Table of Contents
Preface
Section 1: Reinforcement Learning Foundations
Chapter 1: Introduction to Reinforcement Learning
Why reinforcement learning?
The three paradigms of ML
Supervised learning
Unsupervised learning
Reinforcement learning
RL application areas and success stories
Games
Robotics and autonomous systems
Supply chain
Manufacturing
Personalization and recommender systems
Smart cities
Elements of a RL problem
RL concepts
Casting Tic-Tac-Toe as a RL problem
Setting up your RL environment
Hardware requirements
Operating system
Software toolbox
Summary
References
Chapter 2: Multi-Armed Bandits
Exploration-Exploitation Trade-Off
What is a MAB?
Problem definition
Experimenting with a simple MAB problem
Case study: Online advertising ...
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