Book Image

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

Book Image

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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Table of Contents (8 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
Free Chapter
1
Introducing Machine Learning and ML-Agents
Introducing Machine Learning and ML-Agents
Machine Learning
ML-Agents
Running a sample
Creating an environment
Academy, Agent, and Brain
Summary
2
The Bandit and Reinforcement Learning
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
3
Deep Reinforcement Learning with Python
Deep Reinforcement Learning with Python
Installing Python and tools
ML-Agents external brains
Neural network foundations
Deep Q-learning
Proximal policy optimization
Exercises
Summary
4
Going Deeper with Deep Learning
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
5
Playing the Game
Playing the Game
Multi-agent environments
Adversarial self-play
Decisions and On-Demand Decision Making
Imitation learning
Curriculum Learning
Exercises
Summary
6
Terrarium Revisited – A Multi-Agent Ecosystem
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
7
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ML-Agents

For the rest of this book, we will be using the ML-Agents platform with Unity to build ML models that we can learn to play and simulate in various environments. Before we do that, though, we need to pull down the ML-Agents package from GitHub using git. Jump on your computer and open up a command prompt or shell window and follow along:

If you have never used git before, make sure to install it from https://git-scm.com/. You will need to install git before continuing with the following exercises and thus the rest of this book.
  1. Navigate to your work or root folder (on Windows, we will assume that this is C:\):
      cd/
  1. Execute the following command:
      mkdir ML-Agents
  1. This will create the folder ML-Agents. Now, execute the following:
      cd ML-Agents
      git clone https://github.com/Unity-Technologies/ml-agents.git
  1. This uses git to pull down the required files for ML-Agents into a new folder called ml-agents. git will show the files as they are getting pulled into the folder. You can verify that the files have been pulled down successfully by changing to the new folder and executing:
      cd ml-agents
      dir
  1. Right now, we are doing this to make sure that there are any files here. We will get to the specifics later.

Good—that should have been fairly painless. If you had issues pulling the code down, you can always visit the ML-Agents page on GitHub at https://github.com/Unity-Technologies/ml-agents and manually pull the code down. Of course, we will be using more of git to manage and pull files, so you should resolve any problems you may have encountered.

If you are not familiar with GitHub and git, then you really should be. git completely dominates source control across all areas of software development now and is widely used, even at Microsoft, who abandoned their own source control for it. Do yourself a favor, even if you develop your code just for yourself: use source control.

Now that we have ML-Agents installed, we will take a look at one of Unity's sample projects that ships with a toolkit in the next section.

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End of Section 3
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