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RAPID CONVERGENCE TECHNIQUES FOR MARKOV DECISION PROCESSES *
Author(s) -
Zaldivar Miguel,
Hodgson Thom J.
Publication year - 1975
Publication title -
decision sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.238
H-Index - 108
eISSN - 1540-5915
pISSN - 0011-7315
DOI - 10.1111/j.1540-5915.1975.tb00993.x
Subject(s) - convergence (economics) , markov decision process , computer science , markov chain , mathematical optimization , rate of convergence , markov process , scale (ratio) , white paper , algorithm , mathematics , machine learning , key (lock) , statistics , economics , physics , computer security , archaeology , quantum mechanics , history , economic growth
When a person is working with large scale Markov Decision Processes, he normally uses the policy iteration approach developed by Howard [1] and modified by White [3]. White's modification makes use of the method of successive approximations. Computational experience has shown that for many processes, the rate of convergence of the successive approximation is very slow. In this paper, techniques for speeding convergence are discussed. Numerical examples and computational experience which show the relative merits of the various approaches are presented.