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Learn Unity ML-Agents – Fundamentals of Unity Machine Learning: Incorporate new powerful ML algorithms such as Deep Reinforcement Learning for games
Transform games into environments using machine learning and Deep learning with Tensorflow, Keras, and Unity
Key FeaturesLearn how to apply core machine learning concepts to your games with UnityLearn the Fundamentals of Reinforcement Learning and Q-Learning and apply them to your gamesLearn How to build multiple asynchronous agents and run them in a training scenarioBook DescriptionUnity 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 learnDevelop Reinforcement and Deep Reinforcement Learning for games.Understand complex and advanced concepts of reinforcement learning and neural networksExplore various training strategies for cooperative and competitive agent developmentAdapt 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 explorationImplement a simple NN with Keras and use it as an external brain in UnityUnderstand how to add LTSM blocks to an existing DQNBuild multiple asynchronous agents and run them in a training scenarioWho this book is forThis book is intended for developers with an interest in using Machine learning algorithms to develop better games and simulations with Unity.
The reader will be required to have a working knowledge of C# and a basic understanding of Python.
Table of ContentsIntroducing Machine Learning & ML-Agents The Bandit and Reinforcement Learning Deep Reinforcement Learning with PythonAdding Agent Exploration and Memory Playing the Game Terrarium Revisited – Building A Multi-Agent Ecosystem
Key FeaturesLearn how to apply core machine learning concepts to your games with UnityLearn the Fundamentals of Reinforcement Learning and Q-Learning and apply them to your gamesLearn How to build multiple asynchronous agents and run them in a training scenarioBook DescriptionUnity 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 learnDevelop Reinforcement and Deep Reinforcement Learning for games.Understand complex and advanced concepts of reinforcement learning and neural networksExplore various training strategies for cooperative and competitive agent developmentAdapt 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 explorationImplement a simple NN with Keras and use it as an external brain in UnityUnderstand how to add LTSM blocks to an existing DQNBuild multiple asynchronous agents and run them in a training scenarioWho this book is forThis book is intended for developers with an interest in using Machine learning algorithms to develop better games and simulations with Unity.
The reader will be required to have a working knowledge of C# and a basic understanding of Python.
Table of ContentsIntroducing Machine Learning & ML-Agents The Bandit and Reinforcement Learning Deep Reinforcement Learning with PythonAdding Agent Exploration and Memory Playing the Game Terrarium Revisited – Building A Multi-Agent Ecosystem
206 pages, Kindle Edition
Published June 30, 2018
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Displaying 1 of 1 review
August 4, 2018
None of the external examples work!
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.
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.
Displaying 1 of 1 review