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January 1, 2026INTERNATIONAL JOURNAL OF CREATIVE RESEARCH THOUGHTSOpen Access

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

Shweta Singh Kalpana Rai Dr. Vandana Rai Prof. Aakrati Verma Dr. Nidhi Singh

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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.

Implication

Analysis compares DRL algorithm performance on intelligent traffic management, indicating efficiency improvements for SDN applications.

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Cite This Study

Singh et al. (2026) studied this question.

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

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