Merge pull request #84 from huggingface/unit9

Unit9: Adding Decision Transformers

README.md

@@ -32,7 +32,7 @@ This course is **self-paced** you can start when you want 🥳.
 | [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)|                                                                                                         |
 | [Published 🥳](https://github.com/huggingface/deep-rl-class/tree/main/unit7#unit-7-advantage-actor-critic-a2c-using-robotics-simulations-with-pybullet-) | [Advantage Actor Critic (A2C)](https://github.com/huggingface/deep-rl-class/tree/main/unit7#unit-7-advantage-actor-critic-a2c-using-robotics-simulations-with-pybullet-) | [Train a bipedal walker and a spider to learn to walk using A2C](https://github.com/huggingface/deep-rl-class/tree/main/unit7#unit-7-advantage-actor-critic-a2c-using-robotics-simulations-with-pybullet-)                                                                                                        |
 | [Published 🥳](https://github.com/huggingface/deep-rl-class/tree/main/unit8#unit-8-proximal-policy-optimization-ppo-with-pytorch) | [Proximal Policy Optimization (PPO)](https://github.com/huggingface/deep-rl-class/tree/main/unit8#unit-8-proximal-policy-optimization-ppo-with-pytorch)                       | [Code a PPO agent from scratch using PyTorch and bulletproof it with Classical Control Environments](https://colab.research.google.com/github/huggingface/deep-rl-class/blob/main/unit8/unit8.ipynb)                                                                                                           |
-| TBA | Decision Transformers and offline Reinforcement Learning | 🏗️                                                                                                        |
+| [Published 🥳](https://github.com/huggingface/deep-rl-class/tree/main/unit9#unit-9-decision-transformers-and-offline-reinforcement-learning-) | [Decision Transformers and offline Reinforcement Learning](https://github.com/huggingface/deep-rl-class/tree/main/unit9#unit-9-decision-transformers-and-offline-reinforcement-learning-) |  [Train your first Offline Decision Transformer model from scratch to make a half-cheetah run](ADD LINK)                                                                                                        |
 
 
 

unit9/README.md

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+# Unit 9: Decision Transformers and offline Reinforcement Learning 🤖
+
+![cover](assets/img/thumbnail.gif)
+
+In this Unit, you'll learn what is Decision Transformer and Offline Reinforcement Learning. And then, you’ll train your first Offline Decision Transformer model from scratch to make a half-cheetah run. 
+
+This course is **self-paced**, you can start whenever you want.
+
+## Required time ⏱️
+The required time for this unit is, approximately:
+- **1 hour** for the theory
+- **1 hour** for the hands-on.
+
+## Start this Unit 🚀
+Here are the steps for this Unit:
+
+1️⃣ 📖 Read [Introducing Decision Transformers on Hugging Face 🤗](https://huggingface.co/blog/decision-transformers)
+
+2️⃣ 👩‍💻 Then dive on the first hands-on.
+ 👩‍💻 The hands-on 👉 [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1K3UuajwoPY1MzRKNkONNRS3gS5DxZ-qF?usp=sharing)
+ 
+3️⃣ 📖 Read [Train your first Decision Transformer](https://huggingface.co/blog/train-decisions-transformers)
+
+4️⃣ 👩‍💻 Then dive on the hands-on, where **you’ll train your first Offline Decision Transformer model from scratch to make a half-cheetah run**. 
+ 👩‍💻 The hands-on 👉 [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)]()
+
+ADD LINK
+
+## 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 🤗,

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