Hit-and-Run for Sampling and Planning in Non-Convex Spaces

被引：0

作者：

Abbasi-Yadkori, Yasin ^{[1
]}

Bartlett, Peter L. ^{[2
,3
]}

Gabillon, Victor ^{[4
]}

Malek, Alan ^{[2
]}

机构：

[1] Adobe Res, San Jose, CA 95110 USA

[2] Univ Calif Berkeley, Berkeley, CA USA

[3] QUT, Brisbane, Qld, Australia

[4] Queensland Univ Tech, Brisbane, Qld, Australia

来源：

ARTIFICIAL INTELLIGENCE AND STATISTICS, VOL 54 | 2017年 / 54卷

基金：

澳大利亚研究理事会;

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We propose the Hit-and-Run algorithm for planning and sampling problems in non-convex spaces. For sampling, we show the first analysis of the Hit-and-Run algorithm in non-convex spaces and show that it mixes fast as long as certain smoothness conditions are satisfied. In particular, our analysis reveals an intriguing connection between fast mixing and the existence of smooth measure-preserving mappings from a convex space to the non-convex space. For planning, we show advantages of Hit-and-Run compared to state-of-the-art planning methods such as Rapidly-Exploring Random Trees.

引用

页码：888 / 895

页数：8

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