Stochastic Optimization in a Cumulative Prospect Theory Framework

被引:23
|
作者
Jie, Cheng [1 ]
Prashanth, L. A. [2 ,3 ]
Fu, Michael [2 ,4 ]
Marcus, Steve [2 ,5 ]
Szepesvari, Csaba [6 ]
机构
[1] Univ Maryland, Dept Math, College Pk, MD 20742 USA
[2] Univ Maryland, Inst Syst Res, College Pk, MD 20742 USA
[3] Indian Inst Technol Madras, Dept Comp Sci & Engn, Madras 600036, Tamil Nadu, India
[4] Univ Maryland, Robert H Smith Sch Business, College Pk, MD 20742 USA
[5] Univ Maryland, Dept Elect & Comp Engn, College Pk, MD 20742 USA
[6] Univ Alberta Edmonton, Dept Comp Sci, Edmonton, AB T6G 2R3, Canada
基金
美国国家科学基金会; 加拿大自然科学与工程研究理事会;
关键词
Cumulative prospect theory (CPT); reinforcement learning; simultaneous perturbation stochastic approximation (SPSA); stochastic optimization; DECISION; VARIANCE; APPROXIMATION; RISK;
D O I
10.1109/TAC.2018.2822658
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Cumulative prospect theory (CPT) is a popular approach for modeling human preferences. It is based on probabilistic distortions and generalizes the expected utility theory. We bring the CPT to a stochastic optimization framework and propose algorithms for both estimation and optimization of CPT-value objectives. We propose an empirical distribution function-based scheme to estimate the CPT value, and then, use this scheme in the inner loop of a CPT-value optimization procedure. We propose both gradient based as well as gradient-free CPT-value optimization algorithms that are based on two well-known simulation optimization ideas: simultaneous perturbation stochastic approximation and model-based parameter search, respectively. We provide theoretical convergence guarantees for all the proposed algorithms and also illustrate the potential of CPT-based criteria in a traffic signal control application.
引用
收藏
页码:2867 / 2882
页数:16
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