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Deep reinforcement learning is reinforcement learning that is applied using deep neural networks. This type of learning involves computers on acting on sophisticated models and looking at large amounts of input in order to determine an optimized path or action.
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.