Guide · Open source
How to use Spinning Up: learn deep RL from OpenAI's resource
Updated · written with AI from the project's own documentation and checked against our catalogue data
Spinning Up is an educational resource from OpenAI for learning deep reinforcement learning, the approach where agents learn tasks by trial and error combined with deep learning. This guide explains what the repository contains, how to get a local copy, and how to use its reading material and code together. By the end you will know where to start, what to read first and where the project's limits are.
Spinning Upopenai/spinningup · ★ 12k · MIT Install options & downloads Quick start
- 1Check your Python setup. The project lists Python as its prerequisite, so confirm you have a working Python installation before you begin.
- 2Clone the repository. Run the git clone command from the README to download openai/spinningup to your machine.
- 3Open the official site. Visit spinningup.openai.com for the introduction, essay, key papers list and exercises.
- 4Read the RL introduction. Work through the terminology and basic theory before moving to algorithms.
- 5Study one algorithm in the code. Pick a single standalone implementation and read it alongside its documentation.
- 6Do the warm-up exercises. Use the exercises to check that you understand the concepts before going further.
What Spinning Up contains
Spinning Up bundles several resources rather than a single software package. The README lists a short introduction to RL terminology, kinds of algorithms and basic theory, an essay on how to grow into an RL research role, a curated list of important papers organized by topic, a documented code repository of short standalone algorithm implementations, and a few exercises as warm-ups.
- Introduction to RL terminology, algorithm types and basic theory
- Essay on growing into an RL research role
- Curated list of key papers organized by topic
- Code repo of short, standalone implementations of key algorithms
- Warm-up exercises
Install Spinning Up by cloning the repository
The project uses a clone-and-build approach and the listed prerequisite is Python. Clone the repository with the command from the README, then follow the setup instructions in the repository itself.
- Prerequisite: Python
- Clone the repository from GitHub
git clone https://github.com/openai/spinningup.gitRead the RL introduction and key papers first
If you are new to the field, start with the online introduction, which covers RL terminology, kinds of algorithms and basic theory. Then use the curated list of key papers to move from concepts to primary sources, grouped by topic so you can follow one area at a time.
- Read the introduction to RL terms and basic theory on the project site
- Use the key papers list to find foundational work by topic
- Use the research-role essay if your goal is a research career
Learn algorithms by reading the standalone code
The code repo holds short, standalone implementations of key algorithms. Because each one is self-contained, you can read a single file to see how an algorithm works, then compare it against the explanation on the site. The approach suits people who learn best by reading code next to prose.
- Pick one algorithm and read its implementation from start to finish
- Check the matching explanation in the documentation
- Work through the warm-up exercises to test your understanding
Know the limits before you build on it
The README marks the project as in maintenance mode, which it describes as expecting bug fixes and minor updates. It is an educational resource, so it is not a production RL library or a single framework for large-scale training. The material also assumes some deep learning background, so complete beginners to machine learning may need to study that first.
If you need a maintained library for real projects, the comparable projects listed for this directory are Stable Baselines3, CleanRL, RLlib and Gymnasium.
- Maintenance mode: expect bug fixes and minor updates only
- Not a production-ready RL library
- Assumes some deep learning background
Cite Spinning Up in research
If you use Spinning Up in published research, the README asks you to cite it. The BibTeX entry is provided in the repository and is reproduced in the source for this guide.
@article{SpinningUp2018,
author = {Achiam, Joshua},
title = {{Spinning Up in Deep Reinforcement Learning}},
year = {2018}
}What people use it for
- A student building a structured foundation in deep reinforcement learning
- A developer reading readable, standalone implementations of key algorithms
- A researcher assembling a reading list of important RL papers
- A learner who wants to study code and explanations side by side
Frequently asked questions
Is Spinning Up free to use?
The repository is released under the MIT licence, as listed for this project. Check the LICENSE file in the repository for the full terms.
What do I need to install Spinning Up?
The listed prerequisite is Python. Install method is clone-and-build, so you clone the repository from GitHub and build it from there.
Is Spinning Up still maintained?
The README says the project is in maintenance mode and expects bug fixes and minor updates. It is not positioned for major new development.
Can I use Spinning Up for production RL?
The project is educational and maintenance-mode, so it is not intended as a production-ready RL library. Use a maintained library for production work.
What are good alternatives to Spinning Up?
Comparable projects listed for this directory are Stable Baselines3, CleanRL, RLlib and Gymnasium. Choose based on whether you need learning material, readable code, or a framework for training.
Do I need machine learning experience first?
The material assumes some deep learning background, so complete beginners to machine learning may find it hard to follow without preparation.
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Our verdict
Spinning Up suits students and developers who want a structured, readable introduction to deep RL and a curated paper list. It is a poor choice if you need a maintained framework for production or large-scale training, or if you are new to machine learning.
Install options & downloads