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Unit 5 quiz and some rewording for Unit 6
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| # Quiz | ||
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| 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**. | ||
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| ### Q1: Which of the following tools are specifically designed for video games development? | ||
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| <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, | ||
| }, | ||
| ]} | ||
| /> | ||
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| ### Q2: What of the following statements are true about Unity ML-Agents? | ||
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| <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, | ||
| }, | ||
| ]} | ||
| /> | ||
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| ### Q3: Fill the missing letters | ||
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| - 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 _ | ||
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| <details> | ||
| <summary>Solution</summary> | ||
| - b r a i n | ||
| - a c a d e m y | ||
| </details> | ||
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| ### Q4: Define with your own words what is a `raycast` | ||
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| <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> | ||
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| ### Q5: Which are the differences between capturing the environment using `frames` or `raycasts`? | ||
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| <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, | ||
| }, | ||
| ]} | ||
| /> | ||
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| ### Q6: Name several environment and agent input variables used to train the agent in the Snowball or Pyramid environments | ||
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| <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> | ||
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| Congrats on finishing this Quiz 🥳, if you missed some elements, take time to read the chapter again to reinforce (😏) your knowledge. |
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