Skip to Main Content
Deep Learning and the Game of Go
book

Deep Learning and the Game of Go

by Kevin Ferguson, Max Pumperla
January 2019
Intermediate to advanced content levelIntermediate to advanced
384 pages
13h 27m
English
Manning Publications
Content preview from 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 ...

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.
Start your free trial

You might also like

Deep Learning

Deep Learning

Andrew Glassner
Deep Learning with PyTorch

Deep Learning with PyTorch

Eli Stevens, Thomas Viehmann, Luca Pietro Giovanni Antiga

Publisher Resources

ISBN: 9781617295324Supplemental ContentPublisher SupportOtherPublisher WebsitePurchase Link