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Information Theoretic Learning Methods for Markov Decision Processes With Parametric Uncertainty
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Information Theoretic Learning Methods for Markov Decision Processes With Parametric Uncertainty. |
| 개인저자 | Kumar, Peeyush. |
| 단체저자명 | University of Washington. Industrial and Systems Engineering. |
| 발행사항 | [S.l.]: University of Washington., 2018. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2018. |
| 형태사항 | 126 p. |
| 기본자료 저록 | Dissertation Abstracts International 79-12B(E). Dissertation Abstract International |
| ISBN | 9780438177499 |
| 학위논문주기 | Thesis (Ph.D.)--University of Washington, 2018. |
| 일반주기 |
Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.
Adviser: Archis V. Ghate. |
| 요약 | Markov decision processes (MDPs) model a class of stochastic sequential decision problems with applications in engineering, medicine, and business analytics. There is considerable interest in the literature in MDPs with imperfect information, wh |
| 요약 | In the first part, the value of a parameter that characterizes the transition probabilities is unknown to the decision-maker. Information Directed Policy Sampling (IDPS) is proposed to manage the exploration-exploitation trade-off. A generalizat |
| 요약 | Uncertainty in transition probabilities arises from two levels in the second part. The top level corresponds to the ambiguity about the system model. Bottom-level uncertainty is rooted in the unknown parameter values for each possible model. Pri |
| 요약 | The third part extends the above to partially observable Markov decision processes (POMDPs). A connection between POMDPs and the first two chapters is exploited to devise algorithms and provide analytical performance guarantees in three cases: a |
| 요약 | The fourth part develops a formal information theoretic framework inspired by stochastic thermodynamics. It utilizes the idea that information is physical. An explicit link between information entropy and stochastic dynamics of a system coupled |
| 일반주제명 | Operations research. Artificial intelligence. Computer science. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
