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70 lines
3.8 KiB
Markdown
70 lines
3.8 KiB
Markdown
# Unit 1: Introduction to Deep Reinforcement Learning
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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.
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You'll then be able to **compare your agent’s results with other classmates thanks to a 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 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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2️⃣ **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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3️⃣ 📖 Read An [Introduction to Deep Reinforcement Learning](), where you’ll learn the foundations of Deep RL. You can also watch the video version attached to the article. 👉 [ARTICLE LINK]
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4️⃣ 👩💻 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 👉
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The 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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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.
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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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## 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 channel and join a study group.
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- Read multiple time 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 documentations. Have fun 🥳
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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 open an issue on the Github Repo: [https://github.com/huggingface/deep-rl-class/issues](https://github.com/huggingface/deep-rl-class/issues)
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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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