The human brain is often the main inspiration and comparison we make when building AI and is something deep learning researchers often look to for inspiration or reassurance. By studying the brain and its parts in more detail, we often discover neural sub-processes. An example of a neural sub-process would be our visual cortex, the area or region of our brain responsible for vision. We now understand that this area of our brain is wired differently and responds differently to input. This just so happens to be analogous to analog what we have found in our previous attempts at using neural networks to classify images. Now, the human brain has many sub-processes all with specific mapped areas in the brain (sight, hearing, smell, speech, taste, touch, and memory/temporal), but in this chapter, we will look at how we model just sight and memory...
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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