes, if you need experimental RL algorithms or specialized policy variants beyond what stable_baselines3 core offers. The low install friction, active maintenance, and MIT license make it a reasonable addition to an RL workflow. The experimental nature of the algorithms means this is best suited for research or prototyping rather than production systems requiring stability guarantees.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires stable_baselines3 as a runtime dependency
- Low friction installation as a pure Python wheel.
- Actively maintained with a recent release and steady commit activity.
License · maintenance · safety
MIT (permissive) — MIT license permits unrestricted use, modification, and distribution in both open and closed projects with minimal obligations.
last release 2026-06-15 (60 days) · last repo commit 2026-07-24 · 729 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 439,072 downloads/mo, #6,659 on PyPI
Alternatives
Verify before relying
pip install sb3-contrib
from sb3_contrib import MaskablePPO
model = MaskablePPO('MlpPolicy', env)- Whether the master version of stable_baselines3 requirement creates practical installation friction beyond standard dependency resolution
- Performance or stability characteristics of experimental algorithms relative to stable_baselines3 core implementations
What it is and what it does
SB3-Contrib is a companion package to stable_baselines3 that houses experimental and newer reinforcement learning algorithms and tools. It maintains the documentation and code style of the main library while hosting implementations that are either too niche, too experimental, or too difficult to integrate into the core package. The package includes algorithms like Augmented Random Search, Quantile Regression DQN, masked PPO variants, recurrent PPO, and Truncated Quantile Critics, along with environment wrappers.
The package is designed for researchers and practitioners who want access to cutting-edge RL implementations without waiting for them to mature enough for the main library. It trades stability guarantees for breadth: algorithms here follow consistent documentation and style but may not have the same level of testing or long-term support as core stable_baselines3 methods.
Use it for
- Implement action masking in PPO for environments with invalid actions using MaskablePPO
- Train agents with recurrent policies using RecurrentPPO for partially observable environments
- Experiment with newer algorithms like Truncated Quantile Critics or Trust Region Policy Optimization
- Prototype RL solutions with less mature but potentially better-performing algorithms before production deployment
- Access environment wrappers like Time Feature Wrapper to augment observation spaces
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you need experimental RL algorithms or specialized policy variants beyond what stable_baselines3 core offers.
The low install friction, active maintenance, and MIT license make it a reasonable addition to an RL workflow. The experimental nature of the algorithms means this is best suited for research or prototyping rather than production systems requiring stability guarantees.
Install
sb3-contrib on PyPI
Before you install
Low friction installation as a pure Python wheel. Actively maintained with a recent release and steady commit activity. Requires the master version of stable_baselines3 as a runtime dependency.
Requires stable_baselines3 as a runtime dependency
License in practice
MIT license permits unrestricted use, modification, and distribution in both open and closed projects with minimal obligations.
Quickstart
pip install sb3-contrib
from sb3_contrib import MaskablePPO
model = MaskablePPO('MlpPolicy', env)Verify before relying
- Whether the master version of stable_baselines3 requirement creates practical installation friction beyond standard dependency resolution
- Performance or stability characteristics of experimental algorithms relative to stable_baselines3 core implementations
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 1 packagestable_baselines3 |
| Maintenance | Actively maintained 60 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 439,072 / month, #6,659 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Programming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13 |
Evidence: sb3_contrib-2.9.0-py3-none-any.whl
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See also mabwiser · stable-baselines3 · dopamine-rl · tianshou · rsl-rl-lib · skrl · mjlab · trl · torchrl · nemo-gym