Acceleration Operators in the Value Iteration Algorithms for Markov Decision Processes
成果类型：
Article
署名作者：
Shlakhter, Oleksandr; Lee, Chi-Guhn; Khmelev, Dmitry; Jaber, Nasser
署名单位：
University of Toronto; University of Toronto
刊物名称：
OPERATIONS RESEARCH
ISSN/ISSBN：
0030-364X
DOI：
10.1287/opre.1090.0705
发表日期：
2010
页码：
193-202
关键词：
摘要：
We study the general approach to accelerating the convergence of the most widely used solution method of Markov decision processes (MDPs) with the total expected discounted reward. Inspired by the monotone behavior of the contraction mappings in the feasible set of the linear programming problem equivalent to the MDP, we establish a class of operators that can be used in combination with a contraction mapping operator in the standard value iteration algorithm and its variants. We then propose two such operators, which can be easily implemented as part of the value iteration algorithm and its variants. Numerical studies show that the computational savings can be significant especially when the discount factor approaches one and the transition probability matrix becomes dense, in which the standard value iteration algorithm and its variants suffer from slow convergence.