Name
Title | ||
|---|---|---|
Experience-driven Congestion Control: When Multi-Path TCP Meets Deep Reinforcement Learning |
Abstract | ||
|---|---|---|
In this paper, we aim to study networking problems from a whole new perspective by leveraging emerging deep learning, to develop an experience-driven approach, which enables a network or a protocol to learn the best way to control itself from its own experience (e.g., runtime statistics data), just as a human learns a skill. We present design, implementation and evaluation of a deep reinforcement ... |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/JSAC.2019.2904358 | IEEE Journal on Selected Areas in Communications |
Keywords | Field | DocType |
Runtime,Mathematical model,Reinforcement learning,Protocols,Heuristic algorithms,Recurrent neural networks,Resource management | Computer science,Computer network,Recurrent neural network,Network congestion,Artificial intelligence,Deep learning,Novelty,Philosophy of design,Goodput,Linux kernel,Reinforcement learning | Journal |
Volume | Issue | ISSN |
37 | 6 | 0733-8716 |
Citations | PageRank | References |
16 | 0.66 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhiyuan Xu | 1 | 73 | 6.42 |
| Jian Tang | 2 | 1095 | 74.34 |
| Chengxiang Yin | 3 | 57 | 4.14 |
| Yanzhi Wang | 4 | 1082 | 136.11 |
| Guoliang Xue | 5 | 48 | 9.12 |