A Game-Theoretical Approach to Markov Decision Processes, Stochastic Positional Games and Multicriteria Control Models

2014 ◽  
pp. 213-339
Author(s):  
Dmitrii Lozovanu ◽  
Stefan Pickl
Keyword(s):  
Markov Decision Processes ◽  
Theoretical Approach ◽  
Decision Processes ◽  
Positional Games ◽  
Control Models ◽  
Markov Decision ◽  
Game Theoretical Approach

Markov Decision Processes and Determining Nash Equilibria for Stochastic Positional Games

2011 ◽  
Vol 44 (1) ◽  
pp. 13398-13403 ◽  
Author(s):  
Dmitrii Lozovanu ◽  
Stefan Pickl ◽  
Erik Kropat
Keyword(s):  
Markov Decision Processes ◽  
Nash Equilibria ◽  
Decision Processes ◽  
Positional Games ◽  
Markov Decision

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.

scholarly journals Transfer-Entropy-Regularized Markov Decision Processes

2021 ◽  
pp. 1-1
Author(s):  
Takashi Tanaka ◽  
Henrik Sandberg ◽  
Mikael Skoglund
Keyword(s):  
Markov Decision Processes ◽  
Decision Processes ◽  
Transfer Entropy ◽  
Markov Decision
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