Deep Q-Networks

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What Does Deep Q-Networks Mean?

Deep Q Networks (DQN) are neural networks (and/or related tools) that utilize deep Q learning in order to provide models such as the simulation of intelligent video game play. Rather than being a specific name for a specific neural network build, Deep Q Networks may be composed of convolutional neural networks and other structures that use specific methods to learn about various processes.

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Techopedia Explains Deep Q-Networks

The method of deep Q learning typically uses something called general policy iteration, described as the conjunction of policy evaluation and policy iteration, to learn policies from high dimensional sensory input.

For example, a common type of deep Q network covered in tech publications like Medium takes sensory input from Atari 2600 video games to model outcomes. This is done on a very fundamental level by gathering samples, storing them and using them for experience replay in order to update the Q network.

In a general sense, deep Q networks train on inputs that represent active players in areas or other experienced samples and learn to match those data with desired outputs. This is a powerful method in the development of artificial intelligence that can play games like chess at a high level, or carry out other high-level cognitive activities – the Atari or chess video game playing example is also a good example of how AI uses the types of interfaces that were traditionally used by human agents.

In other words, with deep Q learning, the AI player gets to be more like a human player in learning to achieve desired outcomes.

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Margaret Rouse
Senior Editor
Margaret Rouse
Senior Editor

Margaret is an award-winning technical writer and teacher known for her ability to explain complex technical subjects to a non-technical business audience. Over the past twenty years, her IT definitions have been published by Que in an encyclopedia of technology terms and cited in articles by the New York Times, Time Magazine, USA Today, ZDNet, PC Magazine, and Discovery Magazine. She joined Techopedia in 2011. Margaret's idea of a fun day is helping IT and business professionals learn to speak each other’s highly specialized languages.