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

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: Tables(data); Convergence. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1985
Accession Number
ADA159950

Entities

People

  • F. Y. Ding
  • R. E. King
  • Thom J. Hodgson

Organizations

  • North Carolina State University

Tags

DTIC Thesaurus Topics

  • Algorithms
  • Computations
  • Computer Programming
  • Convergence
  • Dynamic Programming
  • Engineering
  • Equations
  • Inventory
  • Linear Programming
  • Markov Chains
  • Markov Processes
  • Observation
  • Operations Research
  • Probability
  • Production
  • Random Variables
  • Systems Engineering

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Operations Research
  • Team-Based Human-Centered Cognitive Task Decision Making and Information Performance.

Technology Areas

  • Space