What question did this study set out to answer?

The aim is to analyze the performance of different deep reinforcement learning (DRL) algorithms in traffic engineering within software-defined networking (SDN).

March 28, 2026Open Access

Comparative Performance Analysis Of DRL Algorithms For Intelligent Traffic Engineering In SDN

Key Points

The aim is to analyze the performance of different deep reinforcement learning (DRL) algorithms in traffic engineering within software-defined networking (SDN).
Comparative analysis of various DRL algorithms.
Evaluation criteria include efficiency and adaptability to traffic conditions.
Application of findings in intelligent traffic management systems.
Identified key strengths and weaknesses of each DRL algorithm under different traffic scenarios.
Demonstrated that certain algorithms significantly improve traffic flow and reduce congestion.
Results indicate potential for enhanced real-time traffic management in SDN.

Abstract

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Cite This Study

Singh et al. (Thu,) studied this question.

synapsesocial.com/papers/69c771348bbfbc51511e10f2 https://doi.org/https://doi.org/10.56975/ijcrt.v14i3.303709

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