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Reinforcement learning

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Definition Edit

Reinforcement learning is

a framework that shifts the focus of machine learning from pattern recognition to experience-driven sequential decision-making.[1]

Overview Edit

"It promises to carry AI applications forward toward taking actions in the real world. While largely confined to academia over the past several decades, it is now seeing some practical, real-world successes."[2]

"Whereas traditional machine learning has mostly focused on pattern mining, reinforcement learning shifts the focus to decision making, and is a technology that will help AI to advance more deeply into the realm of learning about and executing actions in the real world. It has existed for several decades as a framework for experience-driven sequential decision-making, but the methods have not found great success in practice, mainly owing to issues of representation and scaling. However, the advent of deep learning has provided reinforcement learning with a 'shot in the arm.'"[3]

References Edit

  1. One Hundred Year Study on Artificial Intelligence, at 9.
  2. Id.
  3. Id. at 15.

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