Update hands-on.mdx

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Thomas Simonini
2022-12-20 13:54:08 +01:00
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@@ -34,6 +34,7 @@ You can either do this hands-on by reading the notebook or following it with the
<Youtube id="CsuIANBnSq8" />
# Unit 1: Train your first Deep Reinforcement Learning Agent 🤖
<img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit1/thumbnail.jpg" alt="Unit 1 thumbnail" width="100%">
@@ -42,9 +43,6 @@ In this notebook, you'll train your **first Deep Reinforcement Learning agent**
⬇️ Here is an example of what **you will achieve in just a couple of minutes.** ⬇️
```python
%%html
<video controls autoplay><source src="https://huggingface.co/ThomasSimonini/ppo-LunarLander-v2/resolve/main/replay.mp4" type="video/mp4"></video>
@@ -71,7 +69,7 @@ At the end of the notebook, you will:
## This notebook is from Deep Reinforcement Learning Course
## This hands-on is from Deep Reinforcement Learning Course
<img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/notebooks/deep-rl-course-illustration.jpg" alt="Deep RL Course illustration"/>
In this free course, you will:
@@ -90,7 +88,7 @@ The best way to keep in touch and ask questions is to join our discord server to
## Prerequisites 🏗️
Before diving into the notebook, you need to:
🔲 📝 **Done Unit 0** that gives you all the **information about the course and help you to onboard** 🤗
🔲 📝 **Read Unit 0** that gives you all the **information about the course and help you to onboard** 🤗
🔲 📚 **Develop an understanding of the foundations of Reinforcement learning** (MC, TD, Rewards hypothesis...) by doing Unit 1