Merge pull request #48 from huggingface/unit3

Unit 3 Update

unit3/README.md

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+# Unit 3: Deep Q-Learning with Atari Games 👾
+
+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](https://github.com/DLR-RM/rl-baselines3-zoo)**, a training framework for RL using Stable-Baselines that provides scripts for training, evaluating agents, tuning hyperparameters, plotting results, and recording videos.
+
+<img src="assets/img/atari-envs.gif" alt="unit 3 environments"/>
+
+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.
+
+## Required time ⏱️
+The required time for this unit is, approximately:
+- 1-2 hours for the theory
+- 1 hour for the hands-on.
+
+## Start this Unit 🚀
+Here are the steps for this Unit:
+
+1️⃣  If it's not already done, 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.**
+
+- 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.
+
+<img src="https://raw.githubusercontent.com/huggingface/deep-rl-class/unit3/unit2/assets/img/discord_channels.jpg" alt="Discord Channels"/>
+
+3️⃣ 📖 **Read [Deep Q-Learning with Atari] chapter (https://huggingface.co/blog/deep-rl-dqn)**.
+
+4️⃣ 👩‍💻 Then dive on the hands-on, where **you'll train a Deep Q-Learning agent** playing Space Invaders using [RL Baselines3 Zoo](https://github.com/DLR-RM/rl-baselines3-zoo), a training framework based on [Stable-Baselines3](https://stable-baselines3.readthedocs.io/en/master/) 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 👉 [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/deep-rl-class/blob/main/unit3/unit3.ipynb)
+
+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)**.
+
+5️⃣ 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.
+
+## Additional readings 📚
+- [Foundations of Deep RL Series, L2 Deep Q-Learning by Pieter Abbeel](https://youtu.be/Psrhxy88zww)
+- [Playing Atari with Deep Reinforcement Learning](https://arxiv.org/abs/1312.5602)
+- [Double Deep Q-Learning](https://papers.nips.cc/paper/2010/hash/091d584fced301b442654dd8c23b3fc9-Abstract.html)
+- [Prioritized Experience Replay](https://arxiv.org/abs/1511.05952)
+
+## 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
+- 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 👷🏿‍♀️
+
+We want to improve and update the course iteratively with your feedback. **If you have some, please fill this form** 👉 https://forms.gle/3HgA7bEHwAmmLfwh9
+
+## Don’t forget to join the Community 📢
+
+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](https://discord.gg/aYka4Yhff9).
+
+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,

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