Launching Deep Reinforcement Learning Class with Hugging Face 🤗

Hey there👋 it’s Thomas Simonini from Deep Reinforcement Learning Course.
I’m super excited to announce the launch of the new version of the course with Hugging Face 🤗

Deep RL Class, is a free course from beginner to expert, self-paced where you’ll get solid foundations of Deep Reinforcement Learning in theory and practice with hands-on using famous RL libraries such SB3, RL-Baselines3-Zoo, RLlib, CleanRL…

In this free 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, and RLlib.
  • 🤖 Train agents in unique environments with SnowballFight, Huggy the Doggo 🐶, and classical ones such as Space Invaders and PyBullet.
  • 💾 Publish your trained agents in one line of code to the Hub. But also download powerful agents from the community.
  • 🏆 Participate in challenges where you will evaluate your agents against other teams.
  • 🖌️🎨 Learn to share your own environments made with Unity and Godot.
  • For more information, check the syllabus 📚
  • And the best way to keep in touch is to join our discord server to exchange with the community and with us.

➡️➡️➡️ Don’t forget to sign up here: http://eepurl.com/h1pElX

How does the course work?

We publish one Unit every week, with two parts:

  • A theory part where we will explain deeply the topic.
  • A hands on part where we use famous RL libraries such SB3, RL-Baselines3-Zoo, RLlib, CleanRL to train our agents that we publish on Hub in one line of Code.

The Syllabus 📚

The first three Units are already published, you can check the whole syllabus here (don’t forget to ⭐ the Github Repo 🤗) ➡️ https://github.com/huggingface/deep-rl-class

➡️➡️➡️ Don’t forget to sign up here to get the latest updates: http://eepurl.com/h1pElX

Unit 1: Introduction to Deep Reinforcement Learning

👩‍💻: Train a Deep Reinforcement Learning lander agent to land correctly on the Moon 🌕 using Stable-Baselines3

Unit 2: Introduction to Q-Learning

👩‍💻: Train an agent to cross a Frozen lake ⛄ and train an autonomous taxi 🚖.

Unit 3: Deep Q-Learning with Space Invaders and Atari Games

👩‍💻: Train a Deep Q-Learning agent to play Space Invaders using RL-Baselines3-Zoo

Back in April 2018, when I launched my first article about Introduction to RL I didn’t know that it would become one of the most-watched RL courses online and a GitHub repository with 3.400 GitHub stars. For that, I want to thank you.

I hope you will like this updated and improved version of the course. This is made for you. As a consequence, I’m always pleased to receive some feedback 📝.

If you want to help us, please like, share, and speak about our course. By sharing our articles and videos you help us to spread the word.

If you liked my article, please click the 👏 below as many time as you liked the article so other people will see this here on Medium. And don’t forget to follow me on Medium and on Youtube.

Keep learning, stay awesome,

--

--

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store