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Analysis and Solution of Markov Deci...

ProQuest Information and Learning Co.

Analysis and Solution of Markov Decision Problems with a Continuous, Stochastic State Component.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Analysis and Solution of Markov Decision Problems with a Continuous, Stochastic State Component./
作者:	Sukumar, Shruthi.
面頁冊數:	1 online resource (51 pages)
附註:	Source: Masters Abstracts International, Volume: 57-01.
Contained By:	Masters Abstracts International57-01(E).
標題:	Electrical engineering. -
電子資源:	click for full text (PQDT)
ISBN:	9780355300581

Analysis and Solution of Markov Decision Problems with a Continuous, Stochastic State Component.
Sukumar, Shruthi.

Analysis and Solution of Markov Decision Problems with a Continuous, Stochastic State Component. - 1 online resource (51 pages)

Source: Masters Abstracts International, Volume: 57-01.

Thesis (M.S.)--University of Colorado at Boulder, 2017.

Includes bibliographical references

Markov Decision Processes (MDPs) are discrete-time random processes that provide a framework to model sequential decision problems in stochastic environments. However, the use of MDPs to model real-world decision problems is restricted, since they often have continuous variables as part of their state space. Common approaches to extending the use of MDPs to solve these problems include discretization which suffers from inefficiency and inaccuracy. Here, we solve MDPs with continuous and discrete state variables by assuming the reward to be piecewise linear. We however allow for the continuous variable to have an infinite and continuous transition function. We then use our approach to solve an MDP modeling human behaviour in a specific task called delayed gratification. Simulation results are presented to analyze the model predictions which are fit post-hoc to synthetic as well as human data, to justify the approach solving the MDP and modeling behaviour.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355300581Subjects--Topical Terms:

596380
Electrical engineering.
Index Terms--Genre/Form:

554714
Electronic books.

Analysis and Solution of Markov Decision Problems with a Continuous, Stochastic State Component.
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300 $a 1 online resource (51 pages)
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500 $a Source: Masters Abstracts International, Volume: 57-01.
500 $a Adviser: Jason R. Marden.
502 $a Thesis (M.S.)--University of Colorado at Boulder, 2017.
504 $a Includes bibliographical references
520 $a Markov Decision Processes (MDPs) are discrete-time random processes that provide a framework to model sequential decision problems in stochastic environments. However, the use of MDPs to model real-world decision problems is restricted, since they often have continuous variables as part of their state space. Common approaches to extending the use of MDPs to solve these problems include discretization which suffers from inefficiency and inaccuracy. Here, we solve MDPs with continuous and discrete state variables by assuming the reward to be piecewise linear. We however allow for the continuous variable to have an infinite and continuous transition function. We then use our approach to solve an MDP modeling human behaviour in a specific task called delayed gratification. Simulation results are presented to analyze the model predictions which are fit post-hoc to synthetic as well as human data, to justify the approach solving the MDP and modeling behaviour.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Electrical engineering. $3 596380
655 7 $a Electronic books. $2 local $3 554714
690 $a 0544
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a University of Colorado at Boulder. $b Electrical Engineering. $3 1148661
773 0 $t Masters Abstracts International $g 57-01(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10604299 $z click for full text (PQDT)

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