Accession Number:

ADA159950

Title:

A Methodology for Computation Reduction for Specially Structured Large Scale Markov Decision Problems.

Descriptive Note:

Technical rept.,

Corporate Author:

NORTH CAROLINA STATE UNIV AT RALEIGH DEPT OF INDUSTRIAL ENGINEERING

Personal Author(s):

Report Date:

1985-01-01

Pagination or Media Count:

28.0

Abstract:

Markov Decision Processes 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 practical problems have extremely large state and action spaces making them computationally intractable. Typically, the state variable definition is n-dimensional and the number of states expands at a rate proportional to the power of n. For such large problems, the need for large amounts of random access memory and computation time restricts the ability to obtain solutions. The purpose of this paper is to both present a methodology which facilitates the solution of large scale problems, and provide computational results indicating the value of the approach. Additional keywords Tablesdata Convergence. Author

Subject Categories:

  • Statistics and Probability

Distribution Statement:

APPROVED FOR PUBLIC RELEASE