Markov Decision Processes with Discounted Rewards: Improved Successive Over-Relaxation Method

2022 ◽  
pp. 229-238
Author(s):  
Abdellatif Semmouri ◽  
Mostafa Jourhmane ◽  
Bahaa Eddine Elbaghazaoui
Keyword(s):  
Markov Decision Processes ◽  
Relaxation Method ◽  
Decision Processes ◽  
Discounted Rewards ◽  
Markov Decision ◽  
Successive Over Relaxation

Extreme-point solutions in Markov decision processes

1983 ◽  
Vol 20 (04) ◽  
pp. 835-842
Author(s):  
David Assaf
Keyword(s):  
Convex Function ◽  
Extreme Point ◽  
Markov Decision Processes ◽  
Convex Functions ◽  
Sufficient Conditions ◽  
Decision Processes ◽  
Markov Decision ◽  
Full Solution

The paper presents sufficient conditions for certain functions to be convex. Functions of this type often appear in Markov decision processes, where their maximum is the solution of the problem. Since a convex function takes its maximum at an extreme point, the conditions may greatly simplify a problem. In some cases a full solution may be obtained after the reduction is made. Some illustrative examples are discussed.

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