Reinforcement Structural Learning
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F15%3A10318695" target="_blank" >RIV/00216208:11320/15:10318695 - isvavai.cz</a>
Result on the web
<a href="http://wupes.fm.vse.cz/2015/data/Proceedings.pdf" target="_blank" >http://wupes.fm.vse.cz/2015/data/Proceedings.pdf</a>
DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Reinforcement Structural Learning
Original language description
This article shows a novel approach to modelling and reinforcement learning of dynamic stochastic partially observable environment. We present an MCMC algorithm which learns the structure of a graphical model representing the environment. We use an approximation to a Bayesian method to learn posterior distribution over parameters of learned structure. The learning algorithm is online which allows us to use it in reinforcement learning setup. We demonstrate that this algorithm is usable on several simpleexperiments.
Czech name
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Czech description
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Classification
Type
O - Miscellaneous
CEP classification
IN - Informatics
OECD FORD branch
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Result continuities
Project
<a href="/en/project/GAP103%2F10%2F1287" target="_blank" >GAP103/10/1287: PlanEx: Bridging Planning and Execution</a><br>
Continuities
S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2015
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů