diff --git a/units/en/communication/certification.mdx b/units/en/communication/certification.mdx index bf836f8..d82ef65 100644 --- a/units/en/communication/certification.mdx +++ b/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**. Course certification diff --git a/units/en/unit0/introduction.mdx b/units/en/unit0/introduction.mdx index 4b35c3c..fec1422 100644 --- a/units/en/unit0/introduction.mdx +++ b/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**. Course certification @@ -113,7 +116,7 @@ About the team: - Sayak Paul 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'. diff --git a/units/en/unit1/hands-on.mdx b/units/en/unit1/hands-on.mdx index b91eab6..e788cfb 100644 --- a/units/en/unit1/hands-on.mdx +++ b/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 diff --git a/units/en/unit5/how-mlagents-works.mdx b/units/en/unit5/how-mlagents-works.mdx index 12acede..f92054f 100644 --- a/units/en/unit5/how-mlagents-works.mdx +++ b/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.