Name: Learn Unity ML-Agents：Fundamentals of Unity Machine Learning
Author: Micheal Lanham
ISBN: 9781789131864

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Learn Unity ML-Agents：Fundamentals of Unity Machine Learning

Micheal Lanham

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Copyright and Credits
Learn Unity ML - Agents - Fundamentals of Unity Machine Learning
Dedication
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Why subscribe?
PacktPub.com
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
Get in touch
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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Learn Unity ML-Agents：Fundamentals of Unity Machine Learning