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Deep Learning for Games

Welcome to Hands-on Deep Learning for Games. This book is for anyone wanting an extremely practical approach to the complexity of deep learning (DL) for games. Importantly, the concepts discussed in this book aren't solely limited to games. Much of what we'll learn here will easily carry over to other applications/simulations.

Reinforcement learning (RL), which will be a core element we talk about in later chapters, is quickly becoming the dominant machine learning (ML) technology. It has been applied to everything from server optimization to predicting customer activity for retail markets. Our journey in this book will primarily be focused on game development, and our goal will be to build a working adventure game. Keep in the back of your mind how the same principles you discover in this book could be applied to other problems, such as simulations...

Key benefits

Apply the power of deep learning to complex reasoning tasks by building a Game AI

Exploit the most recent developments in machine learning and AI for building smart games

Implement deep learning models and neural networks with Python

Description

The number of applications of deep learning and neural networks has multiplied in the last couple of years. Neural nets has enabled significant breakthroughs in everything from computer vision, voice generation, voice recognition and self-driving cars. Game development is also a key area where these techniques are being applied. This book will give an in depth view of the potential of deep learning and neural networks in game development. We will take a look at the foundations of multi-layer perceptron’s to using convolutional and recurrent networks. In applications from GANs that create music or textures to self-driving cars and chatbots. Then we introduce deep reinforcement learning through the multi-armed bandit problem and other OpenAI Gym environments. As we progress through the book we will gain insights about DRL techniques such as Motivated Reinforcement Learning with Curiosity and Curriculum Learning. We also take a closer look at deep reinforcement learning and in particular the Unity ML-Agents toolkit. By the end of the book, we will look at how to apply DRL and the ML-Agents toolkit to enhance, test and automate your games or simulations. Finally, we will cover your possible next steps and possible areas for future learning.

Who is this book for?

This books is for game developers who wish to create highly interactive games by leveraging the power of machine and deep learning. No prior knowledge of machine learning, deep learning or neural networks is required this book will teach those concepts from scratch. A good understanding of Python is required.

What you will learn

Learn the foundations of neural networks and deep learning.

Use advanced neural network architectures in applications to create music, textures, self driving cars and chatbots.

Understand the basics of reinforcement and DRL and how to apply it to solve a variety of problems.

Working with Unity ML-Agents toolkit and how to install, setup and run the kit.

Understand core concepts of DRL and the differences between discrete and continuous action environments.

Use several advanced forms of learning in various scenarios from developing agents to testing games.

What do you get with Print?

Instant access to your digital eBook copy whilst your Print order is Shipped

Paperback book shipped to your preferred address

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

AI Assistant (beta) to help accelerate your learning

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Hands-On Reinforcement Learning for Games

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Total $ 126.97

Sharky Nov 08, 2019

This book is the best way to get started with Unity ML agents. It has loads of examples. You will need to be run the example programs do some experiments of your own. I hope more books like this get published soon.

Amazon Verified review

N8tn Oct 09, 2019

The narrative of this book holds great promises but unfortunately falls way short of it. The content is not very well written with very minimal explanation of anything. It packs with a lot of Python code that seems like it's coming straight out of the author's computer screen (with some obvious typos, suggesting poor proof-reading on the publisher's part). I gave it a shot because of the promise, but sadly will not recommend to anyone.

Hands-On Deep Learning for Games: Leverage the power of neural networks and reinforcement learning to build intelligent games

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Hands-On Deep Learning for Games

Deep Learning for Games

The past, present, and future of DL

The past

Neural networks – the foundation

Multilayer perceptron in TF

TensorFlow Basics

Training neural networks with backpropagation

Building an autoencoder with Keras

Exercises

Summary

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