Book Image

Hands-On Deep Learning for Games

By : Micheal Lanham
Book Image

Hands-On Deep Learning for Games

By: Micheal Lanham

Overview of this book

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.

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Table of Contents (18 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
Free Chapter
1
Section 1: The Basics
Section 1: The Basics
2
Deep Learning for Games
Deep Learning for Games
The past, present, and future of DL
Neural networks – the foundation
Multilayer perceptron in TF
TensorFlow Basics
Training neural networks with backpropagation
Building an autoencoder with Keras
Exercises
Summary
3
Convolutional and Recurrent Networks
Convolutional and Recurrent Networks
Convolutional neural networks
Understanding convolution
Building a self-driving CNN
Memory and recurrent networks
Playing Rock, Paper, Scissors with LSTMs
Exercises
Summary
4
GAN for Games
GAN for Games
Introducing GANs
Coding a GAN in Keras
Wasserstein GAN
Generating textures with a GAN
A GAN for creating music
Exercises
Summary
5
Building a Deep Learning Gaming Chatbot
Building a Deep Learning Gaming Chatbot
Neural conversational agents
Sequence-to-sequence learning
DeepPavlov
Building the chatbot server
Running the chatbot in Unity
Exercises
Summary
6
Section 2: Deep Reinforcement Learning
Section 2: Deep Reinforcement Learning
7
Introducing DRL
Introducing DRL
Reinforcement learning
RL with the OpenAI Gym
A Q-Learning model
First DRL with Deep Q-learning
RL experiments
Exercises
Summary
8
Unity ML-Agents
Unity ML-Agents
Installing ML-Agents
Training an agent
What's in a brain?
Monitoring training with TensorBoard
Running an agent
Exercises
Summary
9
Agent and the Environment
Agent and the Environment
Exploring the training environment
Understanding state
Understanding visual state
Convolution and visual state
Recurrent networks for remembering series
Exercises
Summary
10
Understanding PPO
Understanding PPO
Marathon RL
The partially observable Markov decision process
Actor-Critic and continuous action spaces
Understanding TRPO and PPO
Learning to tune PPO
Exercises
Summary
11
Rewards and Reinforcement Learning
Rewards and Reinforcement Learning
Rewards and reward functions
Sparsity of rewards
Curriculum Learning
Understanding Backplay
Curiosity Learning
Exercises
Summary
12
Imitation and Transfer Learning
Imitation and Transfer Learning
IL, or behavioral cloning
Online training
Offline training
Transfer learning
Imitation Transfer Learning
Exercises
Summary
13
Building Multi-Agent Environments
Building Multi-Agent Environments
Adversarial and cooperative self-play
Adversarial self-play
Multi-brain play
Adding individuality with intrinsic rewards
Extrinsic rewards for individuality
Exercises
Summary
14
Section 3: Building Games
Section 3: Building Games
15
Debugging/Testing a Game with DRL
Debugging/Testing a Game with DRL
Introducing the game
Setting up ML-Agents
Overriding the Unity input system
Testing through imitation
Analyzing the testing process
Exercises
Summary
16
Obstacle Tower Challenge and Beyond
Obstacle Tower Challenge and Beyond
The Unity Obstacle Tower Challenge
Deep Learning for your game?
Building your game
More foundations of learning
Summary
17
Other Books You May Enjoy
Other Books You May Enjoy
Leave a review - let other readers know what you think

To get the most out of this book

Some knowledge of Python and some exposure to machine learning will be beneficial, as will a knowledge of a C style language, such as C, C++, C#, or Java. Some understanding of calculus, while not essential, will be helpful, as will an understanding of probability and statistics.

Download the example code files

You can download the example code files for this book from your account at www.packt.com. If you purchased this book elsewhere, you can visit www.packt.com/support and register to have the files emailed directly to you.

You can download the code files by following these steps:

  1. Log in or register at www.packt.com.
  2. Select the SUPPORT tab.
  3. Click on Code Downloads & Errata.
  4. Enter the name of the book in the Search box and follow the onscreen instructions.

Once the file is downloaded, please make sure that you unzip or extract the folder using the latest version of:

  • WinRAR/7-Zip for Windows
  • Zipeg/iZip/UnRarX for Mac
  • 7-Zip/PeaZip for Linux

The code bundle for the book is also hosted on GitHub at https://github.com/PacktPublishing/Hands-On-Deep-Learning-for-Games. In case there's an update to the code, it will be updated on the existing GitHub repository.

We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. Check them out!

Download the color images

Conventions used

There are a number of text conventions used throughout this book.

CodeInText: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: "Mount the downloaded WebStorm-10*.dmg disk image file as another disk in your system."

A block of code is set as follows:

html, body, #map {
 height: 100%; 
 margin: 0;
 padding: 0
}

When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:

[default]
exten => s,1,Dial(Zap/1|30)
exten => s,2,Voicemail(u100)
exten => s,102,Voicemail(b100)
exten => i,1,Voicemail(s0)

Any command-line input or output is written as follows:

$ mkdir css
$ cd css

Bold: Indicates a new term, an important word, or words that you see on screen. For example, words in menus or dialog boxes appear in the text like this. Here is an example: "Select System info from the Administration panel."

Warnings or important notes appear like this.
Tips and tricks appear like this.
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