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VALUE APPROXIMATION FUNCTIONS FOR PARTIALLY OBSERVED MARKOV DECISION PROCESSES

Pisareva Natalia Dmitrievna  (Kursk State University Graduate student of the Department of Software and Administration of Information Systems )

This paper explores the problem of applying value approximation functions in the context of partially observable Markov decision processes (POMDPs, or POMDPs). POMDPs are a powerful tool for modeling situations in which decision making is based on imperfect information and the possible consequences of those decisions.

Keywords:Markov solutions, POMDP, process modeling
 

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Citation link:
Pisareva N. D. VALUE APPROXIMATION FUNCTIONS FOR PARTIALLY OBSERVED MARKOV DECISION PROCESSES // Современная наука: актуальные проблемы теории и практики. Серия: Естественные и Технические Науки. -2023. -№08. -С. 100-102 DOI 10.37882/2223-2966.2023.08.26
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