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+# Unit 5: Policy Gradient with PyTorch 
+
+In this Unit, **we'll study Policy Gradient Methods**. 
+
+And we'll **implement Reinforce (a policy gradient method) from scratch using PyTorch**. Before testing its robustness using CartPole-v1, PixelCopter, and Pong.
+
+<img src="assets/img/envs.gif" alt="unit 5 environments"/>
+
+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
+
+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
+- 1-2 hours for the hands-on.
+
+## Start this Unit 🚀
+Here are the steps for this Unit:
+
+1️⃣ 📖 **Read [Policy Gradient with PyTorch] Chapter (https://huggingface.co/blog/deep-rl-pg)**.
+
+2️⃣ 👩‍💻 Then dive on the hands-on where you'll **code your first Deep Reinforcement Learning algorithm from scratch: Reinforce**.
+
+Reinforce is a *Policy-Based Method*: a Deep Reinforcement Learning algorithm that tries **to optimize the policy directly without using an action-value function**.
+More precisely, Reinforce is a *Policy-Gradient Method*, a subclass of *Policy-Based Methods* that aims **to optimize the policy directly by estimating the weights of the optimal policy using Gradient Ascent**.
+
+To test its robustness, we're going to train it in 3 different simple environments:
+- Cartpole-v1
+- PongEnv
+- PixelcopterEnv
+
+Thanks to a leaderboard, **you'll be able to compare your results with other classmates** and exchange the best practices to improve your agent's scores Who will win the challenge for Unit 5 🏆?
+
+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/unit5/unit5.ipynb)
+
+The leaderboard 👉 https://huggingface.co/spaces/chrisjay/Deep-Reinforcement-Learning-Leaderboard
+
+You can work directly **with the colab notebook, which allows you not to have to install everything on your machine (and it’s free)**.
+
+
+## Additional readings 📚
+- [Foundations of Deep RL Series, L3 Policy Gradients and Advantage Estimation by Pieter Abbeel](https://youtu.be/AKbX1Zvo7r8)
+- [Policy Gradient Algorithms](https://lilianweng.github.io/posts/2018-04-08-policy-gradient/)
+
+## 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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