RISK-SENSITIVE LQR PROBLEMS WITH EXPONENTIAL NOISE
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Date
2024-04-26
Authors
Shortanbaiuly, Olzhas
Journal Title
Journal ISSN
Volume Title
Publisher
Nazarbayev University School of Sciences and Humanities
Abstract
This thesis is about optimal control of Markov Decision Processes and solving risk-sensitive cost minimization and reward maximization problems, specifically, the Linear Quadratic Regulator (LQR) problem with Average-Value-at-Risk criteria. The problem is solved for different risk levels, different random noises (theoretical and sampled), and using different methods: analytical and approximate dynamic programming. The obtained results were analyzed and discussed for the presence of certain patterns and trends. The results show that approximate dynamic programming is a very accurate method for solving risk-sensitive LQR problems with exponential noise.
Description
Keywords
LQR problem, Markov Decision Process, Average-Value-at-Risk, Approximate Dynamic Programming, Exponential Distribution, Type of access: Open Access
Citation
Shortanbaiuly, O. (2024). Risk-sensitive LQR problems with exponential noise. Nazarbayev University School of Sciences and Humanities