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Snapshot 0 · 2026-11-04T12:47:25Z · Python · MIT
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4bc0a351d54246c46596c22945f8ce6ae200c903977cf9d2e0011d8ba9303e47GitHub description at capture
An educational resource to help anyone learn deep reinforcement learning.
README at capture
**Status:** Maintenance (expect bug fixes and minor updates)
Welcome to Spinning Up in Deep RL!
==================================
This is an educational resource produced by OpenAI that makes it easier to learn about deep reinforcement learning (deep RL).
For the unfamiliar: [reinforcement learning](https://en.wikipedia.org/wiki/Reinforcement_learning) (RL) is a machine learning approach for teaching agents how to solve tasks by trial and error. Deep RL refers to the combination of RL with [deep learning](http://ufldl.stanford.edu/tutorial/).
This module contains a variety of helpful resources, including:
- a short [introduction](https://spinningup.openai.com/en/latest/spinningup/rl_intro.html) to RL terminology, kinds of algorithms, and basic theory,
- an [essay](https://spinningup.openai.com/en/latest/spinningup/spinningup.html) about how to grow into an RL research role,
- a [curated list](https://spinningup.openai.com/en/latest/spinningup/keypapers.html) of important papers organized by topic,
- a well-documented [code repo](https://github.com/openai/spinningup) of short, standalone implementations of key algorithms,
- and a few [exercises](https://spinningup.openai.com/en/latest/spinningup/exercises.html) to serve as warm-ups.
Get started at [spinningup.openai.com](https://spinningup.openai.com)!
Citing Spinning Up
------------------
If you reference or use Spinning Up in your research, please cite:
```
@article{SpinningUp2018,
author = {Achiam, Joshua},
title = {{Spinning Up in Deep Reinforcement Learning}},
year = {2018}
}
```