Efficient approximate planning in continuous space Markovian Decision Problems

被引：0
|
作者：
Szepesvári, C [1 ]
机构：
[1] Mindmaker Ltd, H-1121 Budapest, Hungary
来源：
AI COMMUNICATIONS | 2001年 / 14卷 / 03期
关键词：
Markovian Decision Problems; planning; value teration; Monte-carlo algorithms;
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：
Monte-Carlo planning algorithms for planning in continuous state-space, discounted Markovian Decision Problems (MDPs) having a smooth transition law and a finite action space are considered. We prove various polynomial complexity results for the considered algorithms, improving upon several known bounds.
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页码：163 / 176
页数：14
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