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Hands-On Deep Learning for Games
Hands-On Deep Learning for Games

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

Section 1: The Basics

This section of the book covers the basic concepts of neural networks and deep learning. We'll be looking at everything from the simplest autoencoder, generative adversarial networks (GANs), and convolutional and recurrent neural networks, all the way to building a working real-world chatbot. This section will give you the basic foundations for building your neural network and deep learning knowledge.

We will include the following chapters in this section:

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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.
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Publication date, Length, Edition, Language, ISBN-13
Publication date : Mar 30, 2019
Length: 392 pages
Edition : 1st
Language : English
ISBN-13 : 9781788994071
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Product Details

Publication date : Mar 30, 2019
Length: 392 pages
Edition : 1st
Language : English
ISBN-13 : 9781788994071
Category :
Languages :
Concepts :
Tools :

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Table of Contents

17 Chapters
Section 1: The Basics Chevron down icon Chevron up icon
Deep Learning for Games Chevron down icon Chevron up icon
Convolutional and Recurrent Networks Chevron down icon Chevron up icon
GAN for Games Chevron down icon Chevron up icon
Building a Deep Learning Gaming Chatbot Chevron down icon Chevron up icon
Section 2: Deep Reinforcement Learning Chevron down icon Chevron up icon
Introducing DRL Chevron down icon Chevron up icon
Unity ML-Agents Chevron down icon Chevron up icon
Agent and the Environment Chevron down icon Chevron up icon
Understanding PPO Chevron down icon Chevron up icon
Rewards and Reinforcement Learning Chevron down icon Chevron up icon
Imitation and Transfer Learning Chevron down icon Chevron up icon
Building Multi-Agent Environments Chevron down icon Chevron up icon
Section 3: Building Games Chevron down icon Chevron up icon
Debugging/Testing a Game with DRL Chevron down icon Chevron up icon
Obstacle Tower Challenge and Beyond Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3
(2 Ratings)
5 star 50%
4 star 0%
3 star 0%
2 star 0%
1 star 50%
Sharky Nov 08, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
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 Amazon
N8tn Oct 09, 2019
Full star icon Empty star icon Empty star icon Empty star icon Empty star icon 1
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.
Amazon Verified review Amazon
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Disclaimer:
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