Risk-sensitive Semi-Markov Decision Problems with Discounted Cost and General Utilities

2021/01/12 by Arnab Bhabak, Bhabak, Arnab, Subhamay Saha +1
Engineering · Mathematics · #Advanced Control Systems Optimization #Algorithm #Artificial intelligence #Bellman equation #Computer science #Control (management) #FOS: Mathematics #Finite state #Horizon #Markov chain #Markov decision process #Markov process #Mathematical economics #Mathematical optimization #Mathematics #Optimal control #Optimization and Control (math.OC) #State (computer science) #State space #Statistics #Time horizon #math.OC

paper · pdf · doi:10.48550/arxiv.2101.04510

published in arXiv (Cornell University) (Cornell University)

arxiv created 2021/01/12 · openalex publication_date 2021/01/12 · arxiv updated 2021/01/13 · openalex created_date 2022/07/25 · openalex updated_date 2026/08/05

Abstract

In this article we consider risk-sensitive control of semi-Markov processes with a discrete state space. We consider general utility functions and discounted cost in the optimization criteria. We consider random finite horizon and infinite horizon problems. Using a state augmentation technique we characterise the value functions and also prescribe optimal controls.

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