Value decomposition based multi-task multi-agent deep reinforcement learning in vehicular networks

S Xu, C Guo, RQ Hu, Y Qian - GLOBECOM 2020-2020 IEEE …, 2020 - ieeexplore.ieee.org
With the development of intelligent transportation system (ITS), a multitude of novel vehicular
applications have been emerging. There is an urgent need for simultaneously supporting …
被引用次数：4 相关文章 所有 2 个版本
[PDF] arxiv.org

Stochastic graph neural network-based value decomposition for marl in internet of vehicles

B Xiao, R Li, F Wang, C Peng, J Wu… - IEEE Transactions …, 2023 - ieeexplore.ieee.org
Autonomous driving has witnessed incredible advances in the past several decades, while
Multi-Agent Reinforcement Learning (MARL) promises to satisfy the essential need of …
被引用次数：5 相关文章 所有 5 个版本
[PDF] arxiv.org

DNN Partitioning, Task Offloading, and Resource Allocation in Dynamic Vehicular Networks: A Lyapunov-Guided Diffusion-Based Reinforcement Learning Approach

Z Liu, H Du, J Lin, Z Gao, L Huang… - arXiv preprint arXiv …, 2024 - arxiv.org
The rapid advancement of Artificial Intelligence (AI) has introduced Deep Neural Network
(DNN)-based tasks to the ecosystem of vehicular networks. These tasks are often …
相关文章 所有 2 个版本

A survey on multi-agent reinforcement learning methods for vehicular networks

I Althamary, CW Huang, P Lin - 2019 15th International …, 2019 - ieeexplore.ieee.org
Under the rapid development of the Internet of Things (IoT), vehicles can be recognized as
mobile smart agents that communicating, cooperating, and competing for resources and …
被引用次数：45 相关文章

Stochastic graph neural network-based value decomposition for multi-agent reinforcement learning in urban traffic control

B Xiao, R Li, F Wang, C Peng, J Wu… - 2023 IEEE 97th …, 2023 - ieeexplore.ieee.org
Multi-Agent Reinforcement Learning (MARL) has reached astonishing achievements in
various fields such as the traffic control of vehicles in a wireless connected environment. In …
被引用次数：3 相关文章

Learning iov in edge: deep reinforcement learning for edge computing enabled vehicular networks

S Xu, C Guo, RQ Hu, Y Qian - ICC 2021-IEEE International …, 2021 - ieeexplore.ieee.org
The development of artificial intelligence, wireless communication and smart sensor platform
facilities the emergence of multitude of novel vehicular applications in recent years. These …
被引用次数：3 相关文章
[PDF] sciencedirect.com

Deep reinforcement learning techniques for vehicular networks: Recent advances and future trends towards 6G

A Mekrache, A Bradai, E Moulay, S Dawaliby - Vehicular Communications, 2022 - Elsevier
Employing machine learning into 6G vehicular networks to support vehicular application
services is being widely studied and a hot topic for the latest research works in the literature …
被引用次数：64 相关文章 所有 9 个版本
[PDF] arxiv.org

Multi-Task Lane-Free Driving Strategy for Connected and Automated Vehicles: A Multi-Agent Deep Reinforcement Learning Approach

M Berahman, M Rostami-Shahrbabaki… - arXiv preprint arXiv …, 2024 - arxiv.org
Deep reinforcement learning has shown promise in various engineering applications,
including vehicular traffic control. The non-stationary nature of traffic, especially in the lane …
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[PDF] arxiv.org

Coordination for connected and automated vehicles at non-signalized intersections: A value decomposition-based multiagent deep reinforcement learning approach

Z Guo, Y Wu, L Wang, J Zhang - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
The recent proliferation of the research on multi-agent deep reinforcement learning (MDRL)
offers an encouraging way to coordinate multiple connected and automated vehicles (CAVs) …
被引用次数：12 相关文章 所有 5 个版本

A Flexible Cooperative MARL Method for Efficient Passage of an Emergency CAV in Mixed Traffic

Z Li, Q Wang, J Wang, Z He - IEEE Transactions on Intelligent …, 2024 - ieeexplore.ieee.org
Connected and autonomous vehicles offer the possibility to carry out control strategies, thus
having great potential to improve traffic efficiency and road safety. The efficient passage of …
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