Spinning Up
Free educational guide and short reference code for learning deep reinforcement learning.
- Stars
- 12k
- Forks
- 2.5k
- Licence
- MIT
- Language
- Python
- Last update
- 2.3 years ago
- Type
- Learning resource
No commits in over a year — check for forks or alternatives before adopting.
What it is
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.
- Work through the introduction to RL terminology and basic theory
- Read the curated list of key papers organized by topic
- Study the short standalone algorithm implementations in the code repo
- Try the warm-up exercises to practice concepts
Great for
- 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
Not ideal for
- 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
How to get it
- 1
Clone and build from source
You'll need: Python
Clone the repository
git clone https://github.com/openai/spinningup.git - 2
Run it
- Open the project homepage at spinningup.openai.com and read the RL introduction
- Read the key papers list to pick a topic
- Clone the repo and browse the algorithm implementations
- Attempt the warm-up exercises
- 3
Get the most out of it
- Read the introduction and key papers list before opening the code
- Use the repo's documented implementations as a reference while reading the papers
- Work through the exercises to check your understanding
- Read the research-career essay if you are considering an RL research path
Downloads
Strengths
- 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
Trade-offs
- 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
How it compares
| Project | Stars | Type | Level | Updated | Best for |
|---|---|---|---|---|---|
| spinningup (this) | 12k | Learning resource | intermediate | 2.3 years ago | Students who want a structured entry point into deep RL |
| stable-baselines3 | 14k | Library | intermediate | 11 days ago | Researchers who want a reliable baseline to compare a new method against |
| Gymnasium | 12.8k | Library | intermediate | 7 days ago | Researchers comparing reinforcement learning algorithms on the same environments |
Stats synced from GitHub. The assessment above was drafted with AI from the project's README on 2026-10-29 and may miss recent changes — check the README before relying on a command. MIT licence: free to use, modify and distribute, including commercially, as long as you keep the copyright notice.