# Hugging Face Deep RL Course: A Free Self-Paced Deep Reinforcement Learning Curriculum

> The Hugging Face Deep Reinforcement Learning Course is a free, self-paced curriculum with theory units and hands-on notebooks covering deep RL algorithms from Q-learning through actor-critic methods. It is built for practitioners who want both conceptual grounding and working code, and it remains accessible as a learning resource even though two features have been disabled.

**huggingface/deep-rl-class** — This repo contains the Hugging Face Deep Reinforcement Learning Course.

- Repository: https://github.com/huggingface/deep-rl-class
- Stars: 5,030 · Forks: 814
- Language: MDX
- License: Apache-2.0
- Published: 2026-09-10 · Updated: 2026-09-10 · Language: en
- Canonical page: https://hysenlabs.com/projects/huggingface-deep-rl-class

## A Free Curriculum Built Around Hands-On Notebooks

The Hugging Face Deep Reinforcement Learning Course is a structured curriculum for learning deep RL. The repository holds the MDX source files for the course website and the Jupyter notebooks used in the practical exercises. The course is published at https://huggingface.co/deep-rl-course/unit0/introduction, where it can be accessed through any browser without registration, though a sign-up link is provided for email updates.

The course was created by Thomas Simonini and Omar Sanseviero and is identified in the BibTeX citation block in the README as having been published in 2023. The course covers the theoretical foundations of reinforcement learning alongside practical implementations, with exercises that run in Google Colab or a local Jupyter environment.

The README states that the course is now in a low-maintenance state. All theory content and practical exercises remain fully accessible, but two specific features are no longer operational. The course is suitable for learners who want a complete self-paced curriculum rather than a community competition experience.

## How the Course Content Is Organized

The repository contains two top-level content directories. The units/ directory holds the MDX source files, one per course unit. MDX is a format that combines Markdown with React components, which the Hugging Face website renders into the interactive course pages. The notebooks/ directory holds the Jupyter notebooks tied to the practical exercises.

The course is structured as a series of units. Each unit covers a topic in the deep RL progression, such as Q-learning, policy gradient methods, or actor-critic architectures, pairing a theory page with a corresponding notebook exercise. The syllabus is published separately at https://simoninithomas.github.io/deep-rl-course.

The practical exercises involve training agents on environments from established libraries. The README notes that issues with specific exercises are often addressed by the community in the repository's issue tracker, and it directs learners there for bug fixes and workarounds contributed by other users.

To work with the notebooks locally, clone the repository:

```bash
git clone https://github.com/huggingface/deep-rl-class
```

This gives access to the raw MDX and notebook files. The .ipynb files in the notebooks/ directory can be opened in any Jupyter-compatible environment, independently of the Hugging Face website.

## What No Longer Works: Unit 7 and the Leaderboard

The README is explicit about two disabled features. Unit 7, titled AI vs AI, is non-functional. The README notes that learners can still train an agent to play soccer and observe its behavior, but the multiplayer component that pitted trained agents against each other no longer works.

The leaderboard, which tracked submitted agent performance across units, is also no longer operational. Learners can still complete all exercises and evaluate their agents locally, but they cannot submit results or compare performance against other participants.

These two features were the interactive and competitive elements of the course. Their absence does not affect the theory content or the training exercises. A learner going through the curriculum for understanding rather than competition will not encounter these gaps until reaching Unit 7.

## What the Course Teaches and What It Assumes

The course begins from introductory RL concepts and progresses through deep RL algorithms. The README describes the content as covering both theory and practical aspects of deep reinforcement learning, positioned as an excellent resource for both dimensions.

The README does not state a mathematical prerequisite explicitly. The course is hosted on Hugging Face's learning platform, which is oriented toward practitioners with programming experience who are new to the theoretical side of RL. Learners with a background in Python and basic machine learning will be the most likely fit.

The practical exercises use frameworks and libraries that were current at the time the course was authored. Some exercises depend on third-party environments that may have changed since the notebooks were last updated. The README directs learners to the issue tracker when they encounter problems, which is where community-contributed fixes are collected.

## Where the Course Has Limits

The course is a fixed curriculum, not a live one. The low-maintenance status means that algorithm improvements, new environments, and updated libraries are unlikely to be incorporated. A learner who finishes the course will have a grounding in the deep RL approaches that were prominent when the material was written, not necessarily the current state of the field.

There is no grading, no certificate, and no instructor feedback. Learners who need formal credentials from their RL study will not find them here. There is also no structured discussion forum directly tied to the repository; the issue tracker is the closest substitute for Q and A.

The unit progression is linear. The course does not offer branching paths for learners who already know some parts of the curriculum and want to skip ahead to specific topics without reading all preceding material.

## Comparing with CS 224r and Other Academic RL Courses

CS 224r is Stanford University's graduate-level deep reinforcement learning course. It covers similar algorithmic content but is structured as a semester course with assignments graded by teaching assistants and a formal course project. It is not free to audit in the same way, and lecture recordings may not always be publicly available.

The practical difference is accountability. A Stanford course provides instructor feedback and a credential. The Hugging Face course provides open access to the same conceptual territory with runnable notebooks, no enrollment required, and no cost. For independent learners who do not need a credential or instructor contact, the Hugging Face course removes the access barriers without removing the technical content.

## Maintenance Status and License

The last push to the repository was on 2026-09-17. The course is released under the Apache-2.0 license, which permits redistribution and derivative works with attribution.

The low-maintenance designation means the project accepts issue reports but is not under active development. The README suggests checking the issue tracker for community workarounds before concluding that a particular exercise is broken. For learners running into problems with specific notebooks, that is the most likely place to find a solution without filing a new issue.

## Conclusion

The Hugging Face Deep RL Course is worth following for anyone learning deep reinforcement learning who wants both theory explanations and working Jupyter notebooks in a single structured curriculum. The leaderboard and the Unit 7 AI-vs-AI mode are disabled, so competitive benchmarking against other learners is not possible. For practitioners who want a graded university course structure or peer review, a formal academic offering suits them better. The content on theory and practice is the primary value here, and the README states it remains fully accessible.

## FAQ

### Is the Hugging Face Deep RL Course still usable in 2026?

The README states that all theory content and practical exercises remain fully accessible. Two features are disabled: Unit 7's AI-vs-AI multiplayer mode and the leaderboard. The course content itself is unaffected.

### What background do I need to start the Hugging Face deep-rl-class?

The README does not list formal prerequisites. The course is oriented toward practitioners with programming experience who are new to deep reinforcement learning, and the exercises use Python and Jupyter notebooks.

### Can I run the deep-rl-class notebooks locally without using the Hugging Face website?

The notebooks/ directory contains standard .ipynb files that can be opened in any Jupyter-compatible environment. The course website renders the MDX unit files, but the notebooks themselves are not website-dependent.

## Sources

- [huggingface/deep-rl-class on GitHub](https://github.com/huggingface/deep-rl-class)
- [Issues](https://github.com/huggingface/deep-rl-class/issues)
- [License: Apache-2.0](https://github.com/huggingface/deep-rl-class/blob/main/LICENSE)
- [README](https://github.com/huggingface/deep-rl-class/blob/main/README.md)

---

Hysen Labs editorial analysis, written from the project's own repository and release notes. Cite the canonical page: https://hysenlabs.com/projects/huggingface-deep-rl-class