Book Overview & Buying
Table Of Contents
Feedback & Rating

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

1 (3)

Buy this Book

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

1 (3)

Buy this Book

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.

Preface

Who this book is for

What this book covers

To get the most out of this book

Get in touch

Free Chapter

Introducing Machine Learning and ML-Agents

Machine Learning

ML-Agents

Running a sample

Creating an environment

Academy, Agent, and Brain

Summary

The Bandit and Reinforcement Learning

Reinforcement Learning

Contextual bandits and state

Exploration and exploitation

MDP and the Bellman equation

Q-Learning and connected agents

Exercises

Summary

Deep Reinforcement Learning with Python

Installing Python and tools

ML-Agents external brains

Neural network foundations

Deep Q-learning

Proximal policy optimization

Exercises

Summary

Going Deeper with Deep Learning

Agent training problems

Convolutional neural networks

Experience replay

Partial observability, memory, and recurrent networks

Asynchronous actor – critic training

Exercises

Summary

Playing the Game

Multi-agent environments

Adversarial self-play

Decisions and On-Demand Decision Making

Imitation learning

Curriculum Learning

Exercises

Summary

Terrarium Revisited – A Multi-Agent Ecosystem

What was/is Terrarium?

Building the Agent ecosystem

Basic Terrarium – Plants and Herbivores

Carnivore: the hunter

Next steps

Exercises

Summary

Other Books You May Enjoy

Leave a review - let other readers know what you think

Customer Reviews

1 (3)

5 star

4 star

3 star

2 star

1 star

100%

Convolutional neural networks

A lot of work has been done over the years in using neural networks to perform image recognition, and along the way, a technique called the convolutional neural network was developed to provide a better method of identifying features in images. This technique works by running a convolution step across the image, as shown in the following diagram:

CNN operation extracting features from an image

What is happening here is that a convolution matrix set by stride is multiplied across the image using a convolution step in order to generate a feature map. We do this in order to isolate features in an image by isolating sections of pixels and applying a grouping filter. If we didn't do this, our network would evaluate the image's raw pixels, which would make recognizing important features in an image difficult. It's not unlike looking at...