Gymnasium vs Stable Baselines3
Which should you use? A side-by-side look at what each one does, who it suits, what it costs, how hard it is to set up, and where each falls short.
Open source · MIT
A standard Python interface for reinforcement learning environments, plus reference environments to test algor
Open source · MIT
A Python library of reliable reinforcement learning algorithms built on PyTorch.
The short answer
Choose Gymnasium if you need…
- Researchers comparing reinforcement learning algorithms on the same environments
- Students learning reinforcement learning with simple, well-known test problems
- Developers who want an environment API that other tools (such as CleanRL) already support
- Anyone migrating older Gym code who wants continued maintenance
Choose Stable Baselines3 if you need…
- Researchers who want a reliable baseline to compare a new method against
- Engineers prototyping an RL agent for a task with a Gymnasium-style environment
- Students with some RL background who want readable, tested implementations
- Users who want to train PPO on a simple environment such as CartPole with a few lines of code
Side-by-side
| Gymnasium | Stable Baselines3 | |
|---|---|---|
| Type | Library | Library |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 12.8k | 14k |
| Setup difficulty | Intermediate | Intermediate |
| How you get it | Install with a package manager | Install with a package manager |
| Main language | Python | Python |
| Last code update | 7 days ago | 11 days ago |
What each one is
Gymnasium
Gymnasium is an open-source Python library that gives reinforcement learning algorithms and simulated environments a shared, standard way to talk to each other. It also includes a set of ready-made environments, such as classic control problems, physics-based games, and Atari games. It is a maintained fork of OpenAI's Gym library.
Stable Baselines3
Stable Baselines3 (SB3) is a Python library that provides tested implementations of common reinforcement learning algorithms, which are methods for training software agents to make decisions by trial and reward. It is written in PyTorch and offers a consistent interface so researchers and engineers can train, compare, and build on these algorithms without writing them from scratch. The README notes that it assumes some prior knowledge of reinforcement learning.
Best for
Gymnasium
- Researchers comparing reinforcement learning algorithms on the same environments
- Students learning reinforcement learning with simple, well-known test problems
- Developers who want an environment API that other tools (such as CleanRL) already support
- Anyone migrating older Gym code who wants continued maintenance
Stable Baselines3
- Researchers who want a reliable baseline to compare a new method against
- Engineers prototyping an RL agent for a task with a Gymnasium-style environment
- Students with some RL background who want readable, tested implementations
- Users who want to train PPO on a simple environment such as CartPole with a few lines of code
Strengths
Gymnasium
- Provides a standard API, so the same agent code can run on many environments
- Maintained by a team that took over from OpenAI's Gym, with documentation and a Discord community
- Environment versions are pinned with suffixes like -v1, which helps reproducibility
Stable Baselines3
- Provides a common, consistent interface across algorithms, as described in the feature table
- Includes documentation, custom environment and policy support, TensorBoard support, and type hints per the README
- Has an active CI pipeline and is documented as a successor to Stable Baselines
Not ideal for
Gymnasium
- People who want a finished product rather than code to build on, since it is a library for experiments and research
- Users who need multi-agent environments, because the README says Gymnasium covers single-agent setups and points to PettingZoo for multi-agent work
- Those who do not know Python, since the API and examples are Python-only
Stable Baselines3
- Complete beginners with no reinforcement learning background, since the README says the library assumes some RL knowledge
- Projects that need the newest experimental algorithms, which the README says are placed in the separate SB3-Contrib repository
- Users who need the fastest possible training, since the README mentions the separate SBX (JAX) variant as faster but with fewer features
Trade-offs
Gymnasium
- Installing the base package does not include dependencies for every environment family; some extras can be difficult to install on certain systems
- Third-party environments may target older API versions and may need the apply_env_compatibility option
- The README is short on detail for advanced use, so deeper usage depends on the separate documentation site
Stable Baselines3
- The README says development is now focused on bug fixes and maintenance, with no major changes planned
- Requires some reinforcement learning knowledge to use effectively
- Newer or experimental features live in separate repositories, so a full toolset means installing several projects
Getting started
Gymnasium
Install the base library
pip install gymnasiumInstall dependencies for one environment family (example: Atari)
pip install "gymnasium[atari]"Stable Baselines3
Install with optional extras (Tensorboard, OpenCV, ale-py, pandas, matplotlib)
pip install 'stable-baselines3[extra]'Install core package without extras
pip install stable-baselines3Frequently asked questions
What is the difference between Gymnasium and Stable Baselines3?
Gymnasium: A standard Python interface for reinforcement learning environments, plus reference environments to test algor. Stable Baselines3: A Python library of reliable reinforcement learning algorithms built on PyTorch. Gymnasium suits researchers comparing reinforcement learning algorithms on the same environments, while Stable Baselines3 suits researchers who want a reliable baseline to compare a new method against.
Are Gymnasium and Stable Baselines3 free?
Gymnasium: Free and open source. Stable Baselines3: Free and open source.
Is Gymnasium or Stable Baselines3 more popular?
Stable Baselines3 has more GitHub stars (14k) than Gymnasium (12.8k). Popularity isn't the same as fit — check which one matches your use case above.
What are other alternatives to Gymnasium and Stable Baselines3?
Other options people compare them with include Tianshou, Weights & Biases, ONNX Runtime, Petals.
Keep comparing
Built from our catalogue entries for both products: open-source details come from each project's README (AI-assisted, refreshed weekly) and GitHub data; AI-tool details are editorial. Spotted something wrong? Let us know from either product's page.