Book Image

Learn Unity ML-Agents ??? Fundamentals of Unity Machine Learning

Book Image

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)

What was/is Terrarium?

Terrarium was a concept that was probably ahead of its time. While the game did allow developers to transfer some form of state between parents and offspring, it was a feature that was never used in successful agents. Instead, developers just optimized their code for a particular set of fixed strategies, which in many cases turned out to be a lot of code. The following is an example screenshot of a Terrarium client running in ecosystem mode:



Original Microsoft Terrarium

Now, the bottom line is that we are not going to be able to replicate the entire Terrarium connected ecosystem concept in one chapter. The elegance that was the Terrarium network ecosystem infrastructure could possibly take several chapters to explain. Instead, our goal is going to build a multi-agent multi-brain ecosystem that is designed to teach you more about building an ML-Agents environment...