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Welcome to the 🤗 Deep Reinforcement Learning Course

Welcome to the most fascinating topic in Artificial Intelligence: Deep Reinforcement Learning.

This course will teach you about Deep Reinforcement Learning from beginner to expert. It’s completely free and open-source!

In this introduction unit you’ll:

- Learn more about the course content.
- Define the path you’re going to take (either self-audit or certification process).
- Learn more about the AI vs. AI challenges you’re going to participate in.
- Learn more about us.
- Create your Hugging Face account (it’s free).
- Sign-up to our Discord server, the place where you can chat with your classmates and us (the Hugging Face team).

Let’s get started!

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What to expect?

In this course, you will:

- 📖 Study Deep Reinforcement Learning in theory and practice.
- 🧑‍💻 Learn to use famous Deep RL libraries such as Stable Baselines3, RL Baselines3 Zoo, Sample Factory and CleanRL.
- 🤖 Train agents in unique environments such as SnowballFight, Huggy the Doggo 🐶, VizDoom (Doom) and classical ones such as Space Invaders, PyBullet and more.
- 💾 Share your trained agents with one line of code to the Hub and also download powerful agents from the community.
- 🏆 Participate in challenges where you will evaluate your agents against other teams. You’ll also get to play against the agents you’ll train.
- 🎓 Earn a certificate of completion by completing 80% of the assignments.

And more!

At the end of this course, you’ll get a solid foundation from the basics to the SOTA (state-of-the-art) of methods.

Don’t forget to sign up to the course (we are collecting your email to be able to send you the links when each Unit is published and give you information about the challenges and updates).

Sign up 👉 here

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Course Maintenance Notice 🚧

Please note that this Deep Reinforcement Learning course is now in a low-maintenance state. However, it remains an excellent resource to learn both the theory and practical aspects of Deep Reinforcement Learning.

Keep in mind the following points:

- Unit 7 (AI vs AI) : This feature is currently non-functional. However, you can still train your agent to play soccer and observe its performance.

- Leaderboard : The leaderboard is no longer operational.

Aside from these points, all theory content and practical exercises remain fully accessible and effective for learning.

If you have any problem with one of the hands-on please check the issue sections where the community give some solutions to bugs.

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What does the course look like?

The course is composed of:

- A theory part: where you learn a concept in theory.

- A hands-on: where you’ll learn to use famous Deep RL libraries to train your agents in unique environments. These hands-on will be Google Colab notebooks with companion tutorial videos if you prefer learning with video format!

- Challenges: you’ll get to put your agent to compete against other agents in different challenges. There will also be a leaderboard for you to compare the agents’ performance.

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What’s the syllabus?

This is the course’s syllabus:

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Two paths: choose your own adventure

You can choose to follow this course either:

- To get a certificate of completion: you need to complete 80% of the assignments.
- To get a certificate of honors: you need to complete 100% of the assignments.
- As a simple audit: you can participate in all challenges and do assignments if you want.

There’s no deadlines, the course is self-paced. Both paths are completely free. Whatever path you choose, we advise you to follow the recommended pace to enjoy the course and challenges with your fellow classmates.

You don’t need to tell us which path you choose. If you get more than 80% of the assignments done, you’ll get a certificate.

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The Certification Process

The certification process is completely free:

- To get a certificate of completion: you need to complete 80% of the assignments.
- To get a certificate of honors: you need to complete 100% of the assignments.

Again, there’s no deadline since the course is self paced. But our advice is to follow the recommended pace section.

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How to get most of the course?

To get most of the course, we have some advice:

1. Join study groups in Discord : studying in groups is always easier. To do that, you need to join our discord server. If you're new to Discord, no worries! We have some tools that will help you learn about it.
2. Do the quizzes and assignments: the best way to learn is to do and test yourself.
3. Define a schedule to stay in sync: you can use our recommended pace schedule below or create yours.

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What tools do I need?

You need only 3 things:

- A computer with an internet connection.
- Google Colab (free version): most of our hands-on will use Google Colab, the free version is enough.
- A Hugging Face Account: to push and load models. If you don’t have an account yet, you can create one here (it’s free).

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What is the recommended pace?

Each chapter in this course is designed to be completed in 1 week, with approximately 3-4 hours of work per week. However, you can take as much time as necessary to complete the course. If you want to dive into a topic more in-depth, we’ll provide additional resources to help you achieve that.

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Who are we

About the author:

- Thomas Simonini is a Developer Advocate at Hugging Face 🤗 specializing in Deep Reinforcement Learning. He founded the Deep Reinforcement Learning Course in 2018, which became one of the most used courses in Deep RL.

About the team:

- Omar Sanseviero is a Machine Learning Engineer at Hugging Face where he works in the intersection of ML, Community and Open Source. Previously, Omar worked as a Software Engineer at Google in the teams of Assistant and TensorFlow Graphics. He is from Peru and likes llamas 🦙.
- Sayak Paul is a Developer Advocate Engineer at Hugging Face. He's interested in the area of representation learning (self-supervision, semi-supervision, model robustness). And he loves watching crime and action thrillers 🔪.

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What are the challenges in this course?

In this new version of the course, you have two types of challenges:

- A leaderboard to compare your agent’s performance to other classmates’.
- AI vs. AI challenges where you can train your agent and compete against other classmates’ agents.

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I still have questions

Please ask your question in our discord server #rl-discussions.

Update on GitHub