Skip to Content
Reinforcement Learning with TensorFlow
book

Reinforcement Learning with TensorFlow

by Sayon Dutta
April 2018
Intermediate to advanced content levelIntermediate to advanced
334 pages
10h 18m
English
Packt Publishing
Content preview from Reinforcement Learning with TensorFlow

Monte Carlo Tree Search

In Chapter 5Q-Learning and Deep Q Networks we studied the Monte Carlo Tree Search. Here, let's revise it again and see how it was used by AlphaGo to achieve better results.

Monte Carlo Tree Search is an alternative approach to game tree search. In this approach, we run many simulations of the game, where each simulation starts with the current game state and ends with one of the two players being the winner. At the start, simulations are random where actions are chosen randomly for both players. At each simulation, for each game state of that simulation, corresponding values are stored. This value of a game state (node) represents the frequency of occurrence of this node and frequency of how many of these occurrences ...

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 with TensorFlow - Second Edition

Deep Learning with TensorFlow - Second Edition

Giancarlo Zaccone, Vihan Jain, Md. Rezaul Karim, Motaz Saad
Deep Learning with TensorFlow 2 and Keras - Second Edition

Deep Learning with TensorFlow 2 and Keras - Second Edition

Antonio Gulli, Dr. Amita Kapoor, Sujit Pal

Publisher Resources

ISBN: 9781788835725Supplemental Content