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FM3Q: Factorized Multi-Agent MiniMax Q-Learning for Two-Team Zero-Sum Markov Game 期刊论文
IEEE Transactions on Emerging Topics in Computational Intelligence, 2024, 页码: 1-13
作者:  Guangzheng Hu;  Yuanheng Zhu;  Haoran Li;  Dongbin Zhao
Adobe PDF(2144Kb)  |  收藏  |  浏览/下载：32/4  |  提交时间：2024/06/05
Games  Q-learning  Task analysis  Optimization  Convergence  Training  Nash equilibrium  Multi-agent reinforcement learning  minimax-Q learning  two-team zero-sum Markov games  
Multistep Look-Ahead Policy Iteration for Optimal Control of Discrete-Time Nonlinear Systems With Isoperimetric Constraints 期刊论文
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, 2023, 卷号: 54, 期号: 3, 页码: 1414-1426
作者:  Li, Tao;  Wei, Qinglai;  Wang, Fei-Yue
Adobe PDF(784Kb)  |  收藏  |  浏览/下载：110/17  |  提交时间：2024/02/22
Performance analysis  Optimal control  Dynamic programming  Iterative algorithms  Upper bound  Measurement  Convergence  Adaptive dynamic programming (ADP)  isoperimetric constraints  nonlinear systems  optimal control  policy iteration  
Hierarchical Policy Learning With Demonstration Learning for Robotic Multiple Peg-in-Hole Assembly Tasks 期刊论文
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2023, 卷号: 19, 期号: 10, 页码: 10254-10264
作者:  Yan, Shaohua;  Xu, De;  Tao, Xian
Adobe PDF(4845Kb)  |  收藏  |  浏览/下载：123/12  |  提交时间：2023/11/17
Assembly model  demonstration learning (DL)  force-based control algorithm  hierarchical reinforcement learning (HRL)  peg-in-hole assembly  
Large sequence models for sequential decision-making: a survey 期刊论文
FRONTIERS OF COMPUTER SCIENCE, 2023, 卷号: 17, 期号: 6, 页码: 18
作者:  Wen, Muning;  Lin, Runji;  Wang, Hanjing;  Yang, Yaodong;  Wen, Ying;  Mai, Luo;  Wang, Jun;  Zhang, Haifeng;  Zhang, Weinan
Adobe PDF(1351Kb)  |  收藏  |  浏览/下载：146/4  |  提交时间：2023/11/17
sequential decision-making  sequence modeling  the Transformer  training system  
Personalized graph neural networks with attention mechanism for session-aware recommendation 期刊论文
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 2020, 卷号: 34, 期号: 8, 页码: 3946-3957
作者:  Mengqi Zhang;  Shu Wu;  Meng Gao;  Xin Jiang;  Ke Xu;  Liang Wang
Adobe PDF(1277Kb)  |  收藏  |  浏览/下载：156/52  |  提交时间：2023/07/03
A Brain-Inspired Theory of Mind Spiking Neural Network for Reducing Safety Risks of Other Agents (vol 16, 753900, 2022) 期刊论文
FRONTIERS IN NEUROSCIENCE, 2022, 卷号: 16, 页码: 2
作者:  Zhao, Zhuoya;  Lu, Enmeng;  Zhao, Feifei;  Zeng, Yi;  Zhao, Yuxuan
Adobe PDF(4502Kb)  |  收藏  |  浏览/下载：163/8  |  提交时间：2022/07/25
brain-inspired model  safety risks  SNNs  R-STDP  theory of mind  
Online Minimax Q Network Learning for Two-Player Zero-Sum Markov Games 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2022, 卷号: 33, 期号: 3, 页码: 1228-1241
作者:  Zhu, Yuanheng;  Zhao, Dongbin
Adobe PDF(2838Kb)  |  收藏  |  浏览/下载：239/8  |  提交时间：2022/06/10
Games  Nash equilibrium  Mathematical model  Markov processes  Convergence  Dynamic programming  Training  Deep reinforcement learning (DRL)  generalized policy iteration (GPI)  Markov game (MG)  Nash equilibrium  Q network  zero sum  
Meta-Residual Policy Learning: Zero-Trial Robot Skill Adaptation via Knowledge Fusion 期刊论文
IEEE Robotics and Automation Letters, 2022, 卷号: 7, 期号: 7, 页码: 3656-3663
作者:  Peng Hao;  Tao Lu;  Shaowei Cui;  Junhang Wei;  YInghao Cai;  Shuo Wang
Adobe PDF(1750Kb)  |  收藏  |  浏览/下载：239/44  |  提交时间：2022/04/08
meta-learning  residual learning  
An agent-based hybrid service delivery for collaborating Internet of Things and 3rd service providers 期刊论文
Journal of Network and Computer Applications, 2014, 期号: 36, 页码: 1684-1695
作者:  Wang JP(王军平);  JUNPING WANG
浏览  |  Adobe PDF(7204Kb)  |  收藏  |  浏览/下载：327/128  |  提交时间：2016/10/24
Internet Of Things  Service Delivery  Hybrid Service Exposure  Hybrid Service Ontology Engine Crawler  Service Enabler’s Container  
Scalable Multi-objects meta-level coordinated learning in Internet of Things 期刊论文
Personal and Ubiquitous Computing, 2015, 卷号: 19, 期号: 7, 页码: 1133–1144
作者:  Wang JP(王军平);  JUNPING WANG
浏览  |  Adobe PDF(3565Kb)  |  收藏  |  浏览/下载：331/118  |  提交时间：2016/10/20
Coordinated  Multi-objects System  Meta-level Control  Coordinated Learning