This book is for anyone who wants a good practical introduction to some specific ML technologies that work and are very fun to play with. While this book covers some very advanced topics, anyone with a high-school level of math, patience, and understanding of C# will be able to work through all the exercises. We do feature example Python code and use Python for most of the training, but only a superficial knowledge of the language is required.
Learn Unity ML-Agents ??? Fundamentals of Unity Machine Learning
Learn Unity ML-Agents ??? Fundamentals of Unity Machine Learning
Overview of this book
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.
Table of Contents (8 chapters)
Preface
Free Chapter
Introducing Machine Learning and ML-Agents
The Bandit and Reinforcement Learning
Deep Reinforcement Learning with Python
Going Deeper with Deep Learning
Playing the Game
Terrarium Revisited – A Multi-Agent Ecosystem
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