MACHINE LEARNING TECHNIQUES APPLIED TO ROBUST OPTIMAL CONTROL PROBLEMS
Loading...
Date
2024-04-19
Authors
Zhangunissov, Dilzhan
Journal Title
Journal ISSN
Volume Title
Publisher
Nazarbayev University School of Sciences and Humanities
Abstract
This project aims to solve the discrete time stochastic optimal control problem of evaluation of Average Value-at-Risk (AVaR) function. AVaR is an important tool in market risk management used to measure the risk. In the paper it was designed as a sequential decision model and solved by formulating an optimal control problem of minimizing the value. Brute force and Approximate Dynamic Programming (ADP) techniques were used for exact and approximate solutions respectively. Golden section search was used to solve the problem completely. The numerical experiments conducted at the end showed the effectiveness of the algorithm in evaluating the AVaR.
Description
Keywords
Type of access: Open Access, approximate dynamic programming, average value-at-risk, optimal control, Markov decision processes
Citation
Zhangunissov, D. (2024). Machine Learning Techniques Applied To Robust Optimal Control Problems. Nazarbayev University School of Sciences and Humanities