Jonsson Anders
Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain.
Kidney Dis (Basel). 2019 Feb;5(1):18-22. doi: 10.1159/000492670. Epub 2018 Oct 12.
Reinforcement learning has achieved tremendous success in recent years, notably in complex games such as Atari, Go, and chess. In large part, this success has been made possible by powerful function approximation methods in the form of deep neural networks. The objective of this paper is to introduce the basic concepts of reinforcement learning, explain how reinforcement learning can be effectively combined with deep learning, and explore how deep reinforcement learning could be useful in a medical context.
近年来，强化学习取得了巨大成功，尤其是在诸如雅达利游戏、围棋和国际象棋等复杂游戏中。在很大程度上，这种成功得益于深度神经网络形式的强大函数逼近方法。本文的目的是介绍强化学习的基本概念，解释强化学习如何能与深度学习有效结合，并探讨深度强化学习在医学背景下如何发挥作用。
