.png){kind=icon}
返回
Decentralized Task Assignment for Mobile Crowdsensing With Multi-Agent Deep Reinforcement Learning
.png){kind=icon}
PRE
AI
DOI：10.1109/JIOT.2023.3268846.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Task assignment is a fundamental research problem in mobile crowdsensing (MCS) since it directly determines an MCS system's practicality and economic value. Due to the complex dynamics of tasks and workers, task assignment problems are usually NP-hard, and approximation-based methods are preferred to impractical optimal methods. In the literature, a graph neural network-based deep reinforcement learning (GDRL) method is proposed in Xu and Song (2022) to solve routing problems in MCS and shows high performance and time efficiency. However, GDRL, as a centralized method, has to cope with the limitation in scalability and the challenge of privacy protection. In this article, we propose a multi-agent deep reinforcement learning-based method named communication-QMIX-based multi-agent DRL (CQDRL) to solve a task assignment problem in a decentralized fashion. The CQDRL method not only inherits the merits of GDRL over handcrafted heuristic and metaheuristic methods but also exploits computation potentials in mobile devices and protects workers' privacy with a decentralized decision-making scheme. Our extensive experiments show that the CQDRL method can achieve significantly better performance than other traditional methods and performs fairly close to the centralized GDRL method.
Keyword:
Task analysis
Sensors
Resource management
Privacy
Metaheuristics
Costs
Routing
Graph embedding
learning-communication
mobile crowdsensing (MCS)
multi-agent deep reinforcement learning (DRL)
QMIX
task assignment
期刊
IF:
8.9
论文数:
1.4W
被引数:
7.8W
机构
引用论文
Study of bi-directional buck-boost converter topologies for application in electrical vehicle motor drives应用于电动汽车电机驱动的双向buck-boost变换器拓扑研究
PREAI
分享收藏Smart Mobile Crowdsensing With Urban Vehicles: A Deep Reinforcement Learning Perspective.png)
IEEE ACCESS
OAAI
IEEE ACCESSIF3.6
OAAI分享收藏Energy-Efficient Mobile Crowdsensing by Unmanned Vehicles: A Sequential Deep Reinforcement Learning Approach无人驾驶汽车的节能移动人群感知: 一种连续的深度强化学习方法
PREAI
分享收藏
查看更多内容