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

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Ashab Uddin , Ahmed Hamdi Sakr , Ning Zhang ·

YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2502.06963

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 and heterogeneous environments pose challenges for traditional offloading strategies, prompting the exploration of Reinforcement Learning (RL) and Deep Reinforcement Learning (DRL) as adaptive decision-making frameworks. This survey presents a comprehensive review of recent advances in DRL-based offloading for vehicular edge computing (VEC). We classify and compare existing works based on learning paradigms (e.g., single-agent, multi-agent), system architectures (e.g., centralized, distributed, hierarchical), and optimization objectives (e.g., latency, energy, fairness). Furthermore, we analyze how Markov Decision Process (MDP) formulations are applied and highlight emerging trends in reward design, coordination mechanisms, and scalability. Finally, we identify open challenges and outline future research directions to guide the development of robust and intelligent offloading strategies for next-generation ITS.

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Reinforcement Learning

Computer Science

Enhanced Data Rates For Gsm Evolution

Edge Computing

Human–Computer Interaction

Artificial Intelligence

Engineering

Systems Engineering

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Reinforcement learning Task (project management) Computer science Enhanced Data Rates for GSM Evolution Edge computing Human–computer interaction Artificial intelligence Engineering Systems engineering

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2502.06963
PDF: https://arxiv.org/pdf/2502.06963
OA Status: green
Related Works: 10
OpenAlex ID: https://openalex.org/W4407423788

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DOI: https://doi.org/10.48550/arxiv.2502.06963

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

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Type: preprint

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Language: en

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Publication year: 2025

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Publication date: 2025-02-10

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Authors: Ashab Uddin, Ahmed Hamdi Sakr, Ning Zhang

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Landing page: https://arxiv.org/abs/2502.06963

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PDF URL: https://arxiv.org/pdf/2502.06963

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Open access: Yes

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OA status: green

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OA URL: https://arxiv.org/pdf/2502.06963

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Concepts: Reinforcement learning, Task (project management), Computer science, Enhanced Data Rates for GSM Evolution, Edge computing, Human–computer interaction, Artificial intelligence, Engineering, Systems engineering

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Cited by: 0

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Related works (count): 10

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