08-12-2023, 07:19 PM
Last updated 1/2023
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 8.63 GB | Duration: 9h 57m
Teach airplanes to fly with Unity's Reinforcement Learning platform
What you'll learn
Learn how to install, run, and train neural networks with Unity ML-Agents
Train airplane agents to fly with Reinforcement Learning, specifically PPO
Create a full, playable airplane racing game in Unity with incredibly challenging AI opponents
Integrate trained neural networks in a game that can be built and deployed cross-platform
Utilize Machine Learning at a high level (no need to write training algorithms)
Lots of opportunities to customize the project and make it your own
Requirements
Intermediate programming skills (Unity uses C#)
A computer that can run Unity 2019.2 or above
Basic Unity skills (how to navigate and use the interface)
Optional: Basic Blender skills (how to navigate and use the interface)
Optional: Prior Machine Learning experience - It will help with understanding, but isn't necessary
Description
Interested in the intersection of video games and artificial intelligence? If so, you will love Unity ML-Agents.Reinforcement Learning with ML-Agents is naturally more intuitive than other machine learning approaches because you can watch your neural network learn in a real-time 3d environment based on rewards for good behavior. It's more fun because you can easily apply it to your own video game ideas rather than working with simplified example problems in a library like OpenAI Gym.In this course, we will create a complete game with incredibly challenging AI opponents.We'll start with an introduction to ML-Agents, including how to use and train the example content.Then, we'll use Blender to make custom assets for our game (you can skip that part if you just want to code).Next, we'll create a full environment for the airplane agents and train them to fly through checkpoints without crashing into obstacles.Finally, we'll take our trained agents and build a full game around them that you can play, including menus for level and difficulty selection.Important note 1: We DO NOT cover the foundations of deep learning or reinforcement learning in this course. We will focus on how to use ML-Agents, which abstracts the hard stuff and allows us to focus on building our training environment and crafting rewards.Important note 2: While the course was originally recorded with ML-Agents version 0.11, we have updated it for version 1.0.As you work through the course, you'll have plenty of opportunities to customize it and make it your own. At the end, you'll have a complete game that you can share with friends, add to your portfolio, or sell on a game marketplace.
Overview
Section 1: COURSE UPDATES
Lecture 1 ❗❗❗COURSE UPDATE - Unity ML-Agents v1.0
Lecture 2 A Note on Newer Versions
Lecture 3 1080p Videos
Section 2: [v1.0] Unity ML-Agents - Introduction & Setup
Lecture 4 Unity Hub & Anaconda
Lecture 5 New Project, Install ML-Agents, & Import Examples
Lecture 6 3D Ball Example
Lecture 7 Training 3D Ball Example
Section 3: [v1.0 & v0.11] Asset Creation in Blender
Lecture 8 Introduction
Lecture 9 Important Resources & Assets
Lecture 10 Low-Poly Terrain: Setup
Lecture 11 Low-Poly Terrain: Subdivide, Triangulate, & Sculpt
Lecture 12 Low-Poly Terrain: Sculpt the Racetrack
Lecture 13 Low-Poly Rocks: Medium Rock
Lecture 14 Low-Poly Rocks: Tall & Flat Rocks
Lecture 15 Low-Poly Rocks: Colliders
Lecture 16 Low-Poly Rocks: Export
Lecture 17 Checkpoint: First Piece
Lecture 18 Checkpoint: More Pieces, Collider, & Export
Lecture 19 Finish Line Checkpoint
Lecture 20 Low-Poly Airplane: Reference Images
Lecture 21 Low-Poly Airplane: Body (Part 1)
Lecture 22 Low-Poly Airplane: Body (Part 2)
Lecture 23 Low-Poly Airplane: Body (Part 3)
Lecture 24 Low-Poly Airplane: Wings
Lecture 25 Low-Poly Airplane: Horizontal Stabilizer
Lecture 26 Low-Poly Airplane: Landing Gear
Lecture 27 Low-Poly Airplane: Propeller
Lecture 28 Low-Poly Airplane: Export
Section 4: [v1.0] Project Setup & ML-Agents Installation
Lecture 29 ML-Agents v1.0 Note
Lecture 30 Important Resources & Assets
Lecture 31 New Project
Lecture 32 Project Clean-up & Install ML-Agents
Section 5: [v1.0 & v0.11] Desert Race Path Creation
Lecture 33 Import 3D Meshes
Lecture 34 Desert Terrain & Rock Prefabs
Lecture 35 [v0.11] Airplane Prefab
Lecture 36 [v1.0] Airplane Prefab
Lecture 37 Checkpoint Prefabs
Lecture 38 Desert Area Prefab
Lecture 39 Placing Rocks in the Desert Environment
Lecture 40 Complete Rock Placement
Lecture 41 Race Path
Lecture 42 Complete Race Path
Lecture 43 Boundaries
Section 6: [v1.0] Aircraft Area
Lecture 44 Variables
Lecture 45 Awake() & Start()
Lecture 46 ResetAgentPosition()
Lecture 47 Race Condition Fix
Lecture 48 AircraftArea Prefab Setup
Lecture 49 Rotate.cs
Section 7: [v1.0] Aircraft Agent & Aircraft Player
Lecture 50 AircraftAgent.cs: Variables
Lecture 51 AircraftAgent.cs: Initialize()
Lecture 52 AircraftAgent.cs: OnActionReceived()
Lecture 53 AircraftAgent.cs: ProcessMovement()
Lecture 54 AircraftPlayer.cs: Input System & Variables
Lecture 55 AircraftPlayer.cs: Heuristic()
Lecture 56 AircraftPlayer.cs: Airplane Prefab Setup
Lecture 57 AircraftPlayer: Input Bindings
Lecture 58 AircraftPlayer.cs: Follow Camera, DecisionRequester, & Test Flight
Lecture 59 AircraftAgent.cs: More Variables
Lecture 60 AircraftAgent.cs: Training Logic in Initialize() & OnActionReceived()
Lecture 61 AircraftAgent.cs: VectorToNextCheckpoint()
Lecture 62 AircraftAgent.cs: GotCheckpoint()
Lecture 63 AircraftAgent.cs: OnEpisodeBegin()
Lecture 64 AircraftAgent.cs: Freeze() & Thaw()
Lecture 65 AircraftAgent.cs: OnTriggerEnter()
Lecture 66 AircraftAgent.cs: OnCollisionEnter & ExplosionReset()
Lecture 67 AircraftAgent.cs: CollectObservations() & Heuristic()
Lecture 68 AircraftAgent: RayPerceptionSensor3D & AircraftAgent Components
Lecture 69 AircraftAgent: Behavior Parameters, AircraftAgent, & Explosion
Section 8: [v1.0] Training the Aircraft Agents
Lecture 70 New Training Scene
Lecture 71 Config Files
Lecture 72 Start Training
Lecture 73 Tensorboard & Training Progress
Lecture 74 Training Results
Lecture 75 Introducing Randomness
Lecture 76 Race Your ML-Agents
Section 9: [v1.0 & v0.11] Game Logic & Menus
Lecture 77 Introduction
Lecture 78 GameManager.cs: Enums & StateChangeHandler
Lecture 79 GameManager.cs: Accessors & Singleton Logic
Lecture 80 GameManager.cs: Level Loading Logic
Lecture 81 Main Menu Scene + Layout
Lecture 82 Main Menu Content
Lecture 83 MainMenuController.cs: Variables & Dropdown Lists
Lecture 84 MainMenuController.cs: Interaction Logic
Lecture 85 MainMenuController.cs: Hook Up UI, Build Manager, & Game Manager
Lecture 86 Create RaceManager & Other UI Scripts
Lecture 87 RaceManager.cs: Variables & Hook Ups
Lecture 88 RaceManager.cs: More Variables & Accessors
Lecture 89 RaceManager.cs: Awake() & Start()
Lecture 90 RaceManager.cs: Start() Continued
Lecture 91 RaceManager.cs: OnStateChange()
Lecture 92 RaceManager.cs: StartRace()
Lecture 93 RaceManager.cs: PauseInputPerformed()
Lecture 94 RaceManager.cs: FixedUpdate()
Lecture 95 RaceManager.cs: PlaceComparer()
Lecture 96 RaceManager.cs: GetAgentCheckpoint()
Lecture 97 RaceManager.cs: OnDestroy(), GetAgentLap(), GetAgentPlace()
Lecture 98 RaceManager.cs: GetAgentTime()
Lecture 99 Heads Up Display UI
Lecture 100 HUD Checkpoint Indicator
Lecture 101 HUDController.cs: Variables
Lecture 102 HUDController.cs: UI Update Functions
Lecture 103 HUDController.cs: Hook Up to UI
Lecture 104 Pause Menu
Lecture 105 PauseMenuController.cs
Lecture 106 Countdown UI & CountdownUIController.cs
Lecture 107 Gameover Screen & GameoverUI.cs
Lecture 108 Play Testing
Lecture 109 Pause Button Bugfix
Lecture 110 RaceManager Prefab
Lecture 111 Adding Randomness
Lecture 112 Post Processing
Lecture 113 Making the Main Menu More Interesting
Lecture 114 Creating a Snow Scene
Lecture 115 Flying in Snow
Lecture 116 Adding Water, Rocks, & Updating the Race Path
Lecture 117 Complete Snow Level & Add Snow to Levels List
Lecture 118 Fixing Snow Level Loading Bug
Lecture 119 Build & Play Complete Game
Intermediate software developers with an interest in AI in the Unity3d Game Engine,Developers that want to use Reinforcement Learning, but don't need to know the low level details,Game developers interested in adding neural network AI to their games
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