diff --git a/units/en/_toctree.yml b/units/en/_toctree.yml
index cc96faf..02ff883 100644
--- a/units/en/_toctree.yml
+++ b/units/en/_toctree.yml
@@ -148,6 +148,8 @@
     title: Hands-on
   - local: unit5/bonus
     title: Bonus. Learn to create your own environments with Unity and MLAgents
+  - local: unit5/quiz
+    title: Quiz
   - local: unit5/conclusion
     title: Conclusion
 - title: Unit 6. Actor Critic methods with Robotics environments
diff --git a/units/en/unit5/quiz.mdx b/units/en/unit5/quiz.mdx
new file mode 100644
index 0000000..7b9ec0c
--- /dev/null
+++ b/units/en/unit5/quiz.mdx
@@ -0,0 +1,130 @@
+# Quiz
+
+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: Which of the following tools are specifically designed for video games development?
+
+<Question
+	choices={[
+		{
+			text: "Unity (C#)",
+			explain: "",
+            correct: true,
+		},
+		{
+			text: "Unreal Engine (C++)",
+			explain: "",
+            correct: true,
+		},
+		{
+			text: "Godot (GDScript, C++, C#)",
+			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: "JetBrains' CLion",
+			explain: "Although useful for its support of C++ for Unreal Engine, it's not a video games development IDE",
+            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: 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>
+- b r a i n
+- a c a d e m y
+</details>
+
+### Q4: Define with your own words what is a `raycast`
+
+<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 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 (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 (normally) lines and check their collisions",
+      explain: "",
+      correct: true,
+    },
+	]}
+/>
+
+
+### 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 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>
+
+
+Congrats on finishing this Quiz 🥳, if you missed some elements, take time to read the chapter again to reinforce (😏) your knowledge.
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,
 		},