For this chapter and the last, we took a deep dive into the core elements of deep learning and neural networks. While our review in the last couple chapters was not extensive, it should give you a good base for continuing through the rest of the book. If you had troubles with any of the material in the first two chapters, turn back now and spend more time reviewing the previous material. It is important that you understand the basics of neural network architecture and the use of various specialized layers, as we covered in this chapter (CNN and RNN). Be sure you understand the basics of CNN and how to use it effectively in picking features and what the trade—offs are when using pooling or sub sampling. Also understand the concept of RNN and how and when to use LSTM blocks for predicting or detecting temporal events. Convolutional layers and LSTM blocks are now fundamental...
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.
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Current Title:
Hands-On Deep Learning for Games
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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