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@@ -30,9 +30,10 @@ This course is **self-paced** you can start when you want 🥳.
 | [Published 🥳](https://github.com/huggingface/deep-rl-class/tree/main/unit4#unit-4-an-introduction-to-unity-mlagents-with-hugging-face-) | [🎁 Learn to train your first Unity MLAgent](https://github.com/huggingface/deep-rl-class/tree/main/unit4#unit-4-an-introduction-to-unity-mlagents-with-hugging-face-) | [Train a curious agent to destroy Pyramids 💥](https://colab.research.google.com/github/huggingface/deep-rl-class/blob/main/unit4/unit4.ipynb)  |
 | [Published 🥳](https://github.com/huggingface/deep-rl-class/tree/main/unit5#unit-5-policy-gradient-with-pytorch) | [Policy Gradient with PyTorch](https://huggingface.co/blog/deep-rl-pg)                                     | [Code a Reinforce agent from scratch using PyTorch and train it to play Pong 🎾, CartPole and Pixelcopter 🚁](https://colab.research.google.com/github/huggingface/deep-rl-class/blob/main/unit5/unit5.ipynb)                                       |
 | [Published 🥳](https://github.com/huggingface/deep-rl-class/tree/main/unit6#towards-better-explorations-methods-with-curiosity) | [Towards better explorations methods with Curiosity](https://github.com/huggingface/deep-rl-class/tree/main/unit6#towards-better-explorations-methods-with-curiosity)|                                                                                                         |
-|  July the 15th | Actor-Critic Methods                                     | 🏗️                                                                                                        |
-| July the 21th | Proximal Policy Optimization (PPO)                       | 🏗️                                                                                                        |
-| July the 28th | Decision Transformers and offline Reinforcement Learning | 🏗️                                                                                                        |
+| [Published 🥳]() | [Bonus: Robotics Simulations with PyBullet 🤖]()| [Train a bipedal walker and a spider to learn to walk](https://colab.research.google.com/github/huggingface/deep-rl-class/blob/main/unit7/unit7.ipynb)                                                                                                        |
+|  July the 22th | Actor-Critic Methods                                     | 🏗️                                                                                                        |
+| July the 29th | Proximal Policy Optimization (PPO)                       | 🏗️                                                                                                        |
+| August | Decision Transformers and offline Reinforcement Learning | 🏗️                                                                                                        |
 
 
 
diff --git a/unit7/README.md b/unit7/README.md
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+# Unit 7: Robotics Simulations with PyBullet 🤖
+One of the major industries that use Reinforcement Learning is robotics. Unfortunately, having access to robot equipment is very expensive. Fortunately, some simulations exist to train Robots:
+1. PyBullet
+2. MuJoco
+3. Unity Simulations
+
+We're going to use PyBullet today. And train two agents to walk:
+- A bipedal walker 🦿
+- A spider 🕸️
+
+You'll then be able to **compare your agent’s results with other classmates thanks to a leaderboard** 🔥 👉 https://huggingface.co/spaces/chrisjay/Deep-Reinforcement-Learning-Leaderboard
+
+IMAGE
+
+We'll learn to use PyBullet environments and why we normalize input features.
+
+Let's get started 🥳
+
+## Required time ⏱️
+The required time for this unit is, approximately:
+- 1 hour for the hands-on.
+
+## Start this Unit 🚀
+Here are the steps for this Unit:
+
+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/unit3/unit3.ipynb)
+
+The leaderboard 👉 https://huggingface.co/spaces/chrisjay/Deep-Reinforcement-Learning-Leaderboard
+
+## How to make the most of this course
+
+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 🥳
+- 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 👷🏿‍♀️
+
+We want to improve and update the course iteratively with your feedback. **If you have some, please fill this form** 👉 https://forms.gle/3HgA7bEHwAmmLfwh9
+
+## Don’t forget to join the Community 📢
+
+We have a discord server 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).
+
+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 🤗