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- W4308156983 abstract "In this paper, a routing method based on reinforcement learning (RL) under software-defined networks (SDN), namely the Q-learning widest-path routing algorithm (Q-WPRA), is proposed. This algorithm processes the reward function according to the link bandwidth in the execution environment to find the optimal (i.e., widest) transmission path with the maximum bandwidth between the source and the destination through RL. The experimental results reveal that the Q-WPRA is outperformance than Dijkstra’s algorithm and Dijkstra’s widest-path algorithm to find the widest transmission path in SDN environment under different bandwidths, loss rates, and background traffic." @default.
- W4308156983 created "2022-11-08" @default.
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- W4308156983 date "2022-11-01" @default.
- W4308156983 modified "2023-09-30" @default.
- W4308156983 title "A reinforcement learning approach for widest path routing in software-defined networks" @default.
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- W4308156983 doi "https://doi.org/10.1016/j.icte.2022.10.007" @default.
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