scholarly journals Extreme point characterization of constrained nonstationary infinite-horizon Markov decision processes with finite state space

2014 ◽  
Vol 42 (3) ◽  
pp. 238-245
Author(s):  
Ilbin Lee ◽  
Marina A. Epelman ◽  
H. Edwin Romeijn ◽  
Robert L. Smith
Keyword(s):  
State Space ◽  
Extreme Point ◽  
Markov Decision Processes ◽  
Infinite Horizon ◽  
Decision Processes ◽  
Finite State ◽  
Markov Decision ◽  
Finite State Space

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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