MULTI-AGENT

作品数：981被引量：2487H指数：17
导出分析报告
相关领域：自动化与计算机技术更多>>
相关作者：蒋国瑞黄梯云赵书良杨神化杨乃定更多>>
相关机构：北京工业大学中南大学北京理工大学武汉理工大学更多>>
相关期刊：更多>>
相关基金：国家自然科学基金国家高技术研究发展计划国家教育部博士点基金国家社会科学基金更多>>
-
在结果中检索

检索结果分析

结果分析中...
选择条件：
  • 主题=REINFORCEMENT_LEARNINGx
共 条 记 录，以下是1-10
全选清除导出
参考文献引证文献引用追踪
视图：
排序：
MARCS:A Mobile Crowdsensing Framework Based on Data Shapley Value Enabled Multi-Agent Deep Reinforcement Learning
《Computers, Materials & Continua》2025年第3期4431-4449,共19页Yiqin Wang Yufeng Wang Jianhua Ma Qun Jin 
sponsored by Qinglan Project of Jiangsu Province,and Jiangsu Provincial Key Research and Development Program(No.BE2020084-1).
Opportunistic mobile crowdsensing(MCS)non-intrusively exploits human mobility trajectories,and the participants’smart devices as sensors have become promising paradigms for various urban data acquisition tasks.Howeve...
关键词：Mobile crowdsensing online data acquisition data Shapley value multi-agent deep reinforcement learning centralized training and decentralized execution(CTDE) 
A pipelining task offloading strategy via delay-aware multi-agent reinforcement learning in Cybertwin-enabled 6G network
《Digital Communications and Networks》2025年第1期92-105,共14页Haiwen Niu Luhan Wang Keliang Du Zhaoming Lu Xiangming Wen Yu Liu 
funded by the National Key Research and Development Program of China under Grant 2019YFB1803301;Beijing Natural Science Foundation (L202002)。
Cybertwin-enabled 6th Generation(6G)network is envisioned to support artificial intelligence-native management to meet changing demands of 6G applications.Multi-Agent Deep Reinforcement Learning(MADRL)technologies dri...
关键词：Cybertwin Multi-Agent Deep Reinforcement Learning(MADRL) Task offloading PIPELINING Delay-aware 
Multi-agent deep reinforcement learning based resource management in heterogeneous V2X networks
《Digital Communications and Networks》2025年第1期182-190,共9页Junhui Zhao Fajin Hu Jiahang Li Yiwen Nie 
funded in part by the National Key Research and Development of China Project (2020YFB1807204);in part by National Natural Science Foundation of China (U2001213 and 61971191);in part by the Beijing Natural Science Foundation under Grant L201011;in part by the key project of Natural Science Foundation of Jiangxi Province (20202ACBL202006)。
In Heterogeneous Vehicle-to-Everything Networks(HVNs),multiple users such as vehicles and handheld devices and infrastructure can communicate with each other to obtain more advanced services.However,the increasing num...
关键词：DATA-DRIVEN Deep reinforcement learning Resource allocation V2X communications 
Automatic Generation Control in a Distributed Power Grid Based on Multi-step Reinforcement Learning
《Protection and Control of Modern Power Systems》2024年第4期39-50,共12页Wenmeng Zhao Tuo Zeng Zhihong Liu Lihui Xie Lei Xi Hui Ma 
supported by the National Natural Sci-ence Foundation of China(No.52277108);Guangdong Provincial Department of Science and Technology(No.2022A0505020015).
The increasing use of renewable energy in the power system results in strong stochastic disturbances and degrades the control performance of the distributed power grids.In this paper,a novel multi-agent collaborative ...
关键词：Automatic generation control Dyna framework distributed power grid MULTI-AGENT mod-el-based reinforcement learning 
MADDPG-based Active Distribution Network Dynamic Reconfiguration with Renewable Energy
《Protection and Control of Modern Power Systems》2024年第6期143-155,共13页Changxu Jiang Zheng Lin Chenxi Liu Feixiong Chen Zhenguo Shao 
supported by the Natural Science Foundation of Fujian Province(No.2022J0512 and No.2021J05134);the National Natural Science Foundation of China(No.52377087).
The integration of distributed generations(DG),such as wind turbines and photovoltaics,has a significant impact on the security,stability,and economy of the distribution network due to the randomness and fluctuations ...
关键词：Distribution network reconfiguration active distribution network deep deterministic policy gradient multi-agent deep reinforcement learning. 
Communication-robust multi-agent learning by adaptable auxiliary multi-agent adversary generation
《Frontiers of Computer Science》2024年第6期101-117,共17页Lei YUAN Feng CHEN Zongzhang ZHANG Yang YU 
the National Key R&D Program of China(2020AAA0107200);the National Natural Science Foundation of China(Grant Nos.61921006,61876119,62276126);the Natural Science Foundation of Jiangsu(BK20221442)。
Communication can promote coordination in cooperative Multi-Agent Reinforcement Learning(MARL).Nowadays,existing works mainly focus on improving the communication efficiency of agents,neglecting that real-world commun...
关键词：multi-agent communication adversarial training robustness validation reinforcement learning 
Development of Multi-Agent-Based Indoor 3D Reconstruction
《Computers, Materials & Continua》2024年第10期161-181,共21页Hoi Chuen Cheng Frederick Ziyang Hong Babar Hussain Yiru Wang Chik Patrick Yue 
supported by Bright Dream Robotics and the HKUSTBDR Joint Research Institute Funding Scheme under Project HBJRI-FTP-005(Automated 3D Reconstruction using Robot-mounted 360-Degree Camera with Visible Light Positioning Technology for Building Information Modelling Applications,OKT22EG06).
Large-scale indoor 3D reconstruction with multiple robots faces challenges in core enabling technologies.This work contributes to a framework addressing localization,coordination,and vision processing for multi-agent ...
关键词：Multi-agent system multi-robot human collaboration visible light communication visible light positioning 3D reconstruction reinforcement learning multi-agent path finding 
A Task Offloading Strategy Based on Multi-Agent Deep Reinforcement Learning for Offshore Wind Farm Scenarios
《Computers, Materials & Continua》2024年第10期985-1008,共24页Zeshuang Song Xiao Wang Qing Wu Yanting Tao Linghua Xu Yaohua Yin Jianguo Yan 
supported in part by the National Natural Science Foundation of China under grant 61861007;the Guizhou Province Science and Technology Planning Project ZK[2021]303;the Guizhou Province Science Technology Support Plan under grant[2022]264,[2023]096,[2023]409 and[2023]412;the Science Technology Project of POWERCHINA Guizhou Engineering Co.,Ltd.(DJ-ZDXM-2022-44);the Project of POWERCHINA Guiyang Engineering Corporation Limited(YJ2022-12).
This research is the first application of Unmanned Aerial Vehicles(UAVs)equipped with Multi-access Edge Computing(MEC)servers to offshore wind farms,providing a new task offloading solution to address the challenge of...
关键词：Offshore wind MEC task offloading MADRL AGA-DDPG 
Service Function Chain Deployment Algorithm Based on Multi-Agent Deep Reinforcement Learning
《Computers, Materials & Continua》2024年第9期4875-4893,共19页Wanwei Huang Qiancheng Zhang Tao Liu YaoliXu Dalei Zhang 
The financial support fromthe Major Science and Technology Programs inHenan Province(Grant No.241100210100);National Natural Science Foundation of China(Grant No.62102372);Henan Provincial Department of Science and Technology Research Project(Grant No.242102211068);Henan Provincial Department of Science and Technology Research Project(Grant No.232102210078);the Stabilization Support Program of The Shenzhen Science and Technology Innovation Commission(Grant No.20231130110921001);the Key Scientific Research Project of Higher Education Institutions of Henan Province(Grant No.24A520042)is acknowledged.
Aiming at the rapid growth of network services,which leads to the problems of long service request processing time and high deployment cost in the deployment of network function virtualization service function chain(S...
关键词：Network function virtualization service function chain Markov decision process multi-agent reinforcement learning 
Regional Multi-Agent Cooperative Reinforcement Learning for City-Level Traffic Grid Signal Control
《IEEE/CAA Journal of Automatica Sinica》2024年第9期1987-1998,共12页Yisha Li Ya Zhang Xinde Li Changyin Sun 
supported by the National Science and Technology Major Project (2021ZD0112702);the National Natural Science Foundation (NNSF)of China (62373100,62233003);the Natural Science Foundation of Jiangsu Province of China (BK20202006)。
This article studies the effective traffic signal control problem of multiple intersections in a city-level traffic system.A novel regional multi-agent cooperative reinforcement learning algorithm called RegionSTLight...
关键词：Human-machine cooperation mixed domain attention mechanism multi-agent reinforcement learning spatio-temporal feature traffic signal control 
全选清除导出
共6页<123456>
检索报告 对象比较 聚类工具 使用帮助 返回顶部