MARKOVIAN DECISION PROCESSES WITH UNCERTAIN TRANSITION PROBABILITIES

Abstract

A dynamic programming formulation for the Markovian decision process when transition probabilities are unknown is proposed. This formulation is used to solve simple problems, but is shown to be too difficult to apply to more complex systems. Various approximate methods are then proposed and discussed. A simple approximating algorithm is finally presented.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Mar 01, 1965
Accession Number
AD0612601

Entities

People

  • John M. Gozzolino
  • Ralph L. Miller
  • Romulo Gonzalez-zubieta

Organizations

  • Massachusetts Institute of Technology

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Abstracts
  • Algorithms
  • Complex Systems
  • Computational Science
  • Computations
  • Computer Programming
  • Computer Programs
  • Computers
  • Dynamic Programming
  • Equations
  • Heuristic Methods
  • Markov Processes
  • Operations Research
  • Random Variables
  • Sampling
  • Simulations
  • Statistics

Fields of Study

  • Mathematics

Readers

  • Linear Algebra
  • Team-Based Human-Centered Cognitive Task Decision Making and Information Performance.
  • Theoretical Analysis.