| LDR | | 02765nam u200469 4500 |
| 001 | | 000000422389 |
| 005 | | 20190215170104 |
| 008 | | 181129s2018 |||||||||||||||||c||eng d |
| 020 | |
▼a 9780438177499 |
| 035 | |
▼a (MiAaPQ)AAI10828591 |
| 035 | |
▼a (MiAaPQ)washington:18875 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 247004 |
| 082 | 0 |
▼a 658 |
| 100 | 1 |
▼a Kumar, Peeyush. |
| 245 | 10 |
▼a Information Theoretic Learning Methods for Markov Decision Processes With Parametric Uncertainty. |
| 260 | |
▼a [S.l.]:
▼b University of Washington.,
▼c 2018. |
| 260 | 1 |
▼a Ann Arbor:
▼b ProQuest Dissertations & Theses,
▼c 2018. |
| 300 | |
▼a 126 p. |
| 500 | |
▼a Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B. |
| 500 | |
▼a Adviser: Archis V. Ghate. |
| 502 | 1 |
▼a Thesis (Ph.D.)--University of Washington, 2018. |
| 520 | |
▼a 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 |
| 520 | |
▼a 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 |
| 520 | |
▼a 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 |
| 520 | |
▼a 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 |
| 520 | |
▼a 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 |
| 590 | |
▼a School code: 0250. |
| 650 | 4 |
▼a Operations research. |
| 650 | 4 |
▼a Artificial intelligence. |
| 650 | 4 |
▼a Computer science. |
| 690 | |
▼a 0796 |
| 690 | |
▼a 0800 |
| 690 | |
▼a 0984 |
| 710 | 20 |
▼a University of Washington.
▼b Industrial and Systems Engineering. |
| 773 | 0 |
▼t Dissertation Abstracts International
▼g 79-12B(E). |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0250 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2018 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T14999187
▼n KERIS
▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
| 980 | |
▼a 201812
▼f 2019 |
| 990 | |
▼a ***1012033 |