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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 (Paperback)
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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 (Paperback)
Expected to ship within 18 - 22 working days
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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 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 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 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.
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