CS_ALearn/deep-rl-class
1
0
Fork 0
mirror of https://github.com/huggingface/deep-rl-class.git synced 2026-04-02 02:00:15 +08:00
Code Issues Projects Releases Wiki Activity

Add quiz part1 link

Browse Source
This commit is contained in:
Thomas Simonini
2022-05-31 19:03:35 +02:00
committed by GitHub
parent 08d2d511ac
commit b5a77fdbd8
1 changed files with 5 additions and 3 deletions
Download Patch File Download Diff File Expand all files Collapse all files

8
unit2/README.md
View File

@@ -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)