1st Edition
AlphaGo Simplified Rule-Based AI and Deep Learning in Everyday Games
List of Figures
Preface
Acknowledgments
Section I Rule-Based A.I.
Chapter 1 Rule-Based AI in the Coin Game
Chapter 2 Look-Ahead Search in Tic Tac Toe
Chapter 3 Planning Three Steps Ahead in Connect Four
Chapter 4 Recursion and MiniMax Tree Search
Chapter 5 Depth Pruning in MiniMax
Chapter 6 Alpha-Beta Pruning
Chapter 7 Position Evaluation in MiniMax
Chapter 8 Monte Carlo Tree Search
Section II Deep Learning
Chapter 9 Deep Learning in the Coin Game
Chapter 10 Policy Networks in Tic Tac Toe
Chapter 11 A Policy Network in Connect Four
Section III Reinforcement Learning
Chapter 12 Tabular Q-Learning in the Coin Game
Chapter 13 Self-Play Deep Reinforcement Learning
Chapter 14 Vectorization to Speed Up Deep Reinforcement Learning
Chapter 15 A Value Network in Connect Four
Section IV AlphaGo Algorithms
Chapter 16 Implement AlphaGo in the Coin Game
Chapter 17 AlphaGo in Tic Tac Toe and Connect Four
Chapter 18 Hyperparameter Tuning in AlphaGo
Chapter 19 The Actor-Critic Method and AlphaZero
Chapter 20 Iterative Self-Play and AlphaZero in Tic Tac Toe
Chapter 21 AlphaZero in Unsolved Games
Bibliography
Biography
Mark H. Liu is an Associate Professor of Finance, the (Founding) Director of the MS Finance Program at the University of Kentucky. He obtained his Ph.D. in finance from Boston College in 2004 and his M.A. in economics from Western University in Canada in 1998. Dr. Liu has more than 20 years of coding experience and is the author of two books: Make Python Talk (No Starch Press, 2021) and Machine Learning, Animated (CRC Press, 2023).
