Публікація: Dynamic Bayesian Networks for State- and Action-Space Modelling in Reinforcement Learning
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Дата
2018
Автори
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Видавництво
ХНУРЕ
Анотація
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.
Опис
Ключові слова
Markov Decision Process, Dynamic Bayesian networks, Reinforcement Learning
Бібліографічний опис
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.