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Hands-On Deep Learning for Games

Hands-On Deep Learning for Games

By : Micheal Lanham
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Hands-On Deep Learning for Games

Hands-On Deep Learning for Games

3 (2)
By: Micheal Lanham

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)
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Section 1: The Basics
6
Section 2: Deep Reinforcement Learning
14
Section 3: Building Games

Setting up ML-Agents

At the time of writing this book, ML-Agents is developed and shipped as a GitHub project. It is likely that as the product matures, it will be shipped as its own asset package, but currently, it is not.

Therefore, we first need to export ML-Agents as an asset package. Open up a new Unity Editor session to an ML-Agents or Unity SDK project, and follow these steps:

  1. Locate the ML-Agents folder in the Project window, and select it.
  2. From the menu, select Assets | Export Package.
  3. Be sure that all of the folder contents are highlighted, as shown in the following Exporting package dialog excerpt:
Exporting ML-Agents as an asset package
  1. Be sure to uncheck the Include dependencies checkbox, as shown in the preceding excerpt. As long as you have the proper root folder selected, all of the dependencies that we need should get packaged.
  2. Click on the Export... button...

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