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

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

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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

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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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Exploration and exploitation

One of the dilemmas we face in RL is the balance between exploring all possible actions and exploiting the best possible action. In the multi-armed bandit problem, our search space was small enough to do this with brute force, essentially just by pulling each arm one by one. However, in more complex problems, the number of states could exceed the number of atoms in the known universe. Yes, you read that correctly. In those cases, we need to establish a policy or method whereby we can balance the exploration and exploitation dilemma. There are a few ways in which we can do this, and the following are the most common ways you can approach this:

Greedy Optimistic: The agent initially starts with high values in its q table. This forces the agent to explore all states at least once, since the agent otherwise always greedily chooses the best action.
Greedy...