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] Delete units/en/unit5/quiz.mdx

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-# 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.