Multiple-Environment Markov Decision Processes: Efficient Analysis and Applications
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F20%3A00114616" target="_blank" >RIV/00216224:14330/20:00114616 - isvavai.cz</a>
Result on the web
<a href="https://ojs.aaai.org//index.php/ICAPS/article/view/6644" target="_blank" >https://ojs.aaai.org//index.php/ICAPS/article/view/6644</a>
DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Multiple-Environment Markov Decision Processes: Efficient Analysis and Applications
Original language description
Multiple-environment Markov decision processes (MEMDPs) are MDPs equipped with not one, but multiple probabilistic transition functions, which represent the various possible unknown environments. While the previous research on MEMDPs focused on theoretical properties for long-run average payoff, we study them with discounted-sum payoff and focus on their practical advantages and applications. MEMDPs can be viewed as a special case of Partially observable and Mixed observability MDPs: the state of the system is perfectly observable, but not the environment. We show that the specific structure of MEMDPs allows for more efficient algorithmic analysis, in particular for faster belief updates. We demonstrate the applicability of MEMDPs in several domains. In particular, we formalize the sequential decision-making approach to contextual recommendation systems as MEMDPs and substantially improve over the previous MDP approach.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10200 - Computer and information sciences
Result continuities
Project
<a href="/en/project/GJ19-15134Y" target="_blank" >GJ19-15134Y: Verification and Analysis of Probabilistic Programs</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the International Conference on Automated Planning and Scheduling
ISBN
9781577358244
ISSN
2334-0835
e-ISSN
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Number of pages
9
Pages from-to
48-56
Publisher name
AAAI Press
Place of publication
Palo Alto
Event location
Nancy, Francie
Event date
Jan 1, 2020
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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