Learn Unity ML-Agents – Fundamentals of Unity Machine Learning: Incorporate new powerful ML algorithms such as Deep Reinforcement Learning for games

Learn Unity ML-Agents – Fundamentals of Unity Machine Learning: Incorporate new powerful ML algorithms such as Deep Reinforcement Learning for games

Author: Micheal Lanham

ISBN-10 书号: 1789138132
ISBN-13 书号: 9781789138139
Release Finelybook 出版日期: 2018-06-30
pages 页数: 204

$29.99


Book Description to Finelybook sorting

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.
Contents
1: INTRODUCING MACHINE LEARNING AND ML-AGENTS
2: THE BANDIT AND REINFORCEMENT LEARNING
3: DEEP REINFORCEMENT LEARNING WITH PYTHON
4: GOING DEEPER WITH DEEP LEARNING
5: PLAYING THE GAME
6: TERRARIUM REVISITED – A 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
Authors
Micheal Lanham
Micheal Lanham is a proven software architect with 20 years’ experience of developing a range of software, including games, mobile, graphic, web, desktop, engineering, GIS, and machine learning applications for various industries. In 2000, Micheal began working with machine learning and would later use various technologies for a broad range of apps, from geomechanics to inspecting pipelines in 3D. He was later introduced to Unity and has been an avid developer and author of multiple Unity apps and books since.

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Learn Unity ML-Agents – Fundamentals of Unity Machine Learning
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