Skip to Main Content
Advanced Deep Learning with Keras
book

Advanced Deep Learning with Keras

by Rowel Atienza, Neeraj Verma, Valerio Maggio
October 2018
Intermediate to advanced content levelIntermediate to advanced
368 pages
9h 20m
English
Packt Publishing
Content preview from Advanced Deep Learning with Keras

Deep Q-Network (DQN)

Using the Q-Table to implement Q-Learning is fine in small discrete environments. However, when the environment has numerous states or continuous as in most cases, a Q-Table is not feasible or practical. For example, if we are observing a state made of four continuous variables, the size of the table is infinite. Even if we attempt to discretize the four variables into 1000 values each, the total number of rows in the table is a staggering 10004 = 1e12. Even after training, the table is sparse - most of the cells in this table are zero.

A solution to this problem is called DQN [2] which uses a deep neural network to approximate the Q-Table. As shown in Figure 9.6.1. There are two approaches to build the Q-network:

  1. The input ...
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

Hands-On Neural Networks with Keras

Hands-On Neural Networks with Keras

Niloy Purkait
Deep Learning with Keras

Deep Learning with Keras

Antonio Gulli, Sujit Pal
Keras Deep Learning Cookbook

Keras Deep Learning Cookbook

Rajdeep Dua, Sujit Pal, Manpreet Singh Ghotra

Publisher Resources

ISBN: 9781788629416Supplemental Content