University of Bahrain
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Hierarchical Reinforcement Learning: A Survey

Show simple item record Al-Emran, Mostafa 2018-07-22T10:16:33Z 2018-07-22T10:16:33Z 2015
dc.identifier.issn 2210-142X
dc.description.abstract Reinforcement Learning (RL) has been an interesting research area in Machine Learning and AI. Hierarchical Reinforcement Learning (HRL) that decomposes the RL problem into sub-problems where solving each of which will be more powerful than solving the entire problem will be our concern in this paper. A review of the state-of-the-art of HRL has been investigated. Different HRL-based domains have been highlighted. Different problems in such different domains along with some proposed solutions have been addressed. It has been observed that HRL has not yet been surveyed in the current existing research; the reason that motivated us to work on this paper. Concluding remarks are presented. Some ideas have been emerged during the work on this research and have been proposed for pursuing a future research. en_US
dc.language.iso en en_US
dc.publisher University of Bahrain en_US
dc.rights Attribution-NonCommercial-ShareAlike 4.0 International *
dc.rights.uri *
dc.subject Reinforcement Learning en_US
dc.subject Hierarchical Reinforcement en_US
dc.subject Learning en_US
dc.subject Q-learning en_US
dc.title Hierarchical Reinforcement Learning: A Survey en_US
dc.type Article en_US
dc.volume 04
dc.issue 02
dc.source.title International Journal of Computing and Digital Systems
dc.abbreviatedsourcetitle IJCDS

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