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A fully value distributional deep reinforcement learning framework for multi-agent cooperation Journal article
Fu, Mingsheng, Huang, Liwei, Li, Fan, Qu, Hong, Xu, Chengzhong. A fully value distributional deep reinforcement learning framework for multi-agent cooperation[J]. Neural Networks, 2025, 184.
Authors:  Fu, Mingsheng;  Huang, Liwei;  Li, Fan;  Qu, Hong;  Xu, Chengzhong
Favorite | TC[WOS]:0 TC[Scopus]:0  IF:6.0/7.9 | Submit date：2025/01/22
Deep Reinforcement Learning  Multi-agent Cooperation  Distributional Reinforcement Learning  Neural Networks  
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]:21 TC[Scopus]:26  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  
Multi-Agent DRL-Based Two-Timescale Resource Allocation for Network Slicing in V2X Communications Journal article
Lu, Binbin, Wu, Yuan, Qian, Liping, Zhou, Sheng, Zhang, Haixia, Lu, Rongxing. Multi-Agent DRL-Based Two-Timescale Resource Allocation for Network Slicing in V2X Communications[J]. IEEE Transactions on Network and Service Management, 2024, 21(6), 6744-6758.
Authors:  Lu, Binbin;  Wu, Yuan;  Qian, Liping;  Zhou, Sheng;  Zhang, Haixia; et al.
Favorite | TC[WOS]:0 TC[Scopus]:0  IF:4.7/4.6 | Submit date：2025/01/13
Multi-agent Deep Reinforcement Learning  Network Slicing  Resource Allocations  V2x Communications  
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]:2  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  
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  
Network-wide traffic signal control optimization using a multi-agent deep reinforcement learning Journal article
Li,Zhenning, Yu,Hao, Zhang,Guohui, Dong,Shangjia, Xu,Cheng Zhong. Network-wide traffic signal control optimization using a multi-agent deep reinforcement learning[J]. TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, 2021, 125, 103059.
Authors:  Li,Zhenning;  Yu,Hao;  Zhang,Guohui;  Dong,Shangjia;  Xu,Cheng Zhong
Favorite | TC[WOS]:82 TC[Scopus]:106  IF:7.6/9.6 | Submit date：2021/05/31
Multi-agent Reinforcement Learning  Knowledge Sharing  Adaptive Traffic Signal Control  Deep Learning  Transportation Network