reinforcement-learning 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
Code, exercises and solutions for learning reinforcement learning alongside Sutton's book and David Silver's c
Open source · MIT
Free educational guide and short reference code for learning deep reinforcement learning.
The short answer
Choose reinforcement-learning if you need…
- Students working through Sutton and Barto's reinforcement learning textbook
- Self-learners who want runnable examples of classic RL algorithms
- Developers who want to see tabular methods like Q-learning and SARSA implemented
- People following David Silver's RL course who want matching exercises
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 | Spinning Up | |
|---|---|---|
| Type | Learning resource | Learning resource |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 22.2k | 12k |
| Setup difficulty | Intermediate | Intermediate |
| How you get it | Clone and build from source | Clone and build from source |
| Main language | Jupyter Notebook | Python |
| Last code update | 3 years ago | 2 years ago |
What each one is
reinforcement-learning
This repository is a set of study materials for reinforcement learning, a branch of machine learning where a program learns by trial and error to make good decisions. It pairs Jupyter notebooks containing exercises and worked solutions with the textbook by Sutton and Barto and David Silver's lecture course. The code is written in Python 3 and uses OpenAI Gym environments, with some advanced parts using TensorFlow.
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
- Students working through Sutton and Barto's reinforcement learning textbook
- Self-learners who want runnable examples of classic RL algorithms
- Developers who want to see tabular methods like Q-learning and SARSA implemented
- People following David Silver's RL course who want matching exercises
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
- Maps folders directly to chapters of the Sutton and Barto book and to Silver's course
- Includes exercises with solutions, not just finished code
- Uses standard RL environments from OpenAI Gym so results are easy to reproduce
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
- Readers wanting production-ready RL libraries, since this is a learning aid and the README frames it that way
- People needing the latest deep RL methods, because several entries are marked WIP in the README
- Anyone who cannot read Python or work in Jupyter notebooks
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
- Several algorithms are listed as work in progress, so coverage is incomplete
- The README does not give version pins or detailed setup steps, so environment setup may take effort
- The README links to older course materials and a Gym website, which may be out of date
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
Clone the repository
git clone https://github.com/dennybritz/reinforcement-learningSpinning Up
Clone the repository
git clone https://github.com/openai/spinningup.gitFrequently asked questions
What is the difference between reinforcement-learning and Spinning Up?
reinforcement-learning: Code, exercises and solutions for learning reinforcement learning alongside Sutton's book and David Silver's c. Spinning Up: Free educational guide and short reference code for learning deep reinforcement learning. reinforcement-learning suits students working through sutton and barto's reinforcement learning textbook, while Spinning Up suits students who want a structured entry point into deep rl.
Are reinforcement-learning and Spinning Up free?
reinforcement-learning: Free and open source. Spinning Up: Free and open source.
Is reinforcement-learning or Spinning Up more popular?
reinforcement-learning has more GitHub stars (22.2k) 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 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.