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

Neural conversational agents

The concept of communicating with a computer via natural language first became popular as far back as Star Trek (1966 to 1969). In the series, we can often see Kirk, Scotty, and the gang issuing commands to the computer. Since then, many attempts have been made to build chatbots that can converse naturally with a human. During this often unsuccessful journey over the years, several linguistic methods have been developed. These methods are often grouped together and referred to as natural language processing, or NLP. Now, NLP still is the foundation for most chatbots, including the deep learning variety we will get to shortly.

We often group conversational agents by purpose or task. Currently, we categorize chatbots into two main types:

  • Goal-oriented: These bots are the kind Kirk would use or the ones you likely communicate with on a daily basis, and...