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89 lines
5.5 KiB
Markdown
89 lines
5.5 KiB
Markdown
# DEPRECIATED THE NEW UNIT 1 IS HERE: https://huggingface.co/deep-rl-course/unit1/introduction
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**Everything under is depreciated** 👇, the new version of the course is here: https://huggingface.co/deep-rl-course/unit1/introduction
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# Unit 1: Introduction to Deep Reinforcement Learning 🚀 (DEPRECIATED)
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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.
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<img src="assets/img/LunarLander.gif" alt="LunarLander"/>
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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)** 🔥.
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This course is **self-paced**, you can start whenever you want.
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## Required time ⏱️
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The required time for this unit is, approximately:
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- **2 hours** for the theory
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- **1 hour** for the hands-on.
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## Start this Unit 🚀
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Here are the steps for this Unit:
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1️⃣ 📝 **Sign up to the course** , to receive the updates when each Unit is published.
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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**
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👉🏻 [https://discord.gg/aYka4Yhff9](https://discord.gg/aYka4Yhff9).
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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
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3️⃣ 👋 **Introduce yourself on Discord in #introduce-yourself Discord channel 🤗 and check on the left the Reinforcement Learning section.**
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- In #rl-announcements we give the last information about the course.
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- #discussions is a place to exchange.
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- #unity-ml-agents is to exchange about everything related to this library.
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- #study-groups, to create study groups with your classmates.
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<img src="assets/img/discord_channels.jpg" alt="Discord Channels"/>
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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
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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
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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 🏆?
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👩💻 The hands-on 👉 [](https://colab.research.google.com/github/huggingface/deep-rl-class/blob/main/unit1/unit1.ipynb)
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🏆 The leaderboard 👉 https://huggingface.co/spaces/huggingface-projects/Deep-Reinforcement-Learning-Leaderboard
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You can work directly **with the colab notebook, which allows you not to have to install everything on your machine (and it’s free)**.
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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.
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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
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## Additional readings 📚
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- [Reinforcement Learning: An Introduction, Richard Sutton and Andrew G. Barto Chapter 1, 2 and 3](http://incompleteideas.net/book/RLbook2020.pdf)
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- [Foundations of Deep RL Series, L1 MDPs, Exact Solution Methods, Max-ent RL by Pieter Abbeel](https://youtu.be/2GwBez0D20A)
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- [Spinning Up RL by OpenAI Part 1: Key concepts of RL](https://spinningup.openai.com/en/latest/spinningup/rl_intro.html)
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- [Getting Started With OpenAI Gym: The Basic Building Blocks](https://blog.paperspace.com/getting-started-with-openai-gym/)
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## How to make the most of this course
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To make the most of the course, my advice is to:
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- **Participate in Discord** and join a study group.
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- **Read multiple times** the theory part and takes some notes.
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- 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 🥳.
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- 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.
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## This is a course built with you 👷🏿♀️
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We want to improve and update the course iteratively with your feedback. **If you have some, please fill this form** 👉 https://forms.gle/3HgA7bEHwAmmLfwh9
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## Don’t forget to join the Community 📢
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We have a discord server where you **can exchange with the community and with us, create study groups to grow each other and more**
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👉🏻 [https://discord.gg/aYka4Yhff9](https://discord.gg/aYka4Yhff9).
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Don’t forget to **introduce yourself when you sign up 🤗**
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❓If you have other questions, [please check our FAQ](https://github.com/huggingface/deep-rl-class#faq)
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## Keep learning, stay awesome 🤗,
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