Learn Unity ML-Agents - Fundamentals of Unity Machine Learning [1 ed.] 1789138132, 9781789138139
Unity Machine Learning agents allow researchers and developers to create games and simulations using the Unity Editor, w
2,028 60 4MB
English Pages 204 Year 2018
Table of contents :
1: 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
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
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
Agent training problems
Convolutional neural networks
Experience replay
Partial observability, memory, and recurrent networks
Asynchronous actor – critic training
Exercises
Summary
5: 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
What was/is Terrarium?
Building the Agent ecosystem
Basic Terrarium – Plants and Herbivores
Carnivore: the hunter
Next steps
Exercises
Summary
1: 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
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
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
Agent training problems
Convolutional neural networks
Experience replay
Partial observability, memory, and recurrent networks
Asynchronous actor – critic training
Exercises
Summary
5: 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
What was/is Terrarium?
Building the Agent ecosystem
Basic Terrarium – Plants and Herbivores
Carnivore: the hunter
Next steps
Exercises
Summary
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