Timeslot Scheduling with Reinforcement Learning Using a Double Deep Q-Network 


Vol. 47,  No. 7, pp. 944-952, Jul.  2022
10.7840/kics.2022.47.7.944


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  Abstract

To adopt reinforcement learning in the network scheduling area is getting more attention than ever, because of its flexibility to adapt to the dynamic changes in network traffic specifications and their requirements. In this study, a timeslot scheduling algorithm based on priority is designed using Double deep q-network (DDQN), a reinforcement learning algorithm. To evaluate the behavior of the DDQN agent, a reward function is defined based on the difference between the estimated delay and the deadline of packets transmitted at timeslot; and on the priority of packets. The simulation showed that the designed scheduling algorithm performs better than the existing algorithms such as the strict priority (SP) or weighted round robin (WRR) scheduler, in the sense that more packets have arrived within the deadline. By using the proposed DDQN-based scheduler, it is expected that autonomous network scheduling can be realized in the upcoming network framework.

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  Cite this article

[IEEE Style]

J. Ryu, J. Kwon, J. Joung, "Timeslot Scheduling with Reinforcement Learning Using a Double Deep Q-Network," The Journal of Korean Institute of Communications and Information Sciences, vol. 47, no. 7, pp. 944-952, 2022. DOI: 10.7840/kics.2022.47.7.944.

[ACM Style]

Jihye Ryu, Juhyeok Kwon, and Jinoo Joung. 2022. Timeslot Scheduling with Reinforcement Learning Using a Double Deep Q-Network. The Journal of Korean Institute of Communications and Information Sciences, 47, 7, (2022), 944-952. DOI: 10.7840/kics.2022.47.7.944.

[KICS Style]

Jihye Ryu, Juhyeok Kwon, Jinoo Joung, "Timeslot Scheduling with Reinforcement Learning Using a Double Deep Q-Network," The Journal of Korean Institute of Communications and Information Sciences, vol. 47, no. 7, pp. 944-952, 7. 2022. (https://doi.org/10.7840/kics.2022.47.7.944)

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pISSN : 1226 - 4717
eISSN : 2287 - 3880