From abd4a56c3235fee5f6a08422bbcc90d23d0a251f Mon Sep 17 00:00:00 2001
From: "Jose J. Martinez" <jose.martinez@wayops.eu>
Date: Wed, 6 Dec 2023 18:30:51 +0000
Subject: [PATCH] Unit 5 quiz and rewording of unit 6

---
 units/en/unit5/quiz.mdx | 113 +++++++++++++++++++++++++++-------------
 units/en/unit6/quiz.mdx |  18 +++----
 2 files changed, 87 insertions(+), 44 deletions(-)

diff --git a/units/en/unit5/quiz.mdx b/units/en/unit5/quiz.mdx
index badef49..7b9ec0c 100644
--- a/units/en/unit5/quiz.mdx
+++ b/units/en/unit5/quiz.mdx
@@ -2,72 +2,114 @@
 
 The best way to learn and [to avoid the illusion of competence](https://www.coursera.org/lecture/learning-how-to-learn/illusions-of-competence-BuFzf) **is to test yourself.** This will help you to find **where you need to reinforce your knowledge**.
 
-
-### Q1: What of the following statemets are true about Unity ML-Agents?
+### Q1: Which of the following tools are specifically designed for video games development?
 
 <Question
 	choices={[
 		{
-			text: "It allows you to create learning environments from the Unity ´Scene´ objects",
+			text: "Unity (C#)",
 			explain: "",
-      correct: true,
+            correct: true,
 		},
 		{
-			text: "It allows you to create and train your agents using Reinforcement Learning",
+			text: "Unreal Engine (C++)",
 			explain: "",
-      correct: true,
+            correct: true,
 		},
-    {
-			text: "Its `Communicator` component manages the communication between Unity's C# Environments/Agents and the Python back-end",
+		{
+			text: "Godot (GDScript, C++, C#)",
 			explain: "",
-      correct: true,
+            correct: true,
 		},
-    {
-			text: "The library which carries out the actual training is Pytorch",
-			explain: ""
-      correct: "true"
+		{
+			text: "JetBrains' Rider",
+			explain: "Although useful for its support of C# for Unity, it's not a video games development IDE",
+            correct: false,
 		},
-    {
-			text: "Unity ML-Agents only support Proximal Policy Optimization (PPO)",
-			explain: "No, Unity ML-Agents supports several families of algorithms, including Actor-Critic which is going to be explained in the next section"
-      correct: "false"
+		{
+			text: "JetBrains' CLion",
+			explain: "Although useful for its support of C++ for Unreal Engine, it's not a video games development IDE",
+            correct: false,
 		},
-    {
-			text: "Unity's programming language is C++",
-			explain: "It's C#. If you are interested in programming in C++, take a look at Unreal Engine `Learning Agents`"
-      correct: "false"
+		{
+			text: "Microsoft Visual Studio and Visual Studio Code",
+			explain: "Including support for both Unity and Unreal, they are generic IDEs, not video games oriented.",
+            correct: false,
 		},
 	]}
 />
 
-### Q2: Explain with your own words what is the role of the `Academy`.
+### Q2: What of the following statements are true about Unity ML-Agents?
+
+<Question
+	choices={[
+		{
+			text: "Unity ´Scene´ objects can be used to create learning environments",
+			explain: "",
+            correct: true,
+		},
+		{
+			text: "Unit ML-Agents allows you to create and train your agents using Reinforcement Learning",
+			explain: "",
+            correct: true,
+		},
+        {
+			text: "Its `Communicator` component manages the communication between Unity's C# Environments/Agents and a Python back-end",
+			explain: "",
+            correct: true,
+		},
+        {
+			text: "The training process uses Reinforcement Learning algorithms, implemented in Pytorch",
+			explain: "",
+            correct: true,
+		},
+        {
+			text: "Unity ML-Agents only support Proximal Policy Optimization (PPO)",
+			explain: "No, Unity ML-Agents supports several families of algorithms, including Actor-Critic which is going to be explained in the next section",
+            correct: false,
+		},
+        {
+			text: "It includes a Gym Wrapper and a multi-agent version of it called `PettingZoo`",
+			explain: "",
+            correct: true,
+		},
+	]}
+/>
+
+### Q3: Fill the missing letters
+
+- In Unity ML-Agents, the Policy of an Agent is called a b _ _ _ n
+- The component in charge of orchestrating the agents is called the _ c _ _ _ m _
 
 <details>
 <summary>Solution</summary>
-
-The `Academy` is the orchestrating module in charge of attending the requests from the Python API and sending them to the agents (e.g, `collect observations`)
-
-<img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit5/academy.png" alt="Academy"/>
-
+- b r a i n
+- a c a d e m y
 </details>
 
+### Q4: Define with your own words what is a `raycast`
 
-### Q3: What are the differences between capturing the environment using `frames` or `raycasts`?
+<details>
+<summary>Solution</summary>
+A raycast is (most of the times) a linear projection, as a `laser` which aims to detect collisions through objects.
+</details>
+
+### Q5: Which are the differences between capturing the environment using `frames` or `raycasts`?
 
 <Question
 	choices={[
     {
       text: "By using `frames`, the environment is defined by each of the pixels of the screen. By using `raycasts`, we only send a sample of those pixels.",
-      explain: "`Raycasts` don't have anything to do with pixels. They are projections of geometric shapes, normally lines, that we spawn to check for collisions.",
+      explain: "`Raycasts` don't have anything to do with pixels. They are linear projections (lasers) that we spawn to look for collisions.",
       correct: false,
     },
     {
-      text: "By using `raycasts`, the environment is defined by each of the pixels of the screen. By using `frames`, we spawn a geometric shape (normally lines) to check what objects it collides with",
+      text: "By using `raycasts`, the environment is defined by each of the pixels of the screen. By using `frames`, we spawn a (usually) line to check what objects it collides with",
       explain: "It's the other way around - `frames` collect pixels, `raycasts` check for collisions.",
       correct: false,
     },
     {
-      text: "By using `frames`, we collect all the pixels of the screen, which define the environment. By using `raycast`, we don't use pixels, we spawn geometric shapes (normally lines) and check for collisions",
+      text: "By using `frames`, we collect all the pixels of the screen, which define the environment. By using `raycast`, we don't use pixels, we spawn (normally) lines and check their collisions",
       explain: "",
       correct: true,
     },
@@ -75,12 +117,13 @@ The `Academy` is the orchestrating module in charge of attending the requests fr
 />
 
 
-### Q4: Name several input variables which were used in any of the Snowball or Pyramid environments
+### Q6: Name several environment and agent input variables used to train the agent in the Snowball or Pyramid environments
+
 <details>
 <summary>Solution</summary>
-- Collisions of the raycasts in charge of detecting blocks, (invisible) walls, stones, our target, switches, etc. in the environment.
-- Traditional inputs describing agent features, as its speed (it could also be position, rotation, etc. although that is covered by our raycast already).
-- Some boolean vars, as the switch (on/off) in Pyramids or the `can I shoot?` in the SnowballTarget.
+- Collisions of the raycasts spawned from the agent detecting blocks, (invisible) walls, stones, our target, switches, etc.
+- Traditional inputs describing agent features, as its speed
+- Boolean vars, as the switch (on/off) in Pyramids or the `can I shoot?` in the SnowballTarget.
 </details>
 
 
diff --git a/units/en/unit6/quiz.mdx b/units/en/unit6/quiz.mdx
index 0c49305..09228d7 100644
--- a/units/en/unit6/quiz.mdx
+++ b/units/en/unit6/quiz.mdx
@@ -3,7 +3,7 @@
 The best way to learn and [to avoid the illusion of competence](https://www.coursera.org/lecture/learning-how-to-learn/illusions-of-competence-BuFzf) **is to test yourself.** This will help you to find **where you need to reinforce your knowledge**.
 
 
-### Q1: What of the following interpretations of bias-variance tradeoff is the most accurate in the field of Reinforcement Learning?
+### Q1: Which of the following interpretations of bias-variance tradeoff is the most accurate in the field of Reinforcement Learning?
 
 <Question
 	choices={[
@@ -20,7 +20,7 @@ The best way to learn and [to avoid the illusion of competence](https://www.cour
 	]}
 />
 
-### Q2: Which of the following statements are True, when talking about models with bias and/or variance in RL?
+### Q2: Which of the following statements are true, when talking about models with bias and/or variance in RL?
 
 <Question
 	choices={[
@@ -48,29 +48,29 @@ The best way to learn and [to avoid the illusion of competence](https://www.cour
 />
 
 
-### Q3: Which of the following statements are true about Monte-carlo method?
+### Q3: Which of the following statements are true about Monte Carlo method?
 
 <Question
 	choices={[
 		{
-			text: "It's a sampling mechanism, which means we don't consider analyze all the possible states, but a sample of those",
+			text: "It's a sampling mechanism, which means we don't analyze all the possible states, but a sample of those",
 			explain: "",
       			correct: true,
 		},
     		{
 			text: "It's very resistant to stochasticity (random elements in the trajectory)",
-			explain: "Monte-carlo randomly estimates everytime a sample of trajectories. However, even same trajectories can have different reward values if they contain stochastic elements",
+			explain: "Monte Carlo randomly estimates everytime a sample of trajectories. However, even same trajectories can have different reward values if they contain stochastic elements",
       			correct: false,
 		},
     		{
-			text: "To reduce the impact of stochastic elements in Monte-Carlo, we can take `n` strategies and average them, reducing their impact impact in case of noise",
+			text: "To reduce the impact of stochastic elements in Monte Carlo, we take `n` strategies and average them, reducing their individual impact",
 			explain: "",
 			correct: true,
 		},		    
 	]}
 />
 
-### Q4: What is the Advanced Actor-Critic Method (A2C)?
+### Q4: How would you describe, with your own words, the Actor-Critic Method (A2C)?
 
 <details>
 <summary>Solution</summary>
@@ -83,12 +83,12 @@ The idea behind Actor-Critic is that we learn two function approximations:
 
 </details>
 
-### Q5: Which of the following statemets are True about the Actor-Critic Method?
+### Q5: Which of the following statements are true about the Actor-Critic Method?
 
 <Question
 	choices={[
    		 {
-			text: "The Critic does not learn from the training process",
+			text: "The Critic does not learn any function during the training process",
 			explain: "Both the Actor and the Critic function parameters are updated during training time",
       			correct: false,
 		},