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

Summary

In this chapter, we covered an emerging technique in RL called Imitation Learning or Behavioral Cloning. This technique, as we learned, takes the captured observations of a player playing a game and then uses those observations in an online or offline setting to further train the agent. We further learned that IL is just a form of Transfer Learning. We then covered a technique with ML-Agents that will allow you to transfer brains across environments. Finally, we looked at how to chain IL andtransfer learningas a way of stimulating the agent's training into developing new strategies on its own.

In the next chapter, we will further our understanding of DRL in games by looking at multiple agent training scenarios.