Loading...

Markov Decision Process with Timeconsuming Transition

Qarehdaghi, Hassan | 2020

807 Viewed
  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 53736 (02)
  4. University: Sharif University of Technology
  5. Department: Mathematical Sciences
  6. Advisor(s): Alishahi, Kasra
  7. Abstract:
  8. Mankind according to his authority (or delusion of authority) always finds himself in a situation which need decision-¬making. Usually, he seeks to make the best possible decision. The basis for measuring the goodness of choices is different in different occasions. This measure could be level of enjoyment, economic profit, probability of reaching a goal, etc. These decisions have consequences such that the situations before and after the decisions are not the same. Most challenging decision¬-making situations are those which the decision¬maker has not the complete authority over the situation and the results of decisions are influenced by out of control factors. A significant part of decision¬-making situations, have Markov property, which means that the dependence of future events to the past, is by the present time. For instance, the next move of our opponent in a chess game, only depends on the current arrangement of the pieces, not the past arrangements. Markov decision process, is a mathematical framework which can model these problems. In Some situations, the Markov property is not established generally, but only in decision epochs. These problems are considered in semi-¬Markov decision process framework. According to wide applicability of these models in many contexts, there was a necessity for existence of a writing which introduces these models. Furthermore, one of important duties of mathematical department is to introduce and explain these applicable models to the students of other fields. This writing is an effort to introduce the semi¬-Markov decision process. I hope this could be helpful for students and researchers who try to understand and find application in this field
  9. Keywords:
  10. Markov Decision Making ; Dynamic Programming ; Hidden Semi-Markov Model ; Constrained Markov Decision Process ; Markov Chain Approximation Method

 Digital Object List