Merge pull request #379 from huggingface/ThomasSimonini/BigUpdate

Big Update of August

units/en/communication/certification.mdx

@@ -3,8 +3,10 @@
 
 The certification process is **completely free**:
 
-- To get a *certificate of completion*: you need **to pass 80% of the assignments** before the end of September 2023.
-- To get a *certificate of excellence*: you need **to pass 100% of the assignments** before the end of September 2023.
+- To get a *certificate of completion*: you need **to pass 80% of the assignments**.
+- To get a *certificate of excellence*: you need **to pass 100% of the assignments**.
+
+There's **no deadlines, the course is self-paced**.
 
 <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/unit0/introduction.mdx

@@ -59,10 +59,11 @@ This is the course's syllabus:
 
 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 September 2023.
-- *To get a certificate of honors*: you need to complete 100% of the assignments before the end of September 2023.
-- *As a simple audit*: you can participate in all challenges and do assignments if you want, but you have no deadlines.
+- *To get a certificate of completion*: you need to complete 80% of the assignments. 
+- *To get a certificate of honors*: you need to complete 100% of the assignments.
+- *As a simple audit*: you can participate in all challenges and do assignments if you want.
 
+There's **no deadlines, the course is self-paced**.
 Both paths **are completely free**.
 Whatever path you choose, we advise you **to follow the recommended pace to enjoy the course and challenges with your fellow classmates.**
 
@@ -72,8 +73,10 @@ You don't need to tell us which path you choose. **If you get more than 80% of t
 
 The certification process is **completely free**:
 
-- *To get a certificate of completion*: you need to complete 80% of the assignments before the end of September 2023.
-- *To get a certificate of honors*: you need to complete 100% of the assignments before the end of September 2023.
+- *To get a certificate of completion*: you need to complete 80% of the assignments.
+- *To get a certificate of honors*: you need to complete 100% of the assignments.
+
+Again, there's **no deadline** since the course is self paced. But our advice **is to follow the recommended pace section**.
 
 <img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit0/certification.jpg" alt="Course certification" width="100%"/>
 
@@ -113,7 +116,7 @@ About the team:
 - <a href="https://twitter.com/RisingSayak"> Sayak Paul</a> is a Developer Advocate Engineer at Hugging Face. He's interested in the area of representation learning (self-supervision, semi-supervision, model robustness). And he loves watching crime and action thrillers 🔪.
 
 
-## When do the challenges start? [[challenges]]
+## What are the challenges in this course? [[challenges]]
 
 In this new version of the course, you have two types of challenges:
 - [A leaderboard](https://huggingface.co/spaces/huggingface-projects/Deep-Reinforcement-Learning-Leaderboard) to compare your agent's performance to other classmates'.

units/en/unit1/hands-on.mdx

@@ -315,8 +315,8 @@ We see with `Observation Space Shape (8,)` that the observation is a vector of s
 - Vertical speed (y)
 - Angle
 - Angular speed
-- If the left leg contact point has touched the land
-- If the right leg contact point has touched the land
+- If the left leg contact point has touched the land (boolean)
+- If the right leg contact point has touched the land (boolean)
 
 
 ```python

units/en/unit5/how-mlagents-works.mdx

@@ -31,7 +31,7 @@ With Unity ML-Agents, you have six essential components:
 
 ## Inside the Learning Component [[inside-learning-component]]
 
-Inside the Learning Component, we have **three important elements**:
+Inside the Learning Component, we have **two important elements**:
 
 - The first is the *agent component*, the actor of the scene. We’ll **train the agent by optimizing its policy** (which will tell us what action to take in each state). The policy is called the *Brain*.
 - Finally, there is the *Academy*. This component **orchestrates agents and their decision-making processes**. Think of this Academy as a teacher who handles Python API requests.