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Team-wise effective communication in multi-agent reinforcement learning Journal article
Yang, Ming, Zhao, Kaiyan, Wang, Yiming, Dong, Renzhi, Du, Yali, Liu, Furui, Zhou, Mingliang, U, Leong Hou. Team-wise effective communication in multi-agent reinforcement learning[J]. Autonomous Agents and Multi-Agent Systems, 2024, 38(2), 36.
Authors:  Yang, Ming;  Zhao, Kaiyan;  Wang, Yiming;  Dong, Renzhi;  Du, Yali; et al.
Favorite | TC[WOS]:0 TC[Scopus]:1  IF:2.0/2.1 | Submit date：2024/08/05
Communication  Competition  Cooperation  Multi-agent System  Reinforcement Learning  
A multi-stage stochastic dispatching method for electricity‑hydrogen integrated energy systems driven by model and data Journal article
Yang, Zhixue, Ren, Zhouyang, Li, Hui, Sun, Zhiyuan, Feng, Jianbing, Xia, Weiyi. A multi-stage stochastic dispatching method for electricity‑hydrogen integrated energy systems driven by model and data[J]. Applied Energy, 2024, 371, 123668.
Authors:  Yang, Zhixue;  Ren, Zhouyang;  Li, Hui;  Sun, Zhiyuan;  Feng, Jianbing; et al.
Favorite | TC[WOS]:3 TC[Scopus]:6  IF:10.1/10.4 | Submit date：2024/07/04
Chance-constrained  Electricity‑hydrogen Integrated Energy Systems  Hydrogen Energy  Multi-agent Deep Reinforcement Learning  Uncertainty  
Learning-based Autonomous Channel Access in the Presence of Hidden Terminals Journal article
Shao,Yulin, Cai,Yucheng, Wang,Taotao, Guo,Ziyang, Liu,Peng, Luo,Jiajun, Gunduz,Deniz. Learning-based Autonomous Channel Access in the Presence of Hidden Terminals[J]. IEEE Transactions on Mobile Computing, 2024, 23(5), 3680 - 3695.
Authors:  Shao,Yulin;  Cai,Yucheng;  Wang,Taotao;  Guo,Ziyang;  Liu,Peng; et al.
Favorite | TC[WOS]:1 TC[Scopus]:1  IF:7.7/6.5 | Submit date：2023/08/03
Hidden Terminal  Multi-agent Deep Reinforcement Learning  Multiple Channel Access  Proximal Policy Optimization  Wi-fi  
Multi-Agent Mix Hierarchical Deep Reinforcement Learning for Large-Scale Fleet Management Journal article
Huang, Xiaohui, Ling, Jiahao, Yang, Xiaofei, Zhang, Xiong, Yang, Kaiming. Multi-Agent Mix Hierarchical Deep Reinforcement Learning for Large-Scale Fleet Management[J]. IEEE Transactions on Intelligent Transportation Systems, 2023, 24(12), 14294-14305.
Authors:  Huang, Xiaohui;  Ling, Jiahao;  Yang, Xiaofei;  Zhang, Xiong;  Yang, Kaiming
Favorite | TC[WOS]:1 TC[Scopus]:4  IF:7.9/8.3 | Submit date：2024/01/02
Fleet Management  Hierarchical Reinforcement Learning  Multi-agent Reinforcement Learning  
Emergency Control Method of Multi-Modal Passenger Flow in Urban Rail Transit Journal article
Zhu, Guangyu, Mu, Liang, Sun, Ranran, Zhang, Nuo, Wu, Bo, Zhang, Peng, Law, Rob. Emergency Control Method of Multi-Modal Passenger Flow in Urban Rail Transit[J]. IEEE Transactions on Automation Science and Engineering, 2023, 1 - 11.
Authors:  Zhu, Guangyu;  Mu, Liang;  Sun, Ranran;  Zhang, Nuo;  Wu, Bo; et al.
Favorite | TC[WOS]:0 TC[Scopus]:0  IF:5.9/6.0 | Submit date：2024/02/22
Emergency Control  Multi-agent Deep Reinforcement Learning  Multi-modal Passenger Flow  Urban Rail Transit  
RIS Aided NR-U and WiFi Coexistence in Single Cell and Multiple Cell Networks on Unlicensed Bands Journal article
Zeng,Ming, Ning,Xiangrui, Wang,Wenxin, Wu,Qingqing, Fei,Zesong. RIS Aided NR-U and WiFi Coexistence in Single Cell and Multiple Cell Networks on Unlicensed Bands[J]. IEEE Transactions on Green Communications and Networking, 2023.
Authors:  Zeng,Ming;  Ning,Xiangrui;  Wang,Wenxin;  Wu,Qingqing;  Fei,Zesong
Favorite | TC[WOS]:0 TC[Scopus]:0  IF:5.3/4.5 | Submit date：2023/08/03
Array Signal Processing  Cellular Networks  Interference  Interference Suppression  Multi-agent Reinforcement Learning  Optimization  Optimization  Reconfigurable Intelligent Surfaces (Ris)  Relays  Signal To Noise Ratio  Unlicensed Bands  Wireless Fidelity  
TieComm: Learning a Hierarchical Communication Topology Based on Tie Theory Conference paper
Yang,Ming, Dong,Renzhi, Wang,Yiming, Liu,Furui, Du,Yali, Zhou,Mingliang, U, Leong Hou. TieComm: Learning a Hierarchical Communication Topology Based on Tie Theory[C]:Springer Science and Business Media Deutschland GmbH, 2023, 604-613.
Authors:  Yang,Ming;  Dong,Renzhi;  Wang,Yiming;  Liu,Furui;  Du,Yali; et al.
Favorite | TC[Scopus]:1 | Submit date：2023/08/03
Communication Topology  Cooperation  Multi-agent System  Reinforcement Learning  Social Welfare  
Optimal consensus of a class of discrete-time linear multi-agent systems via value iteration with guaranteed admissibility Journal article
Li, Pingchuan, Zou, Wencheng, Guo, Jian, Xiang, Zhengrong. Optimal consensus of a class of discrete-time linear multi-agent systems via value iteration with guaranteed admissibility[J]. Neurocomputing, 2022, 516, 1-10.
Authors:  Li, Pingchuan;  Zou, Wencheng;  Guo, Jian;  Xiang, Zhengrong
Favorite | TC[WOS]:6 TC[Scopus]:9  IF:5.5/5.5 | Submit date：2023/02/08
Multi-agent System  Optimal Consensus  Reinforcement Learning  Value Iteration  
Noise-Regularized Advantage Value for Multi-Agent Reinforcement Learning Journal article
Wang, Siying, Chen, Wenyu, Hu, Jian, Hu, Siyue, Huang, Liwei. Noise-Regularized Advantage Value for Multi-Agent Reinforcement Learning[J]. Mathematics, 2022, 10(15), 2728.
Authors:  Wang, Siying;  Chen, Wenyu;  Hu, Jian;  Hu, Siyue;  Huang, Liwei
Favorite | TC[WOS]:1 TC[Scopus]:1  IF:2.3/2.2 | Submit date：2023/01/30
Advantage Function  Exploration  Multi-agent Reinforcement Learning  Noise Injection  Proximal Policy Optimization  
Collaborative Intelligent Reflecting Surface Networks With Multi-Agent Reinforcement Learning Journal article
Zhang, Jie, Li, Jun, Zhang, Yijin, Wu, Qingqing, Wu, Xiongwei, Shu, Feng, Jin, Shi, Chen, Wen. Collaborative Intelligent Reflecting Surface Networks With Multi-Agent Reinforcement Learning[J]. IEEE Journal on Selected Topics in Signal Processing, 2022, 16(3), 532-545.
Authors:  Zhang, Jie;  Li, Jun;  Zhang, Yijin;  Wu, Qingqing;  Wu, Xiongwei; et al.
Favorite | TC[WOS]:18 TC[Scopus]:21  IF:8.7/8.4 | Submit date：2022/08/05
Beamforming  Energy Harvesting  Intelligent Reflecting Surface  Multi-agent Reinforcement Learning