From 4f3c617f310492429c65d697d648ea7ad8d7285f Mon Sep 17 00:00:00 2001 From: Thomas Simonini Date: Wed, 13 Jul 2022 19:12:51 +0200 Subject: [PATCH] Update unit1 readme * Some cleanups * Update links --- unit1/README.md | 36 +++++++++++++++++++----------------- 1 file changed, 19 insertions(+), 17 deletions(-) diff --git a/unit1/README.md b/unit1/README.md index 7cb3aee..a7b3a53 100644 --- a/unit1/README.md +++ b/unit1/README.md @@ -1,28 +1,30 @@ # 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. 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** 👉 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 🤗,