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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

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

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

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

1 (3)

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.
Table of Contents (8 chapters)
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Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
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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
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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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Proximal policy optimization

Thus far, our discussion of RL has looked at simpler techniques for building agents with bandits and Q-learning. Q-learning is a popular algorithm, and as we learned, deep Q neural networks provide us with a great foundation to use to solve more difficult problems, such as a cart balancing a pole. The following table summarizes the various RL algorithms, what conditions they are capable of working in, and how they function:

Algorithm Model Policy Action Observation Operator
Q-Learning Model-free Off-policy Discrete Discrete Q value
SARSA – State Action Reward State Action Model-free On-policy Discrete Discrete Q value
DQN – Deep Q Network Model-free Off-policy Discrete Continuous Q value
DDPG – Deep Deterministic Policy Gradient Model-free Off-policy Continuous Continuous Q value
TRPO – Trust Region Policy Optimization...
End of Section 6
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