In 2002, Microsoft developed a fun little developer game called Terrarium that demonstrated the code portability of the .NET Framework. Back then, C# and .NET were just newcomers and Microsoft had a tough sell; after all, it was dethroning VBA and Visual Basic as well as trying to steal away Java developers. The game allowed developers to build tiny programmable creatures that would be born, eat, sleep, reproduce, and die in a terrarium. It also featured the ability of allowing tiny programs to infect other connected terrariums. In fact, Microsoft has a contest that pitted these tiny creature programs against each other and the winning developer was the one that had the most successful creature. While we won't go so far as to build a connected terrarium infrastructure, we will do our best to replicate a number of the cooler...
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.
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Current Title:
Learn Unity ML-Agents ??? Fundamentals of Unity Machine Learning
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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