Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 이주현 | - |
dc.date.accessioned | 2020-02-14T07:29:36Z | - |
dc.date.available | 2020-02-14T07:29:36Z | - |
dc.date.issued | 2019-06 | - |
dc.identifier.citation | International Conference on Information and Communication Technology Convergence (ICTC) | en_US |
dc.identifier.uri | https://www.semanticscholar.org/paper/Beyond-Max-weight-Scheduling-%3A-A-Reinforcement-Bae-Chong/6c963f8bb8015e01a190b73f834a9111f6dbfad1#citing-papers | - |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/125330 | - |
dc.description.abstract | As network architecture becomes complex and the user requirement gets diverse, the role of efficient network resource management becomes more important. However, existing network scheduling algorithms such as the max-weight algorithm suffer from poor delay performance. In this paper, we present a reinforcement learning-based network scheduling algorithm that achieves both optimal throughput and low delay. To this end, we first formulate the network optimization problem as an MDP problem. Then we introduce a new state-action value function called W-function and develop a reinforcement learning algorithm called W-learning that guarantees little performance loss during a learning process. Finally, via simulation, we verify that our algorithm shows delay reduction of up to 40.8% compared to the max-weight algorithm over various scenarios. | en_US |
dc.description.sponsorship | This work was supported by the ICT R&D program of MSICT/IITP. [2017-0-00045, Hyper-connected Intelligent Infrastructure Technology Development], the Engineering Research Center Program through the National Research Foundation of Korea (NRF) funded by the Korean Government MSIT (NRF-2018R1A5A1059921), and Institute for Information & communications Technology Promotion(IITP) grant funded by the Korea government(MSIT)(No.2016-0-00563, Research on Adaptive Machine Learning Technology Development for Intelligent Autonomous Digital Companion). Also this research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education(No. 2018R1D1A1B07045181). | en_US |
dc.language.iso | en_US | en_US |
dc.publisher | IEEE | en_US |
dc.title | Beyond Max-weight Scheduling: A Reinforcement Learning-based Approach | en_US |
dc.type | Article | en_US |
dc.relation.page | 1-1 | - |
dc.contributor.googleauthor | Bae, Jeongmin | - |
dc.contributor.googleauthor | Lee, Joohyun | - |
dc.contributor.googleauthor | Chong, Song | - |
dc.sector.campus | E | - |
dc.sector.daehak | COLLEGE OF ENGINEERING SCIENCES[E] | - |
dc.sector.department | DIVISION OF ELECTRICAL ENGINEERING | - |
dc.identifier.pid | joohyunlee | - |
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