Some minor updates

notebooks/unit1/unit1.ipynb

@@ -230,15 +230,6 @@
       "execution_count": null,
       "outputs": []
     },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "TODO CHANGE LINK OF THE REQUIREMENTS"
-      ],
-      "metadata": {
-        "id": "32e3NPYgH5ET"
-      }
-    },
     {
       "cell_type": "code",
       "execution_count": null,
@@ -1155,4 +1146,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 0
-}
+}

units/en/unit0/introduction.mdx

@@ -52,8 +52,8 @@ The course is composed of:
 
 You can choose to follow this course either:
 
-- *To get a certificate of completion*: you need to complete 80% of the assignments before the end of March 2023.
-- *To get a certificate of honors*: you need to complete 100% of the assignments before the end of March 2023.
+- *To get a certificate of completion*: you need to complete 80% of the assignments before the end of April 2023.
+- *To get a certificate of honors*: you need to complete 100% of the assignments before the end of April 2023.
 - *As a simple audit*: you can participate in all challenges and do assignments if you want, but you have no deadlines.
 
 Both paths **are completely free**.
@@ -65,8 +65,8 @@ You don't need to tell us which path you choose. At the end of March, when we wi
 
 The certification process is **completely free**:
 
-- *To get a certificate of completion*: you need to complete 80% of the assignments before the end of March 2023.
-- *To get a certificate of honors*: you need to complete 100% of the assignments before the end of March 2023.
+- *To get a certificate of completion*: you need to complete 80% of the assignments before the end of April 2023.
+- *To get a certificate of honors*: you need to complete 100% of the assignments before the end of April 2023.
 
 <img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit0/certification.jpg" alt="Course certification" width="100%"/>
 

units/en/unit1/hands-on.mdx

@@ -24,6 +24,8 @@ To find your result, go to the [leaderboard](https://huggingface.co/spaces/huggi
 
 For more information about the certification process, check this section 👉 https://huggingface.co/deep-rl-course/en/unit0/introduction#certification-process
 
+And you can check your progress here 👉 https://huggingface.co/spaces/ThomasSimonini/Check-my-progress-Deep-RL-Course
+
 So let's get started! 🚀
 
 **To start the hands-on click on Open In Colab button** 👇 :

units/en/unit2/glossary.mdx

@@ -14,12 +14,14 @@ This is a community-created glossary. Contributions are welcomed!
 - **The action-value function.** In contrast to the state-value function, the action-value calculates for each state and action pair the expected return if the agent starts in that state and takes an action. Then it follows the policy forever after.
 
 ### Epsilon-greedy strategy:
+
 - Common exploration strategy used in reinforcement learning that involves balancing exploration and exploitation.
 - Chooses the action with the highest expected reward with a probability of 1-epsilon.
 - Chooses a random action with a probability of epsilon.
 - Epsilon is typically decreased over time to shift focus towards exploitation.
 
 ### Greedy strategy:
+
 - Involves always choosing the action that is expected to lead to the highest reward, based on the current knowledge of the environment. (only exploitation)
 - Always chooses the action with the highest expected reward.
 - Does not include any exploration.

units/en/unit2/hands-on.mdx

@@ -22,6 +22,8 @@ To find your result, go to the [leaderboard](https://huggingface.co/spaces/huggi
 
 For more information about the certification process, check this section 👉 https://huggingface.co/deep-rl-course/en/unit0/introduction#certification-process
 
+And you can check your progress here 👉 https://huggingface.co/spaces/ThomasSimonini/Check-my-progress-Deep-RL-Course
+
 
 **To start the hands-on click on Open In Colab button** 👇 :
 

units/en/unit3/hands-on.mdx

@@ -24,6 +24,9 @@ To find your result, go to the leaderboard and find your model, **the result = m
 
 For more information about the certification process, check this section 👉 https://huggingface.co/deep-rl-course/en/unit0/introduction#certification-process
 
+And you can check your progress here 👉 https://huggingface.co/spaces/ThomasSimonini/Check-my-progress-Deep-RL-Course
+
+
 **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/unit3/unit3.ipynb)

units/en/unit4/hands-on.mdx

@@ -28,6 +28,9 @@ To find your result, go to the leaderboard and find your model, **the result = m
 
 For more information about the certification process, check this section 👉 https://huggingface.co/deep-rl-course/en/unit0/introduction#certification-process
 
+And you can check your progress here 👉 https://huggingface.co/spaces/ThomasSimonini/Check-my-progress-Deep-RL-Course
+
+
 **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)
@@ -671,7 +674,7 @@ def record_video(env, policy, out_directory, fps=30):
 ```
 
 ```python
-def push_to_hub(repo_id, 
+def push_to_hub(repo_id,
                 model,
                 hyperparameters,
                 eval_env,
@@ -689,12 +692,12 @@ def push_to_hub(repo_id,
   :param model: the pytorch model we want to save
   :param hyperparameters: training hyperparameters
   :param eval_env: evaluation environment
-  :param video_fps: how many frame per seconds to record our video replay 
+  :param video_fps: how many frame per seconds to record our video replay
   """
 
   _, repo_name = repo_id.split("/")
   api = HfApi()
-  
+
   # Step 1: Create the repo
   repo_url = api.create_repo(
         repo_id=repo_id,
@@ -703,25 +706,25 @@ def push_to_hub(repo_id,
 
   with tempfile.TemporaryDirectory() as tmpdirname:
     local_directory = Path(tmpdirname)
-  
+
     # Step 2: Save the model
     torch.save(model, local_directory / "model.pt")
 
     # Step 3: Save the hyperparameters to JSON
     with open(local_directory / "hyperparameters.json", "w") as outfile:
       json.dump(hyperparameters, outfile)
-    
+
     # Step 4: Evaluate the model and build JSON
-    mean_reward, std_reward = evaluate_agent(eval_env, 
+    mean_reward, std_reward = evaluate_agent(eval_env,
                                             hyperparameters["max_t"],
-                                            hyperparameters["n_evaluation_episodes"], 
+                                            hyperparameters["n_evaluation_episodes"],
                                             model)
     # Get datetime
     eval_datetime = datetime.datetime.now()
     eval_form_datetime = eval_datetime.isoformat()
 
     evaluate_data = {
-          "env_id": hyperparameters["env_id"], 
+          "env_id": hyperparameters["env_id"],
           "mean_reward": mean_reward,
           "n_evaluation_episodes": hyperparameters["n_evaluation_episodes"],
           "eval_datetime": eval_form_datetime,
@@ -733,7 +736,7 @@ def push_to_hub(repo_id,
 
     # Step 5: Create the model card
     env_name = hyperparameters["env_id"]
-    
+
     metadata = {}
     metadata["tags"] = [
           env_name,