Review explores DRL for intelligent offloading in vehicular edge computing

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]

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• Ashab Uddin
• Deep Reinforcement Learning
• Markov Decision Process
• Reinforcement Learning
• Vehicular Edge Computing

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