Spinning Up 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
Free educational guide and short reference code for learning deep reinforcement learning.
Open source · MIT
A Python library of reliable reinforcement learning algorithms built on PyTorch.
The short answer
Choose Spinning Up if you need…
- Students who want a structured entry point into deep RL
- Developers who want readable, standalone implementations of key algorithms
- Researchers looking for a curated reading list of important papers
- People who prefer learning by reading code alongside explanations
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
| Spinning Up | Stable Baselines3 | |
|---|---|---|
| Type | Learning resource | Library |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 12k | 14k |
| Setup difficulty | Intermediate | Intermediate |
| How you get it | Clone and build from source | Install with a package manager |
| Main language | Python | Python |
| Last code update | 2 years ago | 11 days ago |
What each one is
Spinning Up
Spinning Up is an educational resource from OpenAI for learning deep reinforcement learning, where agents learn tasks by trial and error combined with deep learning. It bundles an introduction to RL terms and theory, an essay on growing into an RL research role, a curated list of key papers, a documented code repository of short standalone algorithm implementations, and a few warm-up exercises. The README describes the project as in maintenance mode, expecting bug fixes and minor updates.
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
Spinning Up
- Students who want a structured entry point into deep RL
- Developers who want readable, standalone implementations of key algorithms
- Researchers looking for a curated reading list of important papers
- People who prefer learning by reading code alongside explanations
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
Spinning Up
- Free and MIT licensed, produced by OpenAI
- Clear educational structure combining theory, papers, code, and exercises
- Implementations are described as short and standalone, which makes them easy to read
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
Spinning Up
- Readers who need production-ready RL libraries, since the project is maintenance-mode and focused on education
- Complete beginners to machine learning, since the material assumes some deep learning background
- People who want a single maintained framework for large-scale training
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
Spinning Up
- Status is maintenance, so new features and updates are unlikely beyond bug fixes and minor changes
- The README excerpt provided does not describe installation or setup steps
- The last push date is August 2024, so the code may lag current library versions
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
Spinning Up
Clone the repository
git clone https://github.com/openai/spinningup.gitStable 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 Spinning Up and Stable Baselines3?
Spinning Up: Free educational guide and short reference code for learning deep reinforcement learning. Stable Baselines3: A Python library of reliable reinforcement learning algorithms built on PyTorch. Spinning Up suits students who want a structured entry point into deep rl, while Stable Baselines3 suits researchers who want a reliable baseline to compare a new method against.
Are Spinning Up and Stable Baselines3 free?
Spinning Up: Free and open source. Stable Baselines3: Free and open source.
Is Spinning Up or Stable Baselines3 more popular?
Stable Baselines3 has more GitHub stars (14k) than Spinning Up (12k). Popularity isn't the same as fit — check which one matches your use case above.
What are other alternatives to Spinning Up and Stable Baselines3?
Other options people compare them with include Gymnasium, 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.