Partial Order Reduction for Markov Decision Processes: A Survey

2006 ◽  
pp. 408-427 ◽  
Author(s):  
Marcus Groesser ◽  
Christel Baier
Keyword(s):  
Partial Order ◽  
Markov Decision Processes ◽  
Order Reduction ◽  
Decision Processes ◽  
Partial Order Reduction ◽  
Markov Decision

Controlled Markov set-chains with discounting

1998 ◽  
Vol 35 (2) ◽  
pp. 293-302 ◽  
Author(s):  
Masami Kurano ◽  
Jinjie Song ◽  
Masanori Hosaka ◽  
Youqiang Huang
Keyword(s):  
Partial Order ◽  
Markov Decision Processes ◽  
Transition Probability ◽  
Decision Processes ◽  
New Model ◽  
Numerical Example ◽  
Markov Decision ◽  
Theoretical Results

In the framework of discounted Markov decision processes, we consider the case that the transition probability varies in some given domain at each time and its variation is unknown or unobservable.To this end we introduce a new model, named controlled Markov set-chains, based on Markov set-chains, and discuss its optimization under some partial order.Also, a numerical example is given to explain the theoretical results and the computation.

Controlled Markov set-chains with discounting

1998 ◽  
Vol 35 (02) ◽  
pp. 293-302 ◽  
Author(s):  
Masami Kurano ◽  
Jinjie Song ◽  
Masanori Hosaka ◽  
Youqiang Huang
Keyword(s):  
Partial Order ◽  
Markov Decision Processes ◽  
Transition Probability ◽  
Decision Processes ◽  
New Model ◽  
Numerical Example ◽  
Markov Decision ◽  
Theoretical Results

In the framework of discounted Markov decision processes, we consider the case that the transition probability varies in some given domain at each time and its variation is unknown or unobservable. To this end we introduce a new model, named controlled Markov set-chains, based on Markov set-chains, and discuss its optimization under some partial order. Also, a numerical example is given to explain the theoretical results and the computation.

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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