
Accession Number : AD0724753
Title : Markov Decision Processes with a New Optimality Criterion.
Descriptive Note : Technical rept.,
Corporate Author : STANFORD UNIV CALIF DEPT OF OPERATIONS RESEARCH
Personal Author(s) : Jaquette,Stratton C.
Report Date : MAY 1971
Pagination or Media Count : 108
Abstract : A Markov decision process can be characterized by specifying the following three elements: a Markov process on which a return function and decision structure is placed, an objective function or optimality criterion, and a class of allowable policies or controls. For a given Markov decision process with these three elements suitably defined, the standard problems to investigate are the following: The existence of a policy, within the class of allowable policies, which attains the maximal value of the objective function; The fact that the optimal policy has a simple form; The construction of a finite algorithm to compute the optimal policy. The report discusses these problems for standard Markov decision processes with a new optimality criterion. (Author)
Descriptors : (*DECISION THEORY, STOCHASTIC PROCESSES), OPTIMIZATION, SET THEORY, INTEGRAL TRANSFORMS, MATRICES(MATHEMATICS), RANDOM VARIABLES, ALGORITHMS
Subject Categories : Operations Research
Distribution Statement : APPROVED FOR PUBLIC RELEASE