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Review explores DRL for intelligent offloading in vehicular edge computing

By PulseAugur Editorial · [1 sources] ·

This paper provides a comprehensive review of Deep Reinforcement Learning (DRL) approaches for intelligent offloading in vehicular edge computing (VEC). It categorizes existing research based on learning paradigms, system architectures, and optimization goals like latency and energy consumption. The review also examines the application of Markov Decision Processes (MDPs) and discusses future research directions for VEC systems. AI

IMPACT Provides a structured overview of DRL applications in VEC, guiding future research in intelligent transportation systems.

RANK_REASON This is a review paper on a specific application of AI/ML techniques. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Review explores DRL for intelligent offloading in vehicular edge computing

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This is a review paper on a specific application of AI/ML techniques. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Ashab Uddin, Ahmed Hamdi Sakr, Ning Zhang ·

    Intelligent Offloading in Vehicular Edge Computing: A Comprehensive Review of Deep Reinforcement Learning Approaches and Architectures

    arXiv:2502.06963v3 Announce Type: replace-cross Abstract: The increasing complexity of Intelligent Transportation Systems (ITS) has led to significant interest in computational offloading to external infrastructures such as edge servers, vehicular nodes, and UAVs. These dynamic a…

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