文章基本信息

标题：Time Hopping Technique for Faster Reinforcement Learning in Simulations
本地全文：下载
作者：P. Kormushev ; K. Nomoto ; F. Dong 等
期刊名称：Cybernetics and Information Technologies
印刷版ISSN：1311-9702
电子版ISSN：1314-4081
出版年度：2011
卷号：11
期号：3
出版社：Bulgarian Academy of Science
摘要：A technique called Time Hopping is proposed for speeding up reinforcement learning algorithms. It is applicable to continuous optimization problems running in computer simulations. Making shortcuts in time by hopping between distant states combined with off-policy reinforcement learning allows the technique to maintain higher learning rate. Experiments on a simulated biped crawling robot confirm that Time Hopping can accelerate the learning process more than seven times.
关键词：Reinforcement learning; biped robot; discrete time systems; optimization;methods; computer simulation.