A Multi-objective Reinforcement Learning Perspective on Internet Congestion Control-Reference-Cited by-同舟云学术

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A Multi-objective Reinforcement Learning Perspective on Internet Congestion Control

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:

Xia Zhenchang,Chen Yanjiao,Wu Libing,Chou Yu-Cheng,Zheng Zhicong,Li Haoyang,Li Baochun

Funder

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9521186/9521256/09521291.pdf?arnumber=9521291

Cited by 5 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. End-to-End Congestion Control as Learning for Unknown Games with Bandit Feedback;2023 IEEE 43rd International Conference on Distributed Computing Systems (ICDCS);2023-07

2. Learned Internet Congestion Control for Short Video Uploading;Proceedings of the 30th ACM International Conference on Multimedia;2022-10-10

3. Glider: rethinking congestion control with deep reinforcement learning;World Wide Web;2022-06-27

4. ACCeSS: Adaptive QoS-aware Congestion Control for Multipath TCP;2022 IEEE/ACM 30th International Symposium on Quality of Service (IWQoS);2022-06-10

5. Application Placement in Fog Environments using Multi-Objective Reinforcement Learning with Maximum Reward Formulation;NOMS 2022-2022 IEEE/IFIP Network Operations and Management Symposium;2022-04-25

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