Understand the core concepts of deep learning and deep reinforcement learning by applying them to develop games
- 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
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
- Title Page
- Copyright and Credits
- About Packt
- Section 1: The Basics
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
Convolutional and Recurrent Networks
- Convolutional neural networks
- Understanding convolution
- Building a self-driving CNN
- Memory and recurrent networks
- Playing Rock, Paper, Scissors with LSTMs
- GAN for Games
Building a Deep Learning Gaming Chatbot
- Neural conversational agents
- Sequence-to-sequence learning
- Building the chatbot server
- Running the chatbot in Unity
- Section 2: Deep Reinforcement Learning
- Introducing DRL
- Unity ML-Agents
- Agent and the Environment
- Marathon RL
- The partially observable Markov decision process
- Actor-Critic and continuous action spaces
- Understanding TRPO and PPO
- Learning to tune PPO
- Rewards and Reinforcement Learning
- Imitation and Transfer Learning
Building Multi-Agent Environments
- Adversarial and cooperative self-play
- Adversarial self-play
- Multi-brain play
- Adding individuality with intrinsic rewards
- Extrinsic rewards for individuality
- Section 3: Building Games
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
- Obstacle Tower Challenge and Beyond
- Other Books You May Enjoy
- Title: Hands-On Deep Learning for Games
- Release date: March 2019
- Publisher(s): Packt Publishing
- ISBN: 9781788994071