Unit 7 quiz

units/en/_toctree.yml

@@ -180,6 +180,8 @@
     title: Self-Play
   - local: unit7/hands-on
     title: Let's train our soccer team to beat your classmates' teams (AI vs. AI)
+  - local: unit7/quiz
+    title: Quiz
   - local: unit7/conclusion
     title: Conclusion
   - local: unit7/additional-readings

units/en/unit7/quiz.mdx

@@ -0,0 +1,139 @@
+# 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: Chose the option which fits better when comparing different types of multi-agent environments
+
+- Your agents aim to maximize common benefits in ____ environments
+- Your agents aim to maximize common benefits while minimizing opponent's in ____ environments
+
+<Question
+	choices={[
+		{
+			text: "competitive, cooperative",
+			explain: "You maximize common benefit in cooperative, while in competitive you also aim to reduce opponent's score",
+   			correct: false,
+		},
+   		{
+			text: "cooperative, competitive",
+			explain: "",
+   			correct: true,
+		},
+	]}
+/>
+
+### Q2: Which of the following statements are true about `decentralized` learning?
+
+<Question
+	choices={[
+		{
+			text: "Each agent is trained independently from the others",
+			explain: "",
+      		correct: true,
+		},
+    	{
+			text: "Inputs from other agents are just considered environment data",
+			explain: "",
+			correct: true,
+		},
+		{
+			text: "Considering other agents part of the environment makes the environment stationary",
+			explain: "In decentralized learning, agents ignore the existence of other agents and consider them part of the environment. However, this means the environment is in constant change, becoming non-stationary.",
+			correct: false,
+		},
+	]}
+/>
+
+
+### Q3: Which of the following statements are true about `centralized` learning?
+
+<Question
+	choices={[
+		{
+			text: "It learns one common policy based on the learnings from all agents' interactions",
+			explain: "",
+      		correct: true,
+		},
+    	{
+			text: "The reward is global",
+			explain: "",
+			correct: true,
+		},
+		{
+			text: "The environment with this approach is stationary",
+			explain: "",
+			correct: true,
+		},
+	]}
+/>
+
+### Q4: Explain in your own words what is the `Self-Play` approach
+
+<details>
+<summary>Solution</summary>
+
+`Self-play` is an approach to instantiate copies of agents with the same policy as your as opponents, so that your agent learns from agents with same training level.
+
+</details>
+
+### Q5: When configuring `Self-play`, several parameters are important. Could you identify, by their definition, which parameter are we talking about?
+
+- The probability of playing against the current self vs an opponent from a pool
+- Variety (dispersion) of training levels of the opponents you can face
+- The number of training steps before spawning a new opponent
+- Opponent change rate
+
+<Question
+	choices={[
+   		 {
+			text: "window, play_against_latest_model_ratio, save_steps, swap_steps+team_change",
+			explain: "",
+      		correct: false,
+		},
+		{
+			text: "play_against_latest_model_ratio, save_steps, window, swap_steps+team_change",
+			explain: "",
+			correct: false,
+		},
+		{
+			text: "play_against_latest_model_ratio, window, save_steps, swap_steps+team_change",
+			explain: "",
+			correct: true,
+		},
+    	{
+			text: "swap_steps+team_change, save_steps, play_against_latest_model_ratio, window",
+			explain: "",
+      		correct: false,
+		},
+	]}
+/>
+
+### Q6: What are the main motivations to use a ELO rating Score?
+
+<Question
+	choices={[
+   		 {
+			text: "The score takes into account the different of skills between you and your opponent",
+			explain: "",
+      		correct: true,
+		},
+		{
+			text: "Although more points can be exchanged depending on the result of the match and given the levels of the agents, the sum is always the same",
+			explain: "",
+			correct: true,
+		},
+		{
+			text: "It's easy for an agent to keep a high score rate",
+			explain: "That is called the `Rating deflation`: keeping a high rate requires much skill over time",
+			correct: false,
+		},
+    	{
+			text: "It works well calculating the individual contributions of each player in a team",
+			explain: "ELO uses the score achieved by the whole team, but individual contributions are not calculated",
+      		correct: false,
+		},
+	]}
+/>
+
+Congrats on finishing this Quiz 🥳, if you missed some elements, take time to read the chapter again to reinforce (😏) your knowledge.