Multi-Objective Approaches to Markov Decision Processes with Uncertain Transition Parameters
Dimitri Scheftelowitsch, Peter Buchholz, Vahid Hashemi, Holger Hermanns · 2017
Markov decision processes (MDPs) are a popular model for performance analysis and optimization of stochastic systems. The parameters of stochastic behavior of MDPs are estimates from empirical observations of a system; their values are not known precisely. Different types of MDPs with uncertain, imprecise or bounded transition rates or probabilities and rewards exist in the literature.