Name: Learn Unity Ml-Agents – Fundamentals of Unity Machine Learning: Incorporate new Powerful ml Algorithms Such as Deep Reinforcement Learning for Games (en Inglés)
Price: 137045.00 COP
Availability: InStock
Author: Micheal Lanham
ISBN: 9781789138139

portada Learn Unity Ml-Agents – Fundamentals of Unity Machine Learning: Incorporate new Powerful ml Algorithms Such as Deep Reinforcement Learning for Games (en Inglés)

Formato

Libro Físico

Editorial

Packt Publishing

Autor

Micheal Lanham

Año

2018

Idioma

Inglés

N° páginas

204

Encuadernación

Tapa Blanda

ISBN13

9781789138139

Categorías

Inteligencia artificial

Learn Unity Ml-Agents – Fundamentals of Unity Machine Learning: Incorporate new Powerful ml Algorithms Such as Deep Reinforcement Learning for Games (en Inglés)

Micheal Lanham (Autor) · Packt Publishing · Tapa Blanda

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Reseña del libro "Learn Unity Ml-Agents – Fundamentals of Unity Machine Learning: Incorporate new Powerful ml Algorithms Such as Deep Reinforcement Learning for Games (en Inglés)"

Transform games into environments using machine learning and Deep learning with Tensorflow, Keras, and UnityKey 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.Table of ContentsIntroducing Machine Learning & ML-AgentsThe Bandit and Reinforcement LearningDeep Reinforcement Learning with PythonAdding Agent Exploration and MemoryPlaying the GameTerrarium Revisited – Building A Multi-Agent Ecosystem