From f3b2cb11bcb2ff7cbd377b6a0585831377190878 Mon Sep 17 00:00:00 2001
From: Thomas Simonini <simonini.thomas.pro@gmail.com>
Date: Sun, 8 Jan 2023 12:10:30 +0100
Subject: [PATCH] Update introduction.mdx

---
 units/en/unit5/introduction.mdx | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/units/en/unit5/introduction.mdx b/units/en/unit5/introduction.mdx
index 5ef5795..0ac033f 100644
--- a/units/en/unit5/introduction.mdx
+++ b/units/en/unit5/introduction.mdx
@@ -1,5 +1,7 @@
 # An Introduction to Unity ML-Agents [[introduction-to-ml-agents]]
 
+<img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit7/thumbnail.png" alt="thumbnail"/>
+
 One of the challenges in Reinforcement Learning is to **create environments**. Fortunately for us, we can use game engines.
 Game engines like [Unity](https://unity.com/), [Godot](https://godotengine.org/) or [Unreal Engine](https://www.unrealengine.com/), are programs made to create video games. They are perfectly suited
 for creating environments: they provide physics systems, 2D/3D rendering, and more.