文章基本信息

标题：Quantum reinforcement learning method and application based on value function
本地全文：下载
作者：Yi-Pei Liu ; Qing-Shan Jia ; Xu Wang 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2022
卷号：55
期号：11
页码：132-137
DOI：10.1016/j.ifacol.2022.08.061
语种：English
出版社：Elsevier
摘要：AbstractThe Multi-Arm bandit(MAB) problem is a classical problem in the field of reinforcement learning with only one state. The Grid problem is a multi-state problem for reinforcement learning. In this work, we focus on how to combine the classical value function method to quantum computation, and we propose three novel quantum reinforcement learning(QRL) algorithms for the MAB problem and one novel QRL algorithm, which is combined with the quantum random walk and Grover algorithm, for the Grid problem. From the experiments, the learning process is speed-up by combining the value function with quantum computation.
关键词：KeywordsQuantum Reinforcement LearningQuantum ComputationQuantum Amplitude AmplificationBanditGrid