From a31043822e7f23d4f55df549b1b9896e2c56f29d Mon Sep 17 00:00:00 2001
From: Juan Martinez <36634572+josejuanmartinez@users.noreply.github.com>
Date: Sun, 3 Dec 2023 18:58:37 +0000
Subject: [PATCH 1/8] Create quiz for unit 6

---
 units/en/unit6/quiz.mdx | 119 ++++++++++++++++++++++++++++++++++++++++
 1 file changed, 119 insertions(+)
 create mode 100644 units/en/unit6/quiz.mdx

diff --git a/units/en/unit6/quiz.mdx b/units/en/unit6/quiz.mdx
new file mode 100644
index 0000000..f9832a9
--- /dev/null
+++ b/units/en/unit6/quiz.mdx
@@ -0,0 +1,119 @@
+# 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: What of the following interpretations of bias-variance tradeoff is the most accurate in the field of Reinforcement Learning?
+
+<Question
+	choices={[
+		{
+			text: "The bias-variance tradeoff reflects how my model is able to generalize the knowledge to previously given tagged data we give to the model during training time.",
+			explain: "This is the traditional bias-variance tradeoff, but we don't have previously tagged data in Reinforcement Learning, but only a reward signal.",
+      correct: false,
+		},
+    {
+			text: "The bias-variance tradeoff reflects how well the reinforcement signal R (reward) reflects the true reward the agent should agent from the enviromment",
+			explain: "",
+      correct: true,
+		},		
+	]}
+/>
+
+### Q2: Which of the following statements are correct?
+<Question
+	choices={[
+		{
+			text: "A biased reward signal returns rewards similar to the real / expected ones from the environment",
+			explain: "",
+      correct: true,
+		},
+    {
+			text: "An unbiased reward signal returns rewards similar to the real / expected ones from the environment",
+			explain: "If a reward signal is unbiased, it means the reward signal we get is similar to the real reward we should be getting from an environment",
+      correct: false,
+		},
+     ,
+    {
+			text: "A reward signal with high variance has much noise in it and gets affected by, for example, stochastic (non constant) elements in the environment"
+			explain: "",
+      correct: true,
+		},		
+    {
+			text: "A reward signal with low variance has much noise in it and gets affected by, for example, stochastic (non constant) elements in the environment"
+			explain: "If a reward signal has low variance, then it's less affected by the noise of the environment, as elements appearing randomly in the trajectory",
+      correct: true,
+		},
+	]}
+/>
+
+
+### 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",
+			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",
+      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"
+			explain: "",
+      correct: true,
+		},		    
+	]}
+/>
+
+### Q4: What is the Advanced Actor-Critic Method (A2C)?
+<details>
+<summary>Solution</summary>
+
+The idea behind Actor-Critic is the following - we learn two function approximations:
+1. A policy that controls how our agent acts (π)
+2. A value function to assist the policy update by measuring how good the action taken is (q)
+
+<img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit8/step2.jpg" alt="Actor-Critic, step 2"/>
+
+</details>
+
+### Q5: Which of the following statemets are True about the Actor-Critic Method?
+<Question
+	choices={[
+    {
+			text: "The Critic does not learn from the training process",
+			explain: "Both the Actor and the Critic function parameters are updated during training time",
+      correct: false,
+		},
+		{
+			text: "The Actor learns a policy function, while the Critic learns a value function",
+			explain: "",
+      correct: true,
+		},
+    {
+			text: "It adds resistance to stochasticity and reduces high variance",
+			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: true,
+		},	    
+	]}
+/>
+
+
+
+### Q6: What is Advantege in the A2C method?
+<details>
+<summary>Solution</summary>
+
+Instead of using directly the Action-Value function of the Critic as it is, we calculate an Advantage function,  the relative advantage of an action compared to the others possible at a state.
+In other words: how taking that action at a state is better compared to the average value of the state
+
+<img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit8/advantage1.jpg" alt="Advantage in A2C"/>
+
+</details>
+
+Congrats on finishing this Quiz 🥳, if you missed some elements, take time to read the chapter again to reinforce (😏) your knowledge.

From 57678da563ef761e8a00fdfbab7e0b632d3d2b9e Mon Sep 17 00:00:00 2001
From: Juan Martinez <36634572+josejuanmartinez@users.noreply.github.com>
Date: Sun, 3 Dec 2023 19:16:18 +0000
Subject: [PATCH 2/8] Update quiz.mdx

---
 units/en/unit6/quiz.mdx | 33 +++++++++++++++++----------------
 1 file changed, 17 insertions(+), 16 deletions(-)

diff --git a/units/en/unit6/quiz.mdx b/units/en/unit6/quiz.mdx
index f9832a9..b53a5ef 100644
--- a/units/en/unit6/quiz.mdx
+++ b/units/en/unit6/quiz.mdx
@@ -8,29 +8,29 @@ The best way to learn and [to avoid the illusion of competence](https://www.cour
 <Question
 	choices={[
 		{
-			text: "The bias-variance tradeoff reflects how my model is able to generalize the knowledge to previously given tagged data we give to the model during training time.",
-			explain: "This is the traditional bias-variance tradeoff, but we don't have previously tagged data in Reinforcement Learning, but only a reward signal.",
+			text: "The bias-variance tradeoff reflects how my model is able to generalize the knowledge to previously tagged data we give to the model during training time.",
+			explain: "This is the traditional bias-variance tradeoff in Machine Learning. In our specific case of Reinforcement Learning, we don't have previously tagged data, but only a reward signal.",
       correct: false,
 		},
     {
-			text: "The bias-variance tradeoff reflects how well the reinforcement signal R (reward) reflects the true reward the agent should agent from the enviromment",
+			text: "The bias-variance tradeoff reflects how well the reinforcement signal reflects the true reward the agent should get from the enviromment",
 			explain: "",
       correct: true,
 		},		
 	]}
 />
 
-### Q2: Which of the following statements are correct?
+### Q2: Which of the following statements are True, when talking about models with bias and/or variance in RL?
 <Question
 	choices={[
 		{
-			text: "A biased reward signal returns rewards similar to the real / expected ones from the environment",
+			text: "An unbiased reward signal returns rewards similar to the real / expected ones from the environment",
 			explain: "",
       correct: true,
 		},
     {
-			text: "An unbiased reward signal returns rewards similar to the real / expected ones from the environment",
-			explain: "If a reward signal is unbiased, it means the reward signal we get is similar to the real reward we should be getting from an environment",
+			text: "A biased reward signal returns rewards similar to the real / expected ones from the environment",
+			explain: "If a reward signal is biased, it means the reward signal we get differs from the real reward we should be getting from an environment",
       correct: false,
 		},
      ,
@@ -41,8 +41,8 @@ The best way to learn and [to avoid the illusion of competence](https://www.cour
 		},		
     {
 			text: "A reward signal with low variance has much noise in it and gets affected by, for example, stochastic (non constant) elements in the environment"
-			explain: "If a reward signal has low variance, then it's less affected by the noise of the environment, as elements appearing randomly in the trajectory",
-      correct: true,
+			explain: "If a reward signal has low variance, then it's less affected by the noise of the environment and produce similar values regardless the random elements in the environment",
+      correct: false,
 		},
 	]}
 />
@@ -63,7 +63,7 @@ The best way to learn and [to avoid the illusion of competence](https://www.cour
 		},
      ,
     {
-			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 can take `n` strategies and average them, reducing their impact impact in case of noise"
 			explain: "",
       correct: true,
 		},		    
@@ -74,9 +74,9 @@ The best way to learn and [to avoid the illusion of competence](https://www.cour
 <details>
 <summary>Solution</summary>
 
-The idea behind Actor-Critic is the following - we learn two function approximations:
-1. A policy that controls how our agent acts (π)
-2. A value function to assist the policy update by measuring how good the action taken is (q)
+The idea behind Actor-Critic is that we learn two function approximations:
+1. A `policy` that controls how our agent acts (π)
+2. A `value` function to assist the policy update by measuring how good the action taken is (q)
 
 <img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit8/step2.jpg" alt="Actor-Critic, step 2"/>
 
@@ -97,7 +97,7 @@ The idea behind Actor-Critic is the following - we learn two function approximat
 		},
     {
 			text: "It adds resistance to stochasticity and reduces high variance",
-			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: "",
       correct: true,
 		},	    
 	]}
@@ -105,11 +105,12 @@ The idea behind Actor-Critic is the following - we learn two function approximat
 
 
 
-### Q6: What is Advantege in the A2C method?
+### Q6: What is `Advantege` in the A2C method?
 <details>
 <summary>Solution</summary>
 
-Instead of using directly the Action-Value function of the Critic as it is, we calculate an Advantage function,  the relative advantage of an action compared to the others possible at a state.
+Instead of using directly the Action-Value function of the Critic as it is, we could use an `Advantage` function. The idea behind an `Advantage` function is that we calculate the relative advantage of an action compared to the others possible at a state, averaging them.
+
 In other words: how taking that action at a state is better compared to the average value of the state
 
 <img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit8/advantage1.jpg" alt="Advantage in A2C"/>

From 306d4084c21c959f3997c3857a88da90a2628b94 Mon Sep 17 00:00:00 2001
From: Juan Martinez <36634572+josejuanmartinez@users.noreply.github.com>
Date: Mon, 4 Dec 2023 16:32:22 +0000
Subject: [PATCH 3/8] Update _toctree.yml

---
 units/en/_toctree.yml | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/units/en/_toctree.yml b/units/en/_toctree.yml
index 704be13..cc96faf 100644
--- a/units/en/_toctree.yml
+++ b/units/en/_toctree.yml
@@ -160,6 +160,8 @@
     title: Advantage Actor Critic (A2C)
   - local: unit6/hands-on
     title: Advantage Actor Critic (A2C) using Robotics Simulations with Panda-Gym 🤖
+  - local: unit6/quiz
+    title: Quiz
   - local: unit6/conclusion
     title: Conclusion
   - local: unit6/additional-readings

From a5d8d6badb0d585a135dc6de32bce74c9f5bdf7e Mon Sep 17 00:00:00 2001
From: Juan Martinez <36634572+josejuanmartinez@users.noreply.github.com>
Date: Mon, 4 Dec 2023 17:27:47 +0000
Subject: [PATCH 4/8] Create quiz for unit 5

---
 units/en/unit5/quiz.mdx | 87 +++++++++++++++++++++++++++++++++++++++++
 1 file changed, 87 insertions(+)
 create mode 100644 units/en/unit5/quiz.mdx

diff --git a/units/en/unit5/quiz.mdx b/units/en/unit5/quiz.mdx
new file mode 100644
index 0000000..badef49
--- /dev/null
+++ b/units/en/unit5/quiz.mdx
@@ -0,0 +1,87 @@
+# 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: What of the following statemets are true about Unity ML-Agents?
+
+<Question
+	choices={[
+		{
+			text: "It allows you to create learning environments from the Unity ´Scene´ objects",
+			explain: "",
+      correct: true,
+		},
+		{
+			text: "It 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 the Python back-end",
+			explain: "",
+      correct: true,
+		},
+    {
+			text: "The library which carries out the actual training is 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: "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"
+		},
+	]}
+/>
+
+### Q2: Explain with your own words what is the role of the `Academy`.
+
+<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"/>
+
+</details>
+
+
+### Q3: What 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.",
+      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",
+      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",
+      explain: "",
+      correct: true,
+    },
+	]}
+/>
+
+
+### Q4: Name several input variables which were used in any of 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.
+</details>
+
+
+Congrats on finishing this Quiz 🥳, if you missed some elements, take time to read the chapter again to reinforce (😏) your knowledge.

From 9614d3d51b428653e09067fc15a3cef4885865cf Mon Sep 17 00:00:00 2001
From: Juan Martinez <36634572+josejuanmartinez@users.noreply.github.com>
Date: Mon, 4 Dec 2023 17:28:43 +0000
Subject: [PATCH 5/8] Delete units/en/unit5/quiz.mdx

---
 units/en/unit5/quiz.mdx | 87 -----------------------------------------
 1 file changed, 87 deletions(-)
 delete mode 100644 units/en/unit5/quiz.mdx

diff --git a/units/en/unit5/quiz.mdx b/units/en/unit5/quiz.mdx
deleted file mode 100644
index badef49..0000000
--- a/units/en/unit5/quiz.mdx
+++ /dev/null
@@ -1,87 +0,0 @@
-# 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: What of the following statemets are true about Unity ML-Agents?
-
-<Question
-	choices={[
-		{
-			text: "It allows you to create learning environments from the Unity ´Scene´ objects",
-			explain: "",
-      correct: true,
-		},
-		{
-			text: "It 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 the Python back-end",
-			explain: "",
-      correct: true,
-		},
-    {
-			text: "The library which carries out the actual training is 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: "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"
-		},
-	]}
-/>
-
-### Q2: Explain with your own words what is the role of the `Academy`.
-
-<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"/>
-
-</details>
-
-
-### Q3: What 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.",
-      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",
-      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",
-      explain: "",
-      correct: true,
-    },
-	]}
-/>
-
-
-### Q4: Name several input variables which were used in any of 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.
-</details>
-
-
-Congrats on finishing this Quiz 🥳, if you missed some elements, take time to read the chapter again to reinforce (😏) your knowledge.

From 40cf7684e51765267c7c6f20f7da8b9858769780 Mon Sep 17 00:00:00 2001
From: Juan Martinez <36634572+josejuanmartinez@users.noreply.github.com>
Date: Wed, 6 Dec 2023 11:10:43 +0000
Subject: [PATCH 6/8] Fixes typo and comma(s)

---
 units/en/unit6/quiz.mdx | 44 ++++++++++++++++++++---------------------
 1 file changed, 21 insertions(+), 23 deletions(-)

diff --git a/units/en/unit6/quiz.mdx b/units/en/unit6/quiz.mdx
index b53a5ef..0fc9b38 100644
--- a/units/en/unit6/quiz.mdx
+++ b/units/en/unit6/quiz.mdx
@@ -10,12 +10,12 @@ The best way to learn and [to avoid the illusion of competence](https://www.cour
 		{
 			text: "The bias-variance tradeoff reflects how my model is able to generalize the knowledge to previously tagged data we give to the model during training time.",
 			explain: "This is the traditional bias-variance tradeoff in Machine Learning. In our specific case of Reinforcement Learning, we don't have previously tagged data, but only a reward signal.",
-      correct: false,
+      			correct: false,
 		},
-    {
+   		{
 			text: "The bias-variance tradeoff reflects how well the reinforcement signal reflects the true reward the agent should get from the enviromment",
 			explain: "",
-      correct: true,
+      			correct: true,
 		},		
 	]}
 />
@@ -26,23 +26,22 @@ The best way to learn and [to avoid the illusion of competence](https://www.cour
 		{
 			text: "An unbiased reward signal returns rewards similar to the real / expected ones from the environment",
 			explain: "",
-      correct: true,
+      			correct: true,
 		},
-    {
+    		{
 			text: "A biased reward signal returns rewards similar to the real / expected ones from the environment",
 			explain: "If a reward signal is biased, it means the reward signal we get differs from the real reward we should be getting from an environment",
-      correct: false,
+      			correct: false,
 		},
-     ,
-    {
+    		{
 			text: "A reward signal with high variance has much noise in it and gets affected by, for example, stochastic (non constant) elements in the environment"
 			explain: "",
-      correct: true,
+      			correct: true,
 		},		
-    {
+    		{
 			text: "A reward signal with low variance has much noise in it and gets affected by, for example, stochastic (non constant) elements in the environment"
 			explain: "If a reward signal has low variance, then it's less affected by the noise of the environment and produce similar values regardless the random elements in the environment",
-      correct: false,
+      			correct: false,
 		},
 	]}
 />
@@ -54,18 +53,17 @@ The best way to learn and [to avoid the illusion of competence](https://www.cour
 		{
 			text: "It's a sampling mechanism, which means we don't consider analyze all the possible states, but a sample of those",
 			explain: "",
-      correct: true,
+      			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",
-      correct: false,
+      			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"
 			explain: "",
-      correct: true,
+			correct: true,
 		},		    
 	]}
 />
@@ -85,27 +83,27 @@ The idea behind Actor-Critic is that we learn two function approximations:
 ### Q5: Which of the following statemets are True about the Actor-Critic Method?
 <Question
 	choices={[
-    {
+   		 {
 			text: "The Critic does not learn from the training process",
 			explain: "Both the Actor and the Critic function parameters are updated during training time",
-      correct: false,
+      			correct: false,
 		},
 		{
 			text: "The Actor learns a policy function, while the Critic learns a value function",
 			explain: "",
-      correct: true,
+      			correct: true,
 		},
-    {
+    		{
 			text: "It adds resistance to stochasticity and reduces high variance",
 			explain: "",
-      correct: true,
+      			correct: true,
 		},	    
 	]}
 />
 
 
 
-### Q6: What is `Advantege` in the A2C method?
+### Q6: What is `Advantage` in the A2C method?
 <details>
 <summary>Solution</summary>
 

From f7c510a063f9fb68f85fcafb60e6e42b9870b42f Mon Sep 17 00:00:00 2001
From: Juan Martinez <36634572+josejuanmartinez@users.noreply.github.com>
Date: Wed, 6 Dec 2023 11:12:38 +0000
Subject: [PATCH 7/8] Adds newline after ###

---
 units/en/unit6/quiz.mdx | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/units/en/unit6/quiz.mdx b/units/en/unit6/quiz.mdx
index 0fc9b38..2a5797e 100644
--- a/units/en/unit6/quiz.mdx
+++ b/units/en/unit6/quiz.mdx
@@ -21,6 +21,7 @@ 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?
+
 <Question
 	choices={[
 		{
@@ -48,6 +49,7 @@ 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?
+
 <Question
 	choices={[
 		{
@@ -69,6 +71,7 @@ The best way to learn and [to avoid the illusion of competence](https://www.cour
 />
 
 ### Q4: What is the Advanced Actor-Critic Method (A2C)?
+
 <details>
 <summary>Solution</summary>
 
@@ -81,6 +84,7 @@ 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?
+
 <Question
 	choices={[
    		 {
@@ -104,6 +108,7 @@ The idea behind Actor-Critic is that we learn two function approximations:
 
 
 ### Q6: What is `Advantage` in the A2C method?
+
 <details>
 <summary>Solution</summary>
 

From f41bf2c5fb50a5200d63a68250586ad568dfa45c Mon Sep 17 00:00:00 2001
From: Juan Martinez <36634572+josejuanmartinez@users.noreply.github.com>
Date: Wed, 6 Dec 2023 11:36:29 +0000
Subject: [PATCH 8/8] Fixes missing commas

---
 units/en/unit6/quiz.mdx | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/units/en/unit6/quiz.mdx b/units/en/unit6/quiz.mdx
index 2a5797e..0c49305 100644
--- a/units/en/unit6/quiz.mdx
+++ b/units/en/unit6/quiz.mdx
@@ -35,12 +35,12 @@ The best way to learn and [to avoid the illusion of competence](https://www.cour
       			correct: false,
 		},
     		{
-			text: "A reward signal with high variance has much noise in it and gets affected by, for example, stochastic (non constant) elements in the environment"
+			text: "A reward signal with high variance has much noise in it and gets affected by, for example, stochastic (non constant) elements in the environment",
 			explain: "",
       			correct: true,
 		},		
     		{
-			text: "A reward signal with low variance has much noise in it and gets affected by, for example, stochastic (non constant) elements in the environment"
+			text: "A reward signal with low variance has much noise in it and gets affected by, for example, stochastic (non constant) elements in the environment",
 			explain: "If a reward signal has low variance, then it's less affected by the noise of the environment and produce similar values regardless the random elements in the environment",
       			correct: false,
 		},
@@ -63,7 +63,7 @@ The best way to learn and [to avoid the illusion of competence](https://www.cour
       			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 can take `n` strategies and average them, reducing their impact impact in case of noise",
 			explain: "",
 			correct: true,
 		},