云南高校图书馆联盟文献共享服务平台- MARL

MARL: 作品数：36被引量：37H指数：4; 导出分析报告; 相关作者：张冲彭大芹阚兴一王三强刘鑫更多>>; 相关机构：重庆邮电大学电子科技大学湖南师范大学中国人民解放军65631部队更多>>; 相关期刊：《Geoscience Frontiers》《塑料助剂》《流程工业》《Petroleum Research》更多>>; 相关基金：国家自然科学基金国家重点基础研究发展计划中国博士后科学基金国家社会科学基金更多>>

Graph-based multi-agent reinforcement learning for collaborative search and tracking of multiple UAVs: 《Chinese Journal of Aeronautics》2025年第3期109-123,共15页Bocheng ZHAO Mingying HUO Zheng LI Wenyu FENG Ze YU Naiming QI Shaohai WANG; supported by the National Natural Science Foundation of China(Nos.12272104,U22B2013).; This paper investigates the challenges associated with Unmanned Aerial Vehicle (UAV) collaborative search and target tracking in dynamic and unknown environments characterized by limited field of view. The primary obj...; 关键词：Unmanned aerial vehicle(UAV) Multi-agent reinforcement learning(MARL) Graph attention network(GAT) Tracking Dynamic and unknown environment

基于MARL的无人机边缘计算任务卸载优化: 《智能计算机与应用》2024年第9期170-178,共9页刘鑫赵莎莎; 针对单个无人机覆盖范围有限、计算能力不足的问题,提出了联合边缘云的多无人机移动边缘计算系统;为了提高物联网设备的任务处理成功率和结果新鲜度,提出了基于多智能体强化学习(Multi-Agent Reinforcement Learning,MARL)的优化方案,...; 关键词：无人机移动边缘计算多智能体强化学习卸载选择

Microstructural analysis of marl stabilized with municipal solid waste and nano-MgO: 《Journal of Rock Mechanics and Geotechnical Engineering》2024年第8期3258-3269,共12页Ali Ohadian Navid Khayat Mehdi Mokhberi; Municipal solid waste(MSW)is accumulating over elapsed time across the world,and it is observed in many projects associated with weak soils,such as marl.Therefore,effective solutions to the environmental problem are e...; 关键词：MARL Shear strength MICROSTRUCTURE Nano-MgO Municipal solid waste(MSW)

Cooperative decision-making algorithm with efficient convergence for UCAV formation in beyond-visual-range air combat based on multi-agent reinforcement learning: 《Chinese Journal of Aeronautics》2024年第8期311-328,共18页Yaoming ZHOU Fan YANG Chaoyue ZHANG Shida LI Yongchao WANG; co-supported by the National Natural Science Foundation of China(No.52272382);the Aeronautical Science Foundation of China(No.20200017051001);the Fundamental Research Funds for the Central Universities,China.; Highly intelligent Unmanned Combat Aerial Vehicle(UCAV)formation is expected to bring out strengths in Beyond-Visual-Range(BVR)air combat.Although Multi-Agent Reinforcement Learning(MARL)shows outstanding performance ...; 关键词：Unmanned combat aerial vehicle(UCAV)formation DECISION-MAKING Beyond-visual-range(BVR)air combat Advantage highlight Multi-agent reinforcement learning(MARL)

Discovering Latent Variables for the Tasks With Confounders in Multi-Agent Reinforcement Learning: 《IEEE/CAA Journal of Automatica Sinica》2024年第7期1591-1604,共14页Kun Jiang Wenzhang Liu Yuanda Wang Lu Dong Changyin Sun; supported in part by the National Natural Science Foundation of China (62136008,62236002,61921004,62173251,62103104);the “Zhishan” Scholars Programs of Southeast University;the Fundamental Research Funds for the Central Universities (2242023K30034)。; Efficient exploration in complex coordination tasks has been considered a challenging problem in multi-agent reinforcement learning(MARL). It is significantly more difficult for those tasks with latent variables that ...; 关键词：Latent variable model maximum entropy multi-agent reinforcement learning(MARL) multi-agent system

FedQMIX:Communication-efficient federated learning via multi-agent reinforcement learning: 《High-Confidence Computing》2024年第2期96-104,共9页Shaohua Cao Hanqing Zhang Tian Wen Hongwei Zhao Quancheng Zheng Weishan Zhang Danyang Zheng; supported by the National Natural Science Foundation of China(NSFC)(62072469)。; Since the data samples on client devices are usually non-independent and non-identically distributed(non-IID),this will challenge the convergence of federated learning(FL)and reduce communication efficiency.This paper...; 关键词：Communication efficient Federated learning MARL

Transformer in reinforcement learning for decision-making:a survey: 《Frontiers of Information Technology & Electronic Engineering》2024年第6期763-790,共28页Weilin YUAN Jiaxing CHEN Shaofei CHEN Dawei FENG Zhenzhen HU Peng LI Weiwei ZHAO; Project supported by the National Natural Science Foundation of China(No.62376280)。; Reinforcement learning(RL)has become a dominant decision-making paradigm and has achieved notable success in many real-world applications.Notably,deep neural networks play a crucial role in unlocking RL’s potential i...; 关键词：TRANSFORMER Reinforcement learning(RL) Decision-making(DM) Deep neural network(DNN) Multi-agent reinforcement learning(MARL) Meta-reinforcement learning(Meta-RL)

Safety-Constrained Multi-Agent Reinforcement Learning for Power Quality Control in Distributed Renewable Energy Networks: 《Computers, Materials & Continua》2024年第4期449-471,共23页Yongjiang Zhao Haoyi Zhong Chang Cyoon Lim; “Regional Innovation Strategy(RIS)”through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(MOE)(2021RIS-002).; This paper examines the difficulties of managing distributed power systems,notably due to the increasing use of renewable energy sources,and focuses on voltage control challenges exacerbated by their variable nature i...; 关键词：Power quality control multi-agent reinforcement learning safety-constrained MARL

A reinforcement learning approach to vehicle coordination for structured advanced air mobility: 《Green Energy and Intelligent Transportation》2024年第2期20-37,共18页Sabrullah Deniz Yufei Wu Yang Shi Zhenbo Wang; This work was funded in part by the National Science Foundation(NSF)CAREER Award CMMI-2237215.; Advanced Air Mobility(AAM)has emerged as a pioneering concept designed to optimize the efficacy and ecological sustainability of air transportation.Its core objective is to provide highly automated air transportation ...; 关键词：Advanced Air Mobility(AAM) Urban Air Mobility(UAM) Air Traffic Control(ATC) Multi-Agent Reinforcement Learning(MARL)

移动性感知下基于负载均衡的任务迁移方案被引量：1: 《电讯技术》2024年第3期333-342,共10页鲜永菊韩瑞寅左维昊汪帅鸽; 国家自然科学基金资助项目(62071077)。; 针对移动边缘计算中用户移动性导致服务器间负载分布不均,用户服务质量(Quality of Service,QoS)下降的问题,提出了一种移动性感知下的分布式任务迁移方案。首先,以优化网络中性能最差的用户QoS为目标,建立了一个长期极大极小化公平性问...; 关键词：移动边缘计算(MEC) 移动性感知任务迁移多智能体强化学习(MARL)

MARL