A METHODOLOGY FOR COMPUTATION REDUCTION FOR SPECIALLY STRUCTURED LARGE-SCALE MARKOV DECISION-PROBLEMS

被引：4

作者：

DING, FY ^{[1
]}

HODGSON, TJ ^{[1
]}

KING, RE ^{[1
]}

机构：

[1] N CAROLINA STATE UNIV,DEPT IND ENGN,RALEIGH,NC 27695

来源：

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH | 1988年 / 34卷 / 01期

关键词：

MATHEMATICAL PROGRAMMING; DYNAMIC - OPERATIONS RESEARCH;

D O I：

10.1016/0377-2217(88)90461-4

中图分类号：

C93 [管理学];

学科分类号：

12 ; 1201 ; 1202 ; 120202 ;

摘要：

Markov Decision Processes (MDP's) deal with sequential decision making in stochastic systems. Existing solution techniques provide powerful tools for determining the optimal policy set in such systems. However, many problems have extremely large state and action spaces making them computationally intractable. The purpose of this paper is both to present a methodology which takes advantage of the structure of many large scale problems (i. e. , problems with a high percentage of transient states under optimal control), and to provide computational results indicating the value of the approach.

引用

页码：105 / 112

页数：8

共 17 条

[1] A MARKOVIAN DECISION PROCESS [J].