From b5a77fdbd89b3c3dc40a0819b27fc3fcd8424ebb Mon Sep 17 00:00:00 2001 From: Thomas Simonini Date: Tue, 31 May 2022 19:03:35 +0200 Subject: [PATCH] Add quiz part1 link --- unit2/README.md | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/unit2/README.md b/unit2/README.md index b02f1af..4fc6892 100644 --- a/unit2/README.md +++ b/unit2/README.md @@ -44,9 +44,11 @@ Are you new to Discord? Check our **discord 101 to get the best practices** 👉 3️⃣ 📖 **Read An [Introduction to Q-Learning Part 1](https://huggingface.co/blog/deep-rl-q-part1)**. -4️⃣ 📖 **Read An [Introduction to Q-Learning Part 2](https://huggingface.co/blog/deep-rl-q-part2)**. +4️⃣ 📝 Take a piece of paper and **check your knowledge with this series of questions** ❔ 👉 https://github.com/huggingface/deep-rl-class/blob/main/unit2/quiz1.md -5️⃣ 👩‍💻 Then dive on the hands-on, where **you’ll implement our first RL agent from scratch**, a Q-Learning agent, and will train it in two environments: +5️⃣ 📖 **Read An [Introduction to Q-Learning Part 2](https://huggingface.co/blog/deep-rl-q-part2)**. + +6️⃣ 👩‍💻 Then dive on the hands-on, where **you’ll implement our first RL agent from scratch**, a Q-Learning agent, and will train it in two environments: 1. Frozen Lake v1 ❄️: where our agent will need to **go from the starting state (S) to the goal state (G)** by walking only on frozen tiles (F) and avoiding holes (H). 2. An autonomous taxi 🚕: where the agent will need **to learn to navigate** a city to **transport its passengers from point A to point B.** @@ -58,7 +60,7 @@ The leaderboard 👉 https://huggingface.co/spaces/chrisjay/Deep-Reinforcement-L 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. +7️⃣ 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 📚 - [Reinforcement Learning: An Introduction, Richard Sutton and Andrew G. Barto Chapter 5, 6 and 7](http://incompleteideas.net/book/RLbook2020.pdf)