Converging Markov Decision Processes with Multiplicative Reward System

Toshiharu Fujita

doi:10.18997/00009112

March 31, 2023Open Access

Converging Markov Decision Processes with Multiplicative Reward System

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Key Points

This research aims to explore a multiplicative reward system in converging Markov decision processes under predetermined decision orders.
Developed a recursive method using dynamic programming.
Analyzed decision processes in a finite-stage stochastic environment.
Assumed predetermined order of decision-making.
Presented a new algorithm for calculating rewards in Markov processes.
Showed that predetermined decision order impacts the efficacy of the reward system.
Demonstrated the applicability of the method in complex decision scenarios.

Implication

This research demonstrates a recursive method for decision-making in a stochastic environment, indicating new strategies in decision processes.

Abstract

We consider multiplicative reward system on a finite-stage converging decision process in stochastic environment. In this paper, we give a recursive method by dynamic programming under the assumption that the order in which decisions are taken is predetermined.

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Cite This Study

Toshiharu Fujita (2023) studied this question.

synapsesocial.com/papers/69cd7ab35652765b073a8146 https://doi.org/10.18997/00009112

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Converging Markov Decision Processes with Multiplicative Reward System

Key Points

Implication

Abstract

Discussion

Cite This Study

Converging Markov Decision Processes with Multiplicative Reward System

Key Points

Implication

Abstract

Discussion

Cite This Study

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