scholarly journals Markov decision processes with restricted observations: Finite horizon case

1997 ◽  
Vol 44 (5) ◽  
pp. 439-456 ◽  
Author(s):  
Yasemin Serin ◽  
Zeynep Muge Avsar
Keyword(s):  
Markov Decision Processes ◽  
Decision Processes ◽  
Finite Horizon ◽  
Markov Decision ◽  
Horizon Case

scholarly journals Lexicographic refinements in possibilistic decision trees and finite-horizon Markov decision processes

2019 ◽  
Vol 366 ◽  
pp. 85-109
Author(s):  
Nahla Ben Amor ◽  
Zeineb El Khalfi ◽  
Hélène Fargier ◽  
Régis Sabbadin
Keyword(s):  
Decision Trees ◽  
Markov Decision Processes ◽  
Decision Processes ◽  
Finite Horizon ◽  
Markov Decision

Functional Reward Markov Decision Processes: Theory and Applications

2017 ◽  
Vol 26 (03) ◽  
pp. 1760014
Author(s):  
Paul Weng ◽  
Olivier Spanjaard
Keyword(s):  
Markov Decision Processes ◽  
Infinite Horizon ◽  
Standard Form ◽  
Sufficient Conditions ◽  
Decision Processes ◽  
Markov Decision ◽  
Standard Models ◽  
Reward Functions ◽  
Planning Problems ◽  
Horizon Case

Markov decision processes (MDP) have become one of the standard models for decisiontheoretic planning problems under uncertainty. In its standard form, rewards are assumed to be numerical additive scalars. In this paper, we propose a generalization of this model allowing rewards to be functional. The value of a history is recursively computed by composing the reward functions. We show that several variants of MDPs presented in the literature can be instantiated in this setting. We then identify sufficient conditions on these reward functions for dynamic programming to be valid. We also discuss the infinite horizon case and the case where a maximum operator does not exist. In order to show the potential of our framework, we conclude the paper by presenting several illustrative examples.

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