Dynamic Bayesian Networks for State- and Action-Space Modelling in Reinforcement Learning
Електронного архіву Харківського національного університету радіоелектроніки (Open Access Repository of KHNURE)
Переглянути архів Інформація| Поле | Співвідношення | |
| Title |
Dynamic Bayesian Networks for State- and Action-Space Modelling in Reinforcement Learning
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| Creator |
Леховицький, Д. І.
Ховрат, А. В. |
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| Subject |
Markov Decision Process
Dynamic Bayesian networks Reinforcement Learning |
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| Description |
In recent years Reinforcement Learning has proven its efficiency in solving problems of sequential decision making, formalized with a concept called Markov Decision Process. Though, there is a lot of problems: high computational complexity for multivariate state- and action-space problems, needs to handle missing data and hidden variables, lack of both good model and a sufficient number of episodes for constructing an optimal policy. In this work we suggest Dynamic Bayesian networks (DBNs) as a solution. These models provide an elegant and compact representation of joint state-action space, efficient inference algorithms, which include Monte-Carlo methods and Belief Propagation, and can be used in Dyna-Q Algorithm for integrating real-world and simulated experience. |
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| Date |
2018-06-05T12:16:44Z
2018-06-05T12:16:44Z 2018 |
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| Type |
Thesis
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| Identifier |
Lekhovitsky D., Khovrat A. Dynamic Bayesian Networks for State- and Action-Space Modelling in Reinforcement Learning / D. Lekhovitsky, A. Khovrat // Радіоелектроніка та молодь у XXI столітті : матеріали 22-го Міжнар. молодіжного форуму, 17–19 апр. 2018 г. – Харків : ХНУРЕ, 2018. – С. 118–119.
http://openarchive.nure.ua/handle/document/5806 |
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| Language |
en
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| Publisher |
ХНУРЕ
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