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