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Learn Unity ML - Agents - Fundamentals of Unity Machine Learning Paperback – June 29, 2018

by Micheal Lanham (Author)
2.9 2.9 out of 5 stars 7 ratings
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Transform games into environments using machine learning and Deep learning with Tensorflow, Keras, and Unity

Key Features

  • Learn how to apply core machine learning concepts to your games with Unity
  • Learn the Fundamentals of Reinforcement Learning and Q-Learning and apply them to your games
  • Learn How to build multiple asynchronous agents and run them in a training scenario

Book Description

Unity Machine Learning agents allow researchers and developers to create games and simulations using the Unity Editor, which serves as an environment where intelligent agents can be trained with machine learning methods through a simple-to-use Python API.

This book takes you from the basics of Reinforcement and Q Learning to building Deep Recurrent Q-Network agents that cooperate or compete in a multi-agent ecosystem. You will start with the basics of Reinforcement Learning and how to apply it to problems. Then you will learn how to build self-learning advanced neural networks with Python and Keras/TensorFlow. From there you move o n to more advanced training scenarios where you will learn further innovative ways to train your network with A3C, imitation, and curriculum learning models. By the end of the book, you will have learned how to build more complex environments by building a cooperative and competitive multi-agent ecosystem.

What you will learn

  • Develop Reinforcement and Deep Reinforcement Learning for games.
  • Understand complex and advanced concepts of reinforcement learning and neural networks
  • Explore various training strategies for cooperative and competitive agent development
  • Adapt the basic script components of Academy, Agent, and Brain to be used with Q Learning.
  • Enhance the Q Learning model with improved training strategies such as Greedy-Epsilon exploration
  • Implement a simple NN with Keras and use it as an external brain in Unity
  • Understand how to add LTSM blocks to an existing DQN
  • Build multiple asynchronous agents and run them in a training scenario

Who This Book Is For

This book is intended for developers with an interest in using Machine learning algorithms to develop better games and simulations with Unity.

Table of Contents

  1. Introducing Machine Learning & ML-Agents
  2. The Bandit and Reinforcement Learning
  3. Deep Reinforcement Learning with Python
  4. Adding Agent Exploration and Memory
  5. Playing the Game
  6. Terrarium Revisited – Building A Multi-Agent Ecosystem
Previous slide of product details
  1. Print length
    204 pages
  2. Language
    English
  3. Publisher
    Packt Publishing
  4. Publication date
    June 29, 2018
  5. Dimensions
    7.5 x 0.43 x 9.25 inches
  6. ISBN-10
    1789138132
  7. ISBN-13
    978-1789138139
  8. See all details
Next slide of product details
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Product details

  • Publisher ‏ : ‎ Packt Publishing (June 29, 2018)
  • Language ‏ : ‎ English
  • Paperback ‏ : ‎ 204 pages
  • ISBN-10 ‏ : ‎ 1789138132
  • ISBN-13 ‏ : ‎ 978-1789138139
  • Item Weight ‏ : ‎ 12.7 ounces
  • Dimensions ‏ : ‎ 7.5 x 0.43 x 9.25 inches
  • Customer Reviews:
    2.9 2.9 out of 5 stars 7 ratings

Customer reviews

2.9 out of 5 stars
7 global ratings

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Top reviews from the United States

  • rishabh
    1.0 out of 5 stars A true let down
    Reviewed in the United States on November 24, 2018
    Verified Purchase
    Chapters have no good explanation of anything. I found better explanation of the ml-agents examples provided by unity on its own website. The last chapter felt like things were heading in a better direction. The author just gives a set of instructions to set up a project instead of explaining why he is using a specific piece of code and its impact on the project. A true let down.
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  • Digital Kristen & Family
    1.0 out of 5 stars None of the external examples work!
    Reviewed in the United States on August 4, 2018
    Verified Purchase
    While the information was great, attempting to get a windows machne to work with the examples was filled with headaches and errors after errors. I was unable to get any of the examples to work that used external trainers. If your looking for a book on theory, pick this book up else i wouldn't suggest this book.
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Top reviews from other countries

  • Crystal Shining Unicorn Princess
    1.0 out of 5 stars Free Unity tutorials for £20
    Reviewed in the United Kingdom on July 31, 2018
    Verified Purchase
    Absolutely useless book covering what is actually written in free unity ML Agents tutorials (not even 100% of what is covered). Don't waste your money, invest in some legit books on machine learning.
    Report