SOLVING LINEAR-QUADRATIC REGULATOR PROBLEM WITH AVERAGE-VALUE-AT-RISK CRITERIA USING APPROXIMATE DYNAMIC PROGRAMMING

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dc.contributor.authorRaikhankyzy, Arailym
dc.date.accessioned2024-06-07T06:35:04Z
dc.date.available2024-06-07T06:35:04Z
dc.date.issued2024-04-26
dc.description.abstractThis master’s thesis explores the intersection of optimal control theory and risk-sensitive decision-making by addressing the finite-horizon discrete-time linear quadratic regulator (LQR) problem with a focus on the average-value-at-risk (AVaR) criteria. The study aims to mathematically formalize the LQR-AVaR problem within the dynamic programming framework and develop a computational algorithm based on approximate dynamic programming techniques to solve it. The algorithm’s effectiveness is rigorously assessed through the analysis of experiment results and plot evaluations. The experiment results indicate that the approximate dynamic programming algorithm, when applied properly, performs well for the problem, with experiments suggesting high accuracy.en_US
dc.identifier.citationRaikhankyzy, A. (2024) Solving Linear-Quadratic Regulator Problem with Average-Value-at-Risk Criteria using Approximate Dynamic Programming. Nazarbayev University School of Sciences and Humanitiesen_US
dc.identifier.urihttp://nur.nu.edu.kz/handle/123456789/7775
dc.language.isoenen_US
dc.publisherNazarbayev University School of Sciences and Humanitiesen_US
dc.rightsAttribution 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/us/*
dc.subjectType of access: Open Accessen_US
dc.subjectoptimal control theoryen_US
dc.subjectrisk-sensitive decision-makingen_US
dc.subjectlinear quadratic regulatoren_US
dc.subjectAverage-Value-at-Risken_US
dc.subjectapproximate dynamic programmingen_US
dc.titleSOLVING LINEAR-QUADRATIC REGULATOR PROBLEM WITH AVERAGE-VALUE-AT-RISK CRITERIA USING APPROXIMATE DYNAMIC PROGRAMMINGen_US
dc.typeMaster's thesisen_US
workflow.import.sourcescience

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