Update PG and add hands-on

units/en/_toctree.yml

@@ -120,8 +120,6 @@
     title: The advantages and disadvantages of Policy-based methods
   - local: unit4/policy-gradient
     title: Diving deeper into Policy-gradient methods
-  - local: unit4/reinforce
-    title: The Reinforce algorithm
   - local: unit4/hands-on
     title: Hands-on
   - local: unit4/quiz

units/en/unit4/hands-on.mdx

@@ -1 +1,33 @@
 # Hands on
+
+
+
+      <CourseFloatingBanner classNames="absolute z-10 right-0 top-0"
+      notebooks={[
+        {label: "Google Colab", value: "https://colab.research.google.com/github/huggingface/deep-rl-class/blob/main/notebooks/unit4/unit4.ipynb"}
+        ]}
+        askForHelpUrl="http://hf.co/join/discord" />
+
+
+
+Now that we studied the theory behind Reinforce, **you’re ready to code your Reinforce agent with PyTorch**. And you'll test its robustness using CartPole-v1 and PixelCopter,.
+
+You'll then be able to iterate and improve this implementation for more advanced environments.
+
+<figure class="image table text-center m-0 w-full">
+  <img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit6/envs.gif" alt="Environments"/>
+</figure>
+
+
+To validate this hands-on for the certification process, you need to push your trained models to the Hub.
+
+- Get a result of >= 450 for `Cartpole-v1`.
+- Get a result of >= 5 for `PixelCopter`.
+
+To find your result, go to the leaderboard and find your model, **the result = mean_reward - std of reward**. **If you don't see your model on the leaderboard, go at the bottom of the leaderboard page and click on the refresh button**.
+
+For more information about the certification process, check this section 👉 https://huggingface.co/deep-rl-course/en/unit0/introduction#certification-process
+
+**To start the hands-on click on Open In Colab button** 👇 :
+
+[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/deep-rl-class/blob/master/notebooks/unit4/unit4.ipynb)

units/en/unit4/policy-gradient.mdx

@@ -85,8 +85,8 @@ Fortunately we're going to use a solution called the Policy Gradient Theorem tha
 
 <img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit6/policy_gradient_theorem.png" alt="Policy Gradient"/>
 
-## The Reinforce algorithm
-The Reinforce algorithm is a policy-gradient algorithm that works like this:
+## The Reinforce algorithm (Monte Carlo Reinforce)
+The Reinforce algorithm also called Monte-Carlo policy-gradient is a policy-gradient algorithm that **uses an estimated return from an entire episode to update the policy parameter** \\(\theta\\):
 
 In a loop:
 - Use the policy \\(\pi_\theta\\)  to collect an episode \\(\tau\\)

units/en/unit4/reinforce.mdx

@@ -1,20 +0,0 @@
-# Monte Carlo Policy Gradient (Reinforce)
-
-
-
-Now that we have seen the big picture of Policy-Gradient and its advantages and disadvantages, **let's study and implement one of them**: Reinforce.
-
-## Reinforce (Monte Carlo Policy Gradient)
-
-Reinforce, also called Monte-Carlo Policy Gradient, **uses an estimated return from an entire episode to update the policy parameter** \\(\theta\\).
-
-
-Now that we studied the theory behind Reinforce, **you’re ready to code your Reinforce agent with PyTorch**. And you'll test its robustness using CartPole-v1, PixelCopter, and Pong.
-
-Start the tutorial here 👉 https://colab.research.google.com/github/huggingface/deep-rl-class/blob/main/unit5/unit5.ipynb
-
-The leaderboard to compare your results with your classmates 🏆 👉 https://huggingface.co/spaces/chrisjay/Deep-Reinforcement-Learning-Leaderboard
-
-<figure class="image table text-center m-0 w-full">
-  <img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit6/envs.gif" alt="Environments"/>
-</figure>