Applying reinforcement learning for random early detection algorithm in adaptive queue management systems

Ayman Basheer Yousif, Hassan Jaleel Hassan, Gaida Muttasher


Recently, the use of internet has been increased all around the hose, the companies, government departments and the video games and so on. Thus, this increased the traffic used in the networks, which generated congestion issues and sent packet drop in the nodes. To solve this problem, certain algorithms are used. The Active queue management is one of the most important algorithms that helps with this issue. For an effective network management, the RL was used, and it will adapt with the parameters of algorithms. Where the suggested algorithm deep Q-networks (DQN) depends on the reinforcement learning (RL) to reduce the drop and delay. Also, the random early detection (RED) (an active queue management (AQM) algorithm) was adopted based on the NS3 situation.


Active queue management; Deep Q-learning; Random early detection; Reinforcement learning;

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The Indonesian Journal of Electrical Engineering and Computer Science (IJEECS)
p-ISSN: 2502-4752, e-ISSN: 2502-4760
This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).

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