# Unit 1: Introduction to Deep Reinforcement Learning ๐
In this Unit, you'll learn the foundations of Deep Reinforcement Learning. And **youโll train your first lander agent ๐ to land correctly on the Moon ๐** using Stable-Baselines3 and share it with the community.
You'll then be able to **[compare your agentโs results with other classmates thanks to the leaderboard](https://huggingface.co/spaces/huggingface-projects/Deep-Reinforcement-Learning-Leaderboard)** ๐ฅ.
This course is **self-paced**, you can start whenever you want.
## Required time โฑ๏ธ
The required time for this unit is, approximately:
- **2 hours** for the theory
- **1 hour** for the hands-on.
## Start this Unit ๐
Here are the steps for this Unit:
1๏ธโฃ ๐ **Sign up to the course** , to receive the updates when each Unit is published.
2๏ธโฃ **Sign up to our Discord Server**. This is the place where youย **can exchange with the community and with us, create study groups to grow each other and more**ย
๐๐ปย [https://discord.gg/aYka4Yhff9](https://discord.gg/aYka4Yhff9).
Are you new to Discord? Check our **discord 101 to get the best practices** ๐ https://github.com/huggingface/deep-rl-class/blob/main/DISCORD.Md
3๏ธโฃ ๐ **Introduce yourself on Discord in #introduce-yourself Discord channelย ๐ค 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.
- #unity-ml-agents is to exchange about everything related to this library.
- #study-groups, to create study groups with your classmates.
4๏ธโฃ ๐ **Read An [Introduction to Deep Reinforcement Learning](https://huggingface.co/blog/deep-rl-intro)**, where youโll learn the foundations of Deep RL. You can also watch the video version attached to the article. ๐ https://huggingface.co/blog/deep-rl-intro
5๏ธโฃ ๐ Take a piece of paper and **check your knowledge with this series of questions** โ ๐ https://github.com/huggingface/deep-rl-class/blob/main/unit1/quiz.md
6๏ธโฃ ๐ฉโ๐ป Then dive on the hands-on, where **youโll train your first lander agent ๐ to land correctly on the Moon ๐ 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 ๐?
๐ฉโ๐ป The hands-on ๐ [](https://colab.research.google.com/github/huggingface/deep-rl-class/blob/main/unit1/unit1.ipynb)
๐ The leaderboard ๐ 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 itโs free)**.
The best way to learn **is to try things on your own**. Thatโs 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.
7๏ธโฃ (Optional) In order to **find the best training parameters you can try this hands-on** made by [Sambit Mukherjee](https://github.com/sambitmukherjee) ๐ https://github.com/huggingface/deep-rl-class/blob/main/unit1/unit1_optuna_guide.ipynb
## Additional readings ๐
- [Reinforcement Learning: An Introduction, Richard Sutton and Andrew G. Barto Chapter 1, 2 and 3](http://incompleteideas.net/book/RLbook2020.pdf)
- [Foundations of Deep RL Series, L1 MDPs, Exact Solution Methods, Max-ent RL by Pieter Abbeel](https://youtu.be/2GwBez0D20A)
- [Spinning Up RL by OpenAI Part 1: Key concepts of RL](https://spinningup.openai.com/en/latest/spinningup/rl_intro.html)
- [Getting Started With OpenAI Gym: The Basic Building Blocks](https://blog.paperspace.com/getting-started-with-openai-gym/)
## How to make the most of this course
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.
- Donโt just do the colab. When you learn something, try to change the environment, change the parameters and read the libraries' documentation. Have fun ๐ฅณ.
- 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 ๐ท๐ฟโโ๏ธ
We want to improve and update the course iteratively with your feedback. **If you have some, please fill this form** ๐ https://forms.gle/3HgA7bEHwAmmLfwh9
## Donโt forget to join the Community ๐ข
We have a discord server where youย **can exchange with the community and with us, create study groups to grow each other and more**ย
๐๐ปย [https://discord.gg/aYka4Yhff9](https://discord.gg/aYka4Yhff9).
Donโt forget toย **introduce yourself when you sign upย ๐ค**
โIf you have other questions, [please check our FAQ](https://github.com/huggingface/deep-rl-class#faq)
## Keep learning, stay awesome ๐ค,