In this chapter, we took a closer look at the Unity PPO trainer. This training model, originally developed at OpenAI, is the current advanced model, and was our focus for starting to build more complex training scenarios. We first revisited the GridWorld example to understand what happens when training goes wrong. From there, we looked at some examples of situations where training is performing sub par, and we learned how to fix some of those issues. Then, we learned how an agent can use visual observations as input into our model, providing the data is processed first. We learned that an agent using visual observation required the use of CNN layers to process and extract features from images. After that, we looked at the value of using experience replay in order to further generalize our models. This taught us that experience and memory were valuable to an agent&apos...
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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