# 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.


<EditOnGithub source="https://github.com/huggingface/deep-rl-class/blob/main/units/en/unit7/quiz.mdx" />