spinningup
An educational resource to help anyone learn deep reinforcement learning.
An OpenAI learning package that teaches deep reinforcement learning through written guides, readable Python implementations of key algorithms, and practice exercises, aimed at people new to the field.
Spinning Up in Deep RL is an educational resource from OpenAI for people who want to learn about deep reinforcement learning. Reinforcement learning is a branch of machine learning where a computer program learns to make decisions by trying things out and receiving feedback on whether those actions were good or bad. The "deep" part refers to using neural networks as the core of the learning system.
The package is designed to lower the barrier to entry for this field, which has historically required a lot of background knowledge to approach. It includes a written introduction to the core concepts and vocabulary, a guide on how to build a career in reinforcement learning research, and a curated reading list of important published papers organized by topic.
Beyond the written materials, the repository includes clean, well-documented Python implementations of several key reinforcement learning algorithms. These are written to be short and readable rather than maximally optimized, so someone studying them can follow the logic without getting lost in engineering complexity. A set of exercises is also included as practice.
The project is in maintenance mode, meaning the OpenAI team is no longer adding major new features but does address bug fixes. All of the documentation and full guides are hosted at the project's website.
Where it fits
- Learn the core vocabulary and ideas of deep reinforcement learning from scratch using the included guides
- Study short, readable Python implementations of algorithms like PPO and SAC to understand how they work
- Follow a curated reading list to build up knowledge of the most important published papers in the field
- Complete practice exercises to test and reinforce understanding of reinforcement learning concepts