reinforcement-learning-an-introduction vs Spinning Up
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
Python code that reproduces the figures and examples from the Sutton & Barto reinforcement learning textbook.
Open source · MIT
Free educational guide and short reference code for learning deep reinforcement learning.
The short answer
Choose reinforcement-learning-an-introduction if you need…
- Students working through the Sutton & Barto book who want runnable versions of its examples
- Readers who learn better by seeing plotted results of algorithms like Sarsa, Q-learning, or Dyna-Q
- Developers who want simple Python reference implementations of classic RL methods
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
Side-by-side
| reinforcement-learning-an-introduction | Spinning Up | |
|---|---|---|
| Type | Learning resource | Learning resource |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 14.8k | 12k |
| Setup difficulty | Intermediate | Intermediate |
| How you get it | Clone and build from source | Clone and build from source |
| Main language | Python | Python |
| Last code update | 2 years ago | 2 years ago |
What each one is
reinforcement-learning-an-introduction
This repository contains Python code that replicates the experiments and figures from the book Reinforcement Learning: An Introduction (2nd Edition) by Sutton and Barto. It is organized by chapter, covering topics such as bandit problems, dynamic programming, Monte Carlo methods, temporal-difference learning, and function approximation. People use it to see how the book's algorithms behave when run on the textbook's examples.
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.
Best for
reinforcement-learning-an-introduction
- Students working through the Sutton & Barto book who want runnable versions of its examples
- Readers who learn better by seeing plotted results of algorithms like Sarsa, Q-learning, or Dyna-Q
- Developers who want simple Python reference implementations of classic RL methods
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
Strengths
reinforcement-learning-an-introduction
- Organized chapter by chapter so it maps directly onto the book
- Covers a broad range of chapters, from tabular methods through function approximation
- Released under the MIT license, so it is free to read and reuse
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
Not ideal for
reinforcement-learning-an-introduction
- People looking for a general-purpose RL library for production use, since the README presents this as a book replication
- Anyone expecting exercise answers, because the author states they do not have exercise answers for the book
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
Trade-offs
reinforcement-learning-an-introduction
- The README is short and the contents list is truncated, so coverage of each chapter is not fully documented here
- The author does not provide exercise solutions, so readers must work those out themselves
- Last push was 2024-08-09 and there is no release, so maintenance status is unclear
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
Getting started
reinforcement-learning-an-introduction
Clone the repository
git clone https://github.com/ShangtongZhang/reinforcement-learning-an-introduction.gitSpinning Up
Clone the repository
git clone https://github.com/openai/spinningup.gitFrequently asked questions
What is the difference between reinforcement-learning-an-introduction and Spinning Up?
reinforcement-learning-an-introduction: Python code that reproduces the figures and examples from the Sutton & Barto reinforcement learning textbook. Spinning Up: Free educational guide and short reference code for learning deep reinforcement learning. reinforcement-learning-an-introduction suits students working through the sutton & barto book who want runnable versions of its examples, while Spinning Up suits students who want a structured entry point into deep rl.
Are reinforcement-learning-an-introduction and Spinning Up free?
reinforcement-learning-an-introduction: Free and open source. Spinning Up: Free and open source.
Is reinforcement-learning-an-introduction or Spinning Up more popular?
reinforcement-learning-an-introduction has more GitHub stars (14.8k) than Spinning Up (12k). Popularity isn't the same as fit — check which one matches your use case above.
What are other alternatives to reinforcement-learning-an-introduction and Spinning Up?
Other options people compare them with include Stable Baselines3, Gymnasium.
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.