In this chapter, we explored the foundations of DL from the basics of the simple single perceptron to more complex multilayer perceptron models. We started with the past, present, and future of DL and, from there, we built a basic reference implementation of a single perceptron so that we could understand the raw simplicity of DL. Then we built on our knowledge by adding more perceptrons into a multiple layer implementation using TF. Using TF allowed us to see how a raw internal model is represented and trained with a much more complex dataset, MNIST. Then we took a long journey through the math, and although a lot of the complex math was abstracted away from us with Keras, we took an in-depth look at how gradient descent and backpropagation work. Finally, we finished off the chapter with another reference implementation from Keras that featured an autoencoder. Auto encoding...
Hands-On Deep Learning for Games
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Hands-On Deep Learning for Games
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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)
Preface
Deep Learning for Games
Convolutional and Recurrent Networks
GAN for Games
Building a Deep Learning Gaming Chatbot
Section 2: Deep Reinforcement Learning
Introducing DRL
Unity ML-Agents
Agent and the Environment
Understanding PPO
Rewards and Reinforcement Learning
Imitation and Transfer Learning
Building Multi-Agent Environments
Section 3: Building Games
Debugging/Testing a Game with DRL
Obstacle Tower Challenge and Beyond
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