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.

Preface

Who this book is for

What this book covers

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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

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Exercises

Use the following exercises to improve your understanding of RL and the PPO trainer.

Convert one of the Unity examples to use just visual observations. Hint, use the GridWorld example as a guide, and remember that the agent may need its own camera.
Alter the CNN configuration of an agent using visual observations in three different ways. You can add more layers, take them away, or alter the kernel filter. Run the training sessions and compare the differences with TensorBoard.
Convert the GridWorld sample to use vector observations and recurrent networks with memory. Hint, you can borrow several pieces of code from the Hallway example.

Revisit the Ball3D example and set it up to use multiple asynchronous agent training.
Set up the crawler example and run it with multiple asynchronous agent training.

If you encounter problems running through these samples, be sure...

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

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

Overview of this book

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