Task Offloading in Vehicular Edge Computing using Deep Reinforcement Learning: A Survey
- URL: http://arxiv.org/abs/2502.06963v1
- Date: Mon, 10 Feb 2025 19:02:20 GMT
- Title: Task Offloading in Vehicular Edge Computing using Deep Reinforcement Learning: A Survey
- Authors: Ashab Uddin, Ahmed Hamdi Sakr, Ning Zhang,
- Abstract summary: We explore the potential of Reinforcement Learning (RL) and Deep Reinforcement Learning (DRL) frameworks to optimize computational offloading through adaptive, real-time decision-making.<n>The paper focuses on key aspects such as standardized learning models, optimized reward structures, and collaborative multi-agent systems, aiming to advance the understanding and application of DRL in vehicular networks.
- Score: 9.21746609806009
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The increasing demand for Intelligent Transportation Systems (ITS) has introduced significant challenges in managing the complex, computation-intensive tasks generated by modern vehicles while offloading tasks to external computing infrastructures such as edge computing (EC), nearby vehicular , and UAVs has become influential solution to these challenges. However, traditional computational offloading strategies often struggle to adapt to the dynamic and heterogeneous nature of vehicular environments. In this study, we explored the potential of Reinforcement Learning (RL) and Deep Reinforcement Learning (DRL) frameworks to optimize computational offloading through adaptive, real-time decision-making, and we have thoroughly investigated the Markov Decision Process (MDP) approaches on the existing literature. The paper focuses on key aspects such as standardized learning models, optimized reward structures, and collaborative multi-agent systems, aiming to advance the understanding and application of DRL in vehicular networks. Our findings offer insights into enhancing the efficiency, scalability, and robustness of ITS, setting the stage for future innovations in this rapidly evolving field.
Related papers
- The Emergence of Deep Reinforcement Learning for Path Planning [27.08547928141541]
Deep reinforcement learning (DRL) has emerged as a powerful method for enabling autonomous agents to learn optimal navigation strategies.<n>This survey provides a comprehensive overview of traditional approaches as well as the recent advancements in DRL applied to path planning tasks.<n>The survey concludes by identifying key open challenges and outlining promising avenues for future research.
arXiv Detail & Related papers (2025-07-21T10:21:42Z) - Deep Research Agents: A Systematic Examination And Roadmap [79.04813794804377]
Deep Research (DR) agents are designed to tackle complex, multi-turn informational research tasks.<n>In this paper, we conduct a detailed analysis of the foundational technologies and architectural components that constitute DR agents.
arXiv Detail & Related papers (2025-06-22T16:52:48Z) - Edge-Cloud Collaborative Computing on Distributed Intelligence and Model Optimization: A Survey [59.52058740470727]
Edge-cloud collaborative computing (ECCC) has emerged as a pivotal paradigm for addressing the computational demands of modern intelligent applications.<n>Recent advancements in AI, particularly deep learning and large language models (LLMs), have dramatically enhanced the capabilities of these distributed systems.<n>This survey provides a structured tutorial on fundamental architectures, enabling technologies, and emerging applications.
arXiv Detail & Related papers (2025-05-03T13:55:38Z) - Intelligent Task Offloading in VANETs: A Hybrid AI-Driven Approach for Low-Latency and Energy Efficiency [2.1877558143992184]
Vehicular Ad-hoc Networks (VANETs) are integral to intelligent transportation systems.
VANETs enable vehicles to offload computational tasks to nearby roadside units (RSUs) and mobile edge computing (MEC) servers for real-time processing.
This research proposes a hybrid AI framework that integrates supervised learning, reinforcement learning, and Particle Swarm Optimization (PSO) for intelligent task offloading and resource allocation.
arXiv Detail & Related papers (2025-04-29T13:20:02Z) - A Survey on Inference Optimization Techniques for Mixture of Experts Models [50.40325411764262]
Large-scale Mixture of Experts (MoE) models offer enhanced model capacity and computational efficiency through conditional computation.<n> deploying and running inference on these models presents significant challenges in computational resources, latency, and energy efficiency.<n>This survey analyzes optimization techniques for MoE models across the entire system stack.
arXiv Detail & Related papers (2024-12-18T14:11:15Z) - Self-Driving Car Racing: Application of Deep Reinforcement Learning [0.0]
The project aims to develop an AI agent that efficiently drives a simulated car in the OpenAI Gymnasium CarRacing environment.
We investigate various RL algorithms, including Deep Q-Network (DQN), Proximal Policy Optimization (PPO), and novel adaptations that incorporate transfer learning and recurrent neural networks (RNNs) for enhanced performance.
arXiv Detail & Related papers (2024-10-30T07:32:25Z) - Multi-Agent Reinforcement Learning for Autonomous Driving: A Survey [14.73689900685646]
Reinforcement Learning (RL) is a potent tool for sequential decision-making and has achieved performance surpassing human capabilities.
As the extension of RL in the multi-agent system domain, multi-agent RL (MARL) not only need to learn the control policy but also requires consideration regarding interactions with all other agents in the environment.
Simulators are crucial to obtain realistic data, which is the fundamentals of RL.
arXiv Detail & Related papers (2024-08-19T03:31:20Z) - Optimizing Age of Information in Vehicular Edge Computing with Federated Graph Neural Network Multi-Agent Reinforcement Learning [44.17644657738893]
This paper focuses on the Age of Information (AoI) as a key metric for data freshness and explores task offloading issues for vehicles under RSU communication resource constraints.
We propose an innovative distributed federated learning framework combining Graph Neural Networks (GNN), named Federated Graph Neural Network Multi-Agent Reinforcement Learning (FGNN-MADRL) to optimize AoI across the system.
arXiv Detail & Related papers (2024-07-01T15:37:38Z) - DNN Partitioning, Task Offloading, and Resource Allocation in Dynamic Vehicular Networks: A Lyapunov-Guided Diffusion-Based Reinforcement Learning Approach [49.56404236394601]
We formulate the problem of joint DNN partitioning, task offloading, and resource allocation in Vehicular Edge Computing.
Our objective is to minimize the DNN-based task completion time while guaranteeing the system stability over time.
We propose a Multi-Agent Diffusion-based Deep Reinforcement Learning (MAD2RL) algorithm, incorporating the innovative use of diffusion models.
arXiv Detail & Related papers (2024-06-11T06:31:03Z) - Aquatic Navigation: A Challenging Benchmark for Deep Reinforcement Learning [53.3760591018817]
We propose a new benchmarking environment for aquatic navigation using recent advances in the integration between game engines and Deep Reinforcement Learning.
Specifically, we focus on PPO, one of the most widely accepted algorithms, and we propose advanced training techniques.
Our empirical evaluation shows that a well-designed combination of these ingredients can achieve promising results.
arXiv Detail & Related papers (2024-05-30T23:20:23Z) - Machine Learning Insides OptVerse AI Solver: Design Principles and
Applications [74.67495900436728]
We present a comprehensive study on the integration of machine learning (ML) techniques into Huawei Cloud's OptVerse AI solver.
We showcase our methods for generating complex SAT and MILP instances utilizing generative models that mirror multifaceted structures of real-world problem.
We detail the incorporation of state-of-the-art parameter tuning algorithms which markedly elevate solver performance.
arXiv Detail & Related papers (2024-01-11T15:02:15Z) - End-to-end Lidar-Driven Reinforcement Learning for Autonomous Racing [0.0]
Reinforcement Learning (RL) has emerged as a transformative approach in the domains of automation and robotics.
This study develops and trains an RL agent to navigate a racing environment solely using feedforward raw lidar and velocity data.
The agent's performance is then experimentally evaluated in a real-world racing scenario.
arXiv Detail & Related papers (2023-09-01T07:03:05Z) - Knowledge-Driven Multi-Agent Reinforcement Learning for Computation
Offloading in Cybertwin-Enabled Internet of Vehicles [24.29177900273616]
We propose a knowledge-driven multi-agent reinforcement learning (KMARL) approach to reduce the latency of task offloading in cybertwin-enabled IoV.
Specifically, in the considered scenario, the cybertwin serves as a communication agent for each vehicle to exchange information and make offloading decisions in the virtual space.
arXiv Detail & Related papers (2023-08-04T09:11:37Z) - MARLIN: Soft Actor-Critic based Reinforcement Learning for Congestion
Control in Real Networks [63.24965775030673]
We propose a novel Reinforcement Learning (RL) approach to design generic Congestion Control (CC) algorithms.
Our solution, MARLIN, uses the Soft Actor-Critic algorithm to maximize both entropy and return.
We trained MARLIN on a real network with varying background traffic patterns to overcome the sim-to-real mismatch.
arXiv Detail & Related papers (2023-02-02T18:27:20Z) - Reinforcement Learning-Empowered Mobile Edge Computing for 6G Edge
Intelligence [76.96698721128406]
Mobile edge computing (MEC) considered a novel paradigm for computation and delay-sensitive tasks in fifth generation (5G) networks and beyond.
This paper provides a comprehensive research review on free-enabled RL and offers insight for development.
arXiv Detail & Related papers (2022-01-27T10:02:54Z) - Explainable Reinforcement Learning for Broad-XAI: A Conceptual Framework
and Survey [0.7366405857677226]
Reinforcement Learning (RL) methods provide a potential backbone for the cognitive model required for the development of Broad-XAI.
RL represents a suite of approaches that have had increasing success in solving a range of sequential decision-making problems.
This paper aims to introduce a conceptual framework, called the Causal XRL Framework (CXF), that unifies the current XRL research and uses RL as a backbone to the development of Broad-XAI.
arXiv Detail & Related papers (2021-08-20T05:18:50Z) - Reconfigurable Intelligent Surface Assisted Mobile Edge Computing with
Heterogeneous Learning Tasks [53.1636151439562]
Mobile edge computing (MEC) provides a natural platform for AI applications.
We present an infrastructure to perform machine learning tasks at an MEC with the assistance of a reconfigurable intelligent surface (RIS)
Specifically, we minimize the learning error of all participating users by jointly optimizing transmit power of mobile users, beamforming vectors of the base station, and the phase-shift matrix of the RIS.
arXiv Detail & Related papers (2020-12-25T07:08:50Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.