Shortanbaiuly, Olzhas2024-06-072024-06-072024-04-26Shortanbaiuly, O. (2024). Risk-sensitive LQR problems with exponential noise. Nazarbayev University School of Sciences and Humanitieshttp://nur.nu.edu.kz/handle/123456789/7791This 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.enAttribution-NonCommercial-ShareAlike 3.0 United StatesLQR problemMarkov Decision ProcessAverage-Value-at-RiskApproximate Dynamic ProgrammingExponential DistributionType of access: Open AccessMaster's thesis