Deep Reinforcement Learning

What Does Deep Reinforcement Learning Mean?

Deep reinforcement learning (Deep RL) is an approach to machine learning that blends reinforcement learning techniques with strategies for deep learning.

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This type of learning requires computers to use sophisticated learning models and look at large amounts of input in order to determine an optimized path or action.

Techopedia Explains Deep Reinforcement Learning

One way to describe deep reinforcement learning is that a deep neural network learns through the reinforcement of individual experiences.

Suppose the deep neural network maps a visual game space and analyzes that game space through a time continuum to see what happens within the game. The computer starts to understand what the outcomes are based on inputs, and can in turn "play smarter." This relates to other similar technological efforts such as deep Q networks.

In general, machine learning experts are pushing these types of models as a way for machines to continuously get smarter or learn to think more like humans, although practical barriers and boundaries apply.

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Margaret Rouse
Technology Expert

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.