FINITE-MEMORY SUBOPTIMAL DESIGN FOR PARTIALLY OBSERVED MARKOV DECISION-PROCESSES
成果类型：
Article
署名作者：
WHITE, CC; SCHERER, WT
署名单位：
University of Michigan System; University of Michigan; University of Virginia
刊物名称：
OPERATIONS RESEARCH
ISSN/ISSBN：
0030-364X
DOI：
10.1287/opre.42.3.439
发表日期：
1994
页码：
439-455
关键词：
摘要：
We develop bounds on the value function and a suboptimal design for the partially observed Markov decision process. These bounds and suboptimal design are based on the M most recent observations and actions. An a priori measure of the quality of these bounds is given. We show that larger M implies tighter bounds. An operations count analysis indicates that (#A#Z)M+1(#S) multiplications and additions are required per successive approximations iteration of the suboptimal design algorithm, where A, Z, and S are the action, observation, and state spaces, respectively, suggesting the algorithm is of potential use for problems with large state spaces. A preliminary numerical study indicates that the quality of the suboptimal design can be excellent.