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.
Table of Contents (18 chapters)
Free Chapter
1
Section 1: The Basics
6
Section 2: Deep Reinforcement Learning
14
Section 3: Building Games

A Q-Learning model

RL is deeply entwined with several mathematical and dynamic programming concepts that could fill a textbook, and indeed there are several. For our purposes, however, we just need to understand the key concepts in order to build our DRL agents. Therefore, we will choose not to get too burdened with the math, but there are a few key concepts that you will need to understand to be successful. If you covered the math in the Chapter 1, Deep Learning for Games, this section will be a breeze. For those that didn't, just take your time, but you can't miss this one.

In order to understand the Q-Learning model, which is a form of RL, we need to go back to the basics. In the next section, we talk about the importance of the Markov decision process and the Bellman equation.

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