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 📝.
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