In this chapter, we dove into working with the ML-Agents framework by developing a heuristic brain that uses an ML technique called Reinforcement Learning to solve various fundamental learning problems. We first explored the classic multi-armed bandit problem in order to first introduce the concepts of RL. Then, we expanded on this problem by adding context or the sense of state. This required us to modify our Value function by adding state and turning our function into a Q function. While this algorithm worked well to solve our simple learning problem, it was not sufficient for more complex problems with delayed rewards. Introducing delayed rewards required us to look at the Bellman equation and understand how we could discount rewards over an agent's steps, thus providing the agent with Q value breadcrumbs as a way for it to find its way home. Finally, we loaded...
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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