You're reading from Advanced Deep Learning with Keras

Product typeBook

Published inOct 2018

Reading LevelIntermediate

PublisherPackt

ISBN-139781788629416

Edition1st Edition

Languages

Python

Tools

Keras TensorFlow

Concepts

Deep Learning

Author (1)

Rowel Atienza

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:

The input is the state-action pair, and the prediction is the Q value
The input is the state, and the prediction is the Q value for each action

The first option is not optimal since the network will be called a number of times equal to the number of...

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Advanced Deep Learning with Keras

Published in: Oct 2018Publisher: PacktISBN-13: 9781788629416

Author (1)

Rowel Atienza

Rowel Atienza is an Associate Professor at the Electrical and Electronics Engineering Institute of the University of the Philippines, Diliman. He holds the Dado and Maria Banatao Institute Professorial Chair in Artificial Intelligence. Rowel has been fascinated with intelligent robots since he graduated from the University of the Philippines. He received his MEng from the National University of Singapore for his work on an AI-enhanced four-legged robot. He finished his Ph.D. at The Australian National University for his contribution on the field of active gaze tracking for human-robot interaction. Rowel's current research work focuses on AI and computer vision. He dreams on building useful machines that can perceive, understand, and reason. To help make his dreams become real, Rowel has been supported by grants from the Department of Science and Technology (DOST), Samsung Research Philippines, and Commission on Higher Education-Philippine California Advanced Research Institutes (CHED-PCARI).
Read more about Rowel Atienza

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Deep Q-Network (DQN)

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