Deep Learning and the Game of Go

Chapter 11. Reinforcement learning with value methods

This chapter covers

• Making a self-improving game AI with the Q-learning algorithm
• Defining and training multi-input neural networks in Keras
• Building and training a Q-learning agent by using Keras

Have you ever read an expert commentary on a high-level chess or Go tournament game? You’ll often see comments like, “Black is far behind at this point” or “The result up to here is slightly better for white.” What does it mean to be “ahead” or “behind” in the middle of such a strategy game? This isn’t basketball, with a running score to refer to. Instead, the commentator means that the board position is favorable to one player or the other. If you want to be precise, you could define it with ...