Deep Learning and the Game of Go

Chapter 14. AlphaGo Zero: Integrating tree search with reinforcement learning

This chapter covers

• Playing games with a variation on Monte Carlo tree search
• Integrating tree search into self-play for reinforcement learning
• Training a neural network to enhance a tree-search algorithm

After DeepMind revealed the second edition of AlphaGo, code-named Master, Go fans all over the world scrutinized its shocking style of play. Master’s games were full of surprising new moves. Although Master was bootstrapped from human games, it was continuously enhanced with reinforcement learning, and that enabled it to discover new moves that humans didn’t play.

This led to an obvious question: what if AlphaGo didn’t rely on human games at all, but instead learned ...