Learn Unity ML-Agents – Fundamentals of Unity Machine Learning · Incorporate New Powerful ML Algorithms Such as Deep Reinforcement Learning for Games by Lanham, Micheal -- Read -- Imperial Library of Trantor

Index

Title Page Copyright and Credits

Learn Unity ML - Agents - Fundamentals of Unity Machine Learning

Dedication Packt Upsell

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Contributors

About the author About the reviewers Packt is searching for authors like you

Preface

Who this book is for What this book covers To get the most out of this book

Download the example code files Download the color images Conventions used

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Reviews

Introducing Machine Learning and ML-Agents

Machine Learning

Training models A Machine Learning example ML uses in gaming

ML-Agents Running a sample

Setting the agent Brain

Creating an environment

Renaming the scripts

Academy, Agent, and Brain

Setting up the Academy Setting up the Agent Setting up the Brain Exercises

Summary

The Bandit and Reinforcement Learning

Reinforcement Learning

Configuring the Agent

Contextual bandits and state

Building the contextual bandits Creating the ContextualDecision script Updating the Agent

Exploration and exploitation

Making decisions with SimpleDecision

MDP and the Bellman equation Q-Learning and connected agents

Looking at the Q-Learning ConnectedDecision script

Exercises Summary

Deep Reinforcement Learning with Python

Installing Python and tools

Installation

Mac/Linux installation Windows installation Docker installation GPU installation

Testing the install

ML-Agents external brains

Running the environment

Neural network foundations

But what does it do?

Deep Q-learning

Building the deep network Training the model Exploring the tensor

Proximal policy optimization

Implementing PPO Understanding training statistics with TensorBoard

Exercises Summary

Going Deeper with Deep Learning

Agent training problems

When training goes wrong

Fixing sparse rewards Fixing the observation of state

Convolutional neural networks Experience replay

Building on experience

Partial observability, memory, and recurrent networks

Partial observability Memory and recurrent networks

Asynchronous actor – critic training

Multiple asynchronous agent training

Exercises Summary

Playing the Game

Multi-agent environments Adversarial self-play

Using internal brains Using trained brains internally

Decisions and On-Demand Decision Making

The Bouncing Banana

Imitation learning

Setting up a cloning behavior trainer

Curriculum Learning Exercises Summary

Terrarium Revisited – A Multi-Agent Ecosystem

What was/is Terrarium? Building the Agent ecosystem

Importing Unity assets Building the environment

Basic Terrarium – Plants and Herbivores

Herbivores to the rescue Building the herbivore Training the herbivore

Carnivore: the hunter

Building the carnivore Training the carnivore

Next steps Exercises Summary

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