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# Unit 1: Introduction to Deep Reinforcement Learning
In this Unit, you'll learn the foundations of Deep RL. And **you’ll train your first lander agent 🚀 to land correctly on the Moon 🌕** using Stable-Baselines3 and share it with the community.
In this Unit, you'll learn the foundations of Deep Reinforcement Learning. And **you’ll train your first lander agent 🚀 to land correctly on the Moon 🌕** using Stable-Baselines3 and share it with the community.
<img src="assets/img/LunarLander.gif" alt="LunarLander"/>
You'll then be able to **compare your agent’s results with other classmates thanks to a leaderboard** 🔥.
You'll then be able to **[compare your agent’s results with other classmates thanks to the leaderboard](https://huggingface.co/spaces/huggingface-projects/Deep-Reinforcement-Learning-Leaderboard)** 🔥.
This course is **self-paced**, you can start whenever you want.
## Required time ⏱️
The required time for this unit is, approximately:
- 2 hours for the theory
- 1 hour for the hands-on.
- **2 hours** for the theory
- **1 hour** for the hands-on.
## Start this Unit 🚀
Here are the steps for this Unit:
1️⃣ Sign up to our Discord Server. This is the place where you **can exchange with the community and with us, create study groups to grow each other and more** 
1️⃣ 📝 **Sign up to the course** , to receive the updates when each Unit is published.
2️⃣ **Sign up to our Discord Server**. This is the place where you **can exchange with the community and with us, create study groups to grow each other and more** 
👉🏻 [https://discord.gg/aYka4Yhff9](https://discord.gg/aYka4Yhff9).
Are you new to Discord? Check our **discord 101 to get the best practices** 👉 https://github.com/huggingface/deep-rl-class/blob/main/DISCORD.Md
2️⃣ **Introduce yourself on Discord in #introduce-yourself Discord channel 🤗 and check on the left the Reinforcement Learning section.**
3️⃣ 👋 **Introduce yourself on Discord in #introduce-yourself Discord channel 🤗 and check on the left the Reinforcement Learning section.**
- In #rl-announcements we give the last information about the course.
- #discussions is a place to exchange.
@@ -31,21 +33,21 @@ Are you new to Discord? Check our **discord 101 to get the best practices** 👉
<img src="assets/img/discord_channels.jpg" alt="Discord Channels"/>
3️⃣ 📖 **Read An [Introduction to Deep Reinforcement Learning](https://huggingface.co/blog/deep-rl-intro)**, where you’ll learn the foundations of Deep RL. You can also watch the video version attached to the article. 👉 https://huggingface.co/blog/deep-rl-intro
4️⃣ 📖 **Read An [Introduction to Deep Reinforcement Learning](https://huggingface.co/blog/deep-rl-intro)**, where you’ll learn the foundations of Deep RL. You can also watch the video version attached to the article. 👉 https://huggingface.co/blog/deep-rl-intro
4️⃣ 📝 Take a piece of paper and **check your knowledge with this series of questions** ❔ 👉 https://github.com/huggingface/deep-rl-class/blob/main/unit1/quiz.md
5️⃣ 📝 Take a piece of paper and **check your knowledge with this series of questions** ❔ 👉 https://github.com/huggingface/deep-rl-class/blob/main/unit1/quiz.md
5️⃣ 👩‍💻 Then dive on the hands-on, where **you’ll train your first lander agent 🚀 to land correctly on the Moon 🌕 using Stable-Baselines3 and share it with the community.** 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 1 🏆?
6️⃣ 👩‍💻 Then dive on the hands-on, where **you’ll train your first lander agent 🚀 to land correctly on the Moon 🌕 using Stable-Baselines3 and share it with the community.** 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 1 🏆?
The hands-on 👉 [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/deep-rl-class/blob/main/unit1/unit1.ipynb)
👩‍💻 The hands-on 👉 [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/deep-rl-class/blob/main/unit1/unit1.ipynb)
The leaderboard 👉 https://huggingface.co/spaces/chrisjay/Deep-Reinforcement-Learning-Leaderboard
🏆 The leaderboard 👉 https://huggingface.co/spaces/huggingface-projects/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)**.
You can work directly **with the colab notebook, which allows you not to have to install everything on your machine (and it’s free)**.
6️⃣ 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.
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.
7️⃣ In order to find the best training parameters you can try this hands-on made by [Sambit Mukherjee](https://github.com/sambitmukherjee) 👉 https://github.com/huggingface/deep-rl-class/blob/main/unit1/unit1_optuna_guide.ipynb
7️⃣ (Optional) In order to **find the best training parameters you can try this hands-on** made by [Sambit Mukherjee](https://github.com/sambitmukherjee) 👉 https://github.com/huggingface/deep-rl-class/blob/main/unit1/unit1_optuna_guide.ipynb
## Additional readings 📚
- [Reinforcement Learning: An Introduction, Richard Sutton and Andrew G. Barto Chapter 1, 2 and 3](http://incompleteideas.net/book/RLbook2020.pdf)
@@ -59,8 +61,8 @@ You can work directly **with the colab notebook, which allows you not to have to
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 🥳
- **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.
## This is a course built with you 👷🏿‍♀️
@@ -77,4 +79,4 @@ Don’t forget to **introduce yourself when you sign up 🤗**
❓If you have other questions, [please check our FAQ](https://github.com/huggingface/deep-rl-class#faq)
Keep learning, stay awesome,
## Keep learning, stay awesome 🤗,