Distributed and Adaptive Traffic Engineering with Deep Reinforcement Learning-Reference-Cited by-同舟云学术

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Distributed and Adaptive Traffic Engineering with Deep Reinforcement Learning

Published:2021-06-25 Issue: Volume: Page:
ISSN:
Container-title:2021 IEEE/ACM 29th International Symposium on Quality of Service (IWQOS)
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Author:

Geng Nan,Xu Mingwei,Yang Yuan,Liu Chenyi,Yang Jiahai,Li Qi,Zhang Shize

Funder

National Natural Science Foundation of China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9521186/9521256/09521303.pdf?arnumber=9521303

Cited by 8 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. TITE: A transformer-based deep reinforcement learning approach for traffic engineering in hybrid SDN with dynamic traffic;Future Generation Computer Systems;2024-12

2. Transferable Neural WAN TE for Changing Topologies;Proceedings of the ACM SIGCOMM 2024 Conference;2024-08-04

3. Traffic Engineering in Large-scale Networks via Multi-Agent Deep Reinforcement Learning with Joint-Training;2024 33rd International Conference on Computer Communications and Networks (ICCCN);2024-07-29

4. MATE: When multi-agent Deep Reinforcement Learning meets Traffic Engineering in multi-domain networks;Computer Networks;2024-06

5. Teal: Learning-Accelerated Optimization of WAN Traffic Engineering;Proceedings of the ACM SIGCOMM 2023 Conference;2023-09

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