Reduction of a Markov decision process with non-linear discounting to a stochastic game with standard total undiscounted criterion
Year: 2025
Type: article
Abstract: We consider a Markov decision process (MDP), whose total discounted utility is aggregated recursively with a concave discount function that is not necessarily linear. The state and action spaces are Borel spaces, and the utility function is nonnegative. We show that it can be reduced to a turn-based stochastic game model with the total undiscounted utility. This reduction result is then applied to the MDP problem with recursively aggregated utility to be maximized or cost to be minimized.
Source: Annals of Operations Research
Authors: Alexey Piunovskiy, Ernst Presman, Yi Zhang, Xinran Zheng
Language: English
Topic: Game Theory and Applications
Field: Decision Sciences
Domain: Social Sciences
Open Access status: hybrid
Estimated APC paid: $3,090
OpenAlex ID: W4408282749