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Home > Data > Deep Learning > Hands-On Deep Learning for Games
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

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...

The past, present, and future of DL

While the term deep learning was first associated with neural networks in 2000 by Igor Aizenberg and colleagues, it has only become popular in the last 5 years. Prior to this, we called this type of algorithm an artificial neural network (ANN). However, deep learning refers to something broader than ANNs and includes many other areas of connected machines. Therefore, to clarify, we will be discussing the ANN form of DL for much of the remainder of this book. However, we will also discuss some other forms of DL that can be used in games, in Chapter 5, Introducing DRL.

The past

The first form of a multilayer perceptron (MLP) network, or what we now call an ANN, was introduced by Alexey Ivakhnenko...

Neural networks – the foundation

The inspiration for neural networks or multilayer perceptrons is the human brain and nervous system. At the heart of our nervous system is the neuron pictured above the computer analog, which is a perceptron:

Example of human neuron beside a perceptron

The neurons in our brain collect input, do something, and then spit out a response much like the computer analog, the perceptron. A perceptron takes a set of inputs, sums them all up, and passes them through an activation function. That activation function determines whether to send output, and at what level to send it when activated. Let's take a closer look at the perceptron, as follows:

Perceptron

On the left-hand side of the preceding diagram, you can see the set of inputs getting pushed in, plus a constant bias. We will get more into the bias later. Then the inputs are multiplied...

Multilayer perceptron in TF

Thus far, we have been looking at a simple example of a single perceptron and how to train it. This worked well for our small dataset, but as the number of inputs increases, the complexity of our networks increases, and this cascades into the math as well. The following diagram shows a multilayer perceptron, or what we commonly refer to as an ANN:

Multilayer perceptron or ANN

In the diagram, we see a network with one input, one hidden, and one output layer. The inputs are now shared across an input layer of neurons. The first layer of neurons processes the inputs, and outputs the results to be processed by the hidden layer and so on, until they finally reach the output layer.

Multilayer networks can get quite complex, and the code for these models is often abstracted away by high-level interfaces such as Keras, PyTorch, and so on. These tools work...

TensorFlow Basics

TensorFlow (TF) is quickly becoming the technology that powers many DL applications. There are other APIs, such as Theano, but it is the one that has gathered the greatest interest and mostly applies to us. Overarching frameworks, such as Keras, offer the ability to deploy TF or Theano models, for instance. This is great for prototyping and building a quick proof of concept, but, as a game developer, you know that when it comes to games, the dominant requirements are always performance and control. TF provides better performance and more control than any higher-level framework such as Keras. In other words, to be a serious DL developer, you likely need and want to learn TF.

TF, as its name suggests, is all about tensors. A tensor is a mathematical concept that describes a set of data organized in n dimensions, where n could be 1, 2 x 2, 4 x 4 x 4, and so on...

Training neural networks with backpropagation

Calculating the activation of a neuron, the forward part, or what we call feed-forward propagation, is quite straightforward to process. The complexity we encounter now is training the errors back through the network. When we train the network now, we start at the last output layer and determine the total error, just as we did with a single perceptron, but now we need to sum up all errors across the output layer. Then we need to use this value to backpropagate the error back through the network, updating each of the weights based on their contribution to the total error. Understanding the contribution of a single weight in a network with thousands or millions of weights could be quite complicated, except thankfully for the help of differentiation and the chain rule. Before we get to the complicated math, we first need to discuss the...

Building an autoencoder with Keras

While we have covered a lot of important ground we will need for understanding DL, what we haven't done yet is build something that can really do anything. One of the first problems we tackle when starting with DL is to build autoencoders to encode and reform data. Working through this exercise allows us to confirm that what goes into a network can also come back out of a network and essentially reassures us that an ANN is not a complete black box. Building and working with autoencoders also allows us to tweak and test various parameters in order to understand their function. Let's get started by opening up the Chapter_1_5.py listing and following these steps:

  1. We will go through the listing section by section. First, we input the base layers Input and Dense, then Model, all from the tensorflow.keras module, with the following imports...

Exercises

Use these additional exercises to assist in your learning and test your knowledge further.

Answer the following questions:

  1. Name three different activation functions. Remember, Google is your friend.
  2. What is the purpose of a bias?
  3. What would you expect to happen if you reduced the number of epochs in one of the chapter samples? Did you try it?
  4. What is the purpose of backpropagation?
  5. Explain the purpose of the Cost function.
  6. What happens when you increase or decrease the number of encoding dimensions in the Keras autoencoder example?
  7. What is the name of the layer type that we feed input into?
  8. What happens when you increase or decrease the batch size?
  9. What is the shape of the input Tensor for the Keras example? Hint: we already have a print statement displaying this.
  10. In the last exercise, how many MNIST samples do we train and test with?

As we progress in the book,...

Summary

In this chapter, we explored the foundations of DL from the basics of the simple single perceptron to more complex multilayer perceptron models. We started with the past, present, and future of DL and, from there, we built a basic reference implementation of a single perceptron so that we could understand the raw simplicity of DL. Then we built on our knowledge by adding more perceptrons into a multiple layer implementation using TF. Using TF allowed us to see how a raw internal model is represented and trained with a much more complex dataset, MNIST. Then we took a long journey through the math, and although a lot of the complex math was abstracted away from us with Keras, we took an in-depth look at how gradient descent and backpropagation work. Finally, we finished off the chapter with another reference implementation from Keras that featured an autoencoder. Auto encoding...

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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 : Mar 30, 2019
Length: 392 pages
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Table of Contents

17 Chapters
Section 1: The Basics Chevron down icon Chevron up icon
Section 1: The Basics
Deep Learning for Games Chevron down icon Chevron up icon
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
Convolutional and Recurrent Networks Chevron down icon Chevron up icon
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
GAN for Games Chevron down icon Chevron up icon
GAN for Games
Introducing GANs
Coding a GAN in Keras
Wasserstein GAN
Generating textures with a GAN
A GAN for creating music
Exercises
Summary
Building a Deep Learning Gaming Chatbot Chevron down icon Chevron up icon
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
Section 2: Deep Reinforcement Learning Chevron down icon Chevron up icon
Section 2: Deep Reinforcement Learning
Introducing DRL Chevron down icon Chevron up icon
Introducing DRL
Reinforcement learning
RL with the OpenAI Gym
A Q-Learning model
First DRL with Deep Q-learning
RL experiments
Exercises
Summary
Unity ML-Agents Chevron down icon Chevron up icon
Unity ML-Agents
Installing ML-Agents
Training an agent
What's in a brain?
Monitoring training with TensorBoard
Running an agent
Exercises
Summary
Agent and the Environment Chevron down icon Chevron up icon
Agent and the Environment
Exploring the training environment
Understanding state
Understanding visual state
Convolution and visual state
Recurrent networks for remembering series
Exercises
Summary
Understanding PPO Chevron down icon Chevron up icon
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
Rewards and Reinforcement Learning Chevron down icon Chevron up icon
Rewards and Reinforcement Learning
Rewards and reward functions
Sparsity of rewards
Curriculum Learning
Understanding Backplay
Curiosity Learning
Exercises
Summary
Imitation and Transfer Learning Chevron down icon Chevron up icon
Imitation and Transfer Learning
IL, or behavioral cloning
Online training
Offline training
Transfer learning
Imitation Transfer Learning
Exercises
Summary
Building Multi-Agent Environments Chevron down icon Chevron up icon
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
Section 3: Building Games Chevron down icon Chevron up icon
Section 3: Building Games
Debugging/Testing a Game with DRL Chevron down icon Chevron up icon
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
Obstacle Tower Challenge and Beyond Chevron down icon Chevron up icon
Obstacle Tower Challenge and Beyond
The Unity Obstacle Tower Challenge
Deep Learning for your game?
Building your game
More foundations of learning
Summary
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Customer reviews

Rating distribution
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Sharky Nov 08, 2019
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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.
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N8tn Oct 09, 2019
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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.
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About the author

Profile icon Micheal Lanham
Micheal Lanham
Micheal Lanham is a proven software and tech innovator with 20 years of experience. During that time, he has developed a broad range of software applications in areas such as games, graphics, web, desktop, engineering, artificial intelligence, GIS, and machine learning applications for a variety of industries as an R&D developer. At the turn of the millennium, Micheal began working with neural networks and evolutionary algorithms in game development. He was later introduced to Unity and has been an avid developer, consultant, manager, and author of multiple Unity games, graphic projects, and books ever since.
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  14. Russian Federation
  15. Syrian Arab Republic
  16. Ukraine
  17. Venezuela
What is custom duty/charge? Chevron down icon Chevron up icon

Customs duty are charges levied on goods when they cross international borders. It is a tax that is imposed on imported goods. These duties are charged by special authorities and bodies created by local governments and are meant to protect local industries, economies, and businesses.

Do I have to pay customs charges for the print book order? Chevron down icon Chevron up icon

The orders shipped to the countries that are listed under EU27 will not bear custom charges. They are paid by Packt as part of the order.

List of EU27 countries: www.gov.uk/eu-eea:

A custom duty or localized taxes may be applicable on the shipment and would be charged by the recipient country outside of the EU27 which should be paid by the customer and these duties are not included in the shipping charges been charged on the order.

How do I know my custom duty charges? Chevron down icon Chevron up icon

The amount of duty payable varies greatly depending on the imported goods, the country of origin and several other factors like the total invoice amount or dimensions like weight, and other such criteria applicable in your country.

For example:

  • If you live in Mexico, and the declared value of your ordered items is over $ 50, for you to receive a package, you will have to pay additional import tax of 19% which will be $ 9.50 to the courier service.
  • Whereas if you live in Turkey, and the declared value of your ordered items is over € 22, for you to receive a package, you will have to pay additional import tax of 18% which will be € 3.96 to the courier service.
How can I cancel my order? Chevron down icon Chevron up icon

Cancellation Policy for Published Printed Books:

You can cancel any order within 1 hour of placing the order. Simply contact customercare@packt.com with your order details or payment transaction id. If your order has already started the shipment process, we will do our best to stop it. However, if it is already on the way to you then when you receive it, you can contact us at customercare@packt.com using the returns and refund process.

Please understand that Packt Publishing cannot provide refunds or cancel any order except for the cases described in our Return Policy (i.e. Packt Publishing agrees to replace your printed book because it arrives damaged or material defect in book), Packt Publishing will not accept returns.

What is your returns and refunds policy? Chevron down icon Chevron up icon

Return Policy:

We want you to be happy with your purchase from Packtpub.com. We will not hassle you with returning print books to us. If the print book you receive from us is incorrect, damaged, doesn't work or is unacceptably late, please contact Customer Relations Team on customercare@packt.com with the order number and issue details as explained below:

  1. If you ordered (eBook, Video or Print Book) incorrectly or accidentally, please contact Customer Relations Team on customercare@packt.com within one hour of placing the order and we will replace/refund you the item cost.
  2. Sadly, if your eBook or Video file is faulty or a fault occurs during the eBook or Video being made available to you, i.e. during download then you should contact Customer Relations Team within 14 days of purchase on customercare@packt.com who will be able to resolve this issue for you.
  3. You will have a choice of replacement or refund of the problem items.(damaged, defective or incorrect)
  4. Once Customer Care Team confirms that you will be refunded, you should receive the refund within 10 to 12 working days.
  5. If you are only requesting a refund of one book from a multiple order, then we will refund you the appropriate single item.
  6. Where the items were shipped under a free shipping offer, there will be no shipping costs to refund.

On the off chance your printed book arrives damaged, with book material defect, contact our Customer Relation Team on customercare@packt.com within 14 days of receipt of the book with appropriate evidence of damage and we will work with you to secure a replacement copy, if necessary. Please note that each printed book you order from us is individually made by Packt's professional book-printing partner which is on a print-on-demand basis.

What tax is charged? Chevron down icon Chevron up icon

Currently, no tax is charged on the purchase of any print book (subject to change based on the laws and regulations). A localized VAT fee is charged only to our European and UK customers on eBooks, Video and subscriptions that they buy. GST is charged to Indian customers for eBooks and video purchases.

What payment methods can I use? Chevron down icon Chevron up icon

You can pay with the following card types:

  1. Visa Debit
  2. Visa Credit
  3. MasterCard
  4. PayPal
What is the delivery time and cost of print books? Chevron down icon Chevron up icon

Shipping Details

USA:

'

Economy: Delivery to most addresses in the US within 10-15 business days

Premium: Trackable Delivery to most addresses in the US within 3-8 business days

UK:

Economy: Delivery to most addresses in the U.K. within 7-9 business days.
Shipments are not trackable

Premium: Trackable delivery to most addresses in the U.K. within 3-4 business days!
Add one extra business day for deliveries to Northern Ireland and Scottish Highlands and islands

EU:

Premium: Trackable delivery to most EU destinations within 4-9 business days.

Australia:

Economy: Can deliver to P. O. Boxes and private residences.
Trackable service with delivery to addresses in Australia only.
Delivery time ranges from 7-9 business days for VIC and 8-10 business days for Interstate metro
Delivery time is up to 15 business days for remote areas of WA, NT & QLD.

Premium: Delivery to addresses in Australia only
Trackable delivery to most P. O. Boxes and private residences in Australia within 4-5 days based on the distance to a destination following dispatch.

India:

Premium: Delivery to most Indian addresses within 5-6 business days

Rest of the World:

Premium: Countries in the American continent: Trackable delivery to most countries within 4-7 business days

Asia:

Premium: Delivery to most Asian addresses within 5-9 business days

Disclaimer:
All orders received before 5 PM U.K time would start printing from the next business day. So the estimated delivery times start from the next day as well. Orders received after 5 PM U.K time (in our internal systems) on a business day or anytime on the weekend will begin printing the second to next business day. For example, an order placed at 11 AM today will begin printing tomorrow, whereas an order placed at 9 PM tonight will begin printing the day after tomorrow.


Unfortunately, due to several restrictions, we are unable to ship to the following countries:

  1. Afghanistan
  2. American Samoa
  3. Belarus
  4. Brunei Darussalam
  5. Central African Republic
  6. The Democratic Republic of Congo
  7. Eritrea
  8. Guinea-bissau
  9. Iran
  10. Lebanon
  11. Libiya Arab Jamahriya
  12. Somalia
  13. Sudan
  14. Russian Federation
  15. Syrian Arab Republic
  16. Ukraine
  17. Venezuela