Deep Learning and the Game of Go

Chapter 10. Reinforcement learning with policy gradients

This chapter covers

• Improving game play with policy gradient learning
• Implementing policy gradient learning in Keras
• Tuning optimizers for policy gradient learning

Chapter 9 showed you how to make a Go-playing program play against itself and save the results in experience data. That’s the first half of reinforcement learning; the next step is to use experience data to improve the agent so that it wins more often. The agent from the previous chapter used a neural network to select which move to play. As a thought experiment, imagine you shift every weight in the network by a random amount. Then the agent will select different moves. Just by luck, some of those new moves will be better ...