In this Unit, we'll study our first Deep Reinforcement Learning agent: Deep Q-Learning.
And we'll train it to play Space Invaders and other Atari environments using RL-Zoo, a training framework for RL using Stable-Baselines that provides scripts for training, evaluating agents, tuning hyperparameters, plotting results, and recording videos.
You'll then be able to compare your agent’s results with other classmates thanks to a leaderboard 🔥 👉 https://huggingface.co/spaces/chrisjay/Deep-Reinforcement-Learning-Leaderboard
This course is self-paced, you can start whenever you want.
The required time for this unit is, approximately:
- 1-2 hours for the theory
- 1 hour for the hands-on.
Here are the steps for this Unit:
1️⃣ 📖 Read Deep Q-Learning with Atari chapter.
2️⃣ 📝 Take a piece of paper and check your knowledge with this series of questions ❔ 👉 https://github.com/huggingface/deep-rl-class/blob/main/unit3/quiz.md
3️⃣ 👩💻 Then dive on the hands-on, where you'll train a Deep Q-Learning agent playing Space Invaders using RL Baselines3 Zoo, a training framework based on Stable-Baselines3 that provides scripts for training, evaluating agents, tuning hyperparameters, plotting results and recording videos.
Thanks to a leaderboard, you'll be able to compare your results with other classmates and exchange the best practices to improve your agent's scores Who will win the challenge for Unit 2 🏆?
The hands-on 👉
The leaderboard 👉 https://huggingface.co/spaces/chrisjay/Deep-Reinforcement-Learning-Leaderboard
You can work directly with the colab notebook, which allows you not to have to install everything on your machine (and it’s free).
4️⃣ The best way to learn is to try things on your own. That’s why we have a challenges section in the colab where we give you some ideas on how you can go further: using another environment, using another model etc.
- Foundations of Deep RL Series, L2 Deep Q-Learning by Pieter Abbeel
- Playing Atari with Deep Reinforcement Learning
- Double Deep Q-Learning
- Prioritized Experience Replay
To make the most of the course, my advice is to:
- Participate in Discord and join a study group.
- Read multiple times the theory part and takes some notes
- Don’t just do the colab. When you learn something, try to change the environment, change the parameters and read the libraries' documentation. Have fun 🥳
- Struggling is a good thing in learning. It means that you start to build new skills. Deep RL is a complex topic and it takes time to understand. Try different approaches, use our additional readings, and exchange with classmates on discord.
We want to improve and update the course iteratively with your feedback. If you have some, please fill this form 👉 https://forms.gle/3HgA7bEHwAmmLfwh9
We have a discord server where you can exchange with the community and with us, create study groups to grow each other and more
👉🏻 https://discord.gg/aYka4Yhff9.
Don’t forget to introduce yourself when you sign up 🤗
❓If you have other questions, please check our FAQ