Understand the core concepts of deep learning and deep reinforcement learning by applying them to develop games
Key Features
- 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
Book 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.
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.
Who this book is 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.
Table of Contents
- Deep Learning for Games
- Convolutional and Recurrent Networks
- GAN for Games
- Building a Deep Learning Gaming Chatbot
- Introducing DRL
- Unity ML-Agents
- Agent and the Environment
- Understanding PPO
- Rewards and Reinforcement Learning
- Imitation and Transfer Learning
- Building Multi-Agent Environments
- Debugging/Testing a Game with DRL
- Obstacle Tower Challenge and Beyond
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 including 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.
