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