This is the code repository for Learn Unity ML-Agents – Fundamentals of Unity Machine Learning, published by Packt.
Incorporate new powerful ML algorithms such as Deep Reinforcement Learning for games
Unity Machine Learning agents allow researchers and developers to create games and simulations using the Unity Editor, which serves as an environment where intelligent agents can be trained with machine learning methods through a simple-to-use Python API.
This book covers the following exciting features: <First 5 What you'll learn points>
- Develop Reinforcement and Deep Reinforcement Learning for games.
- Understand complex and advanced concepts of reinforcement learning and neural networks
- Explore various training strategies for cooperative and competitive agent development
- Adapt the basic script components of Academy, Agent, and Brain to be used with Q Learning.
- Enhance the Q Learning model with improved training strategies such as Greedy-Epsilon exploration
If you feel this book is for you, get your copy today!
All of the code is organized into folders. For example, Chapter02.
The code will look like the following:
void Defend()
{
currentAction = "Defend";
nextAction = Time.timeSinceLevelLoad + (25 / MaxSpeed);
}
Following is what you need for this book: This book is intended for developers with an interest in using Machine learning algorithms to develop better games and simulations with Unity. The reader will be required to have a working knowledge of C# and a basic understanding of Python.
With the following software and hardware list you can run all code files present in the book (Chapter 1-6).
| Chapter | Software required | OS required |
|---|---|---|
| 1 | Unity and ML-Agents | Windows, Mac OS X, and Linux (Any) |
| 3 | Python (Anaconda) | Windows, Mac OS X, and Linux (Any) |
We also provide a PDF file that has color images of the screenshots/diagrams used in this book. Click here to download it.
Micheal Lanham Micheal Lanham is a proven software architect with 20 years' experience of developing a range of software, including games, mobile, graphic, web, desktop, engineering, GIS, and machine learning applications for various industries. In 2000, Micheal began working with machine learning and would later use various technologies for a broad range of apps, from geomechanics to inspecting pipelines in 3D. He was later introduced to Unity and has been an avid developer and author of multiple Unity apps and books since.
- Augmented Reality Game Development
- Game Audio Development with Unity 5.X
- Learn ARCore - Fundamentals of Google ARCore
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