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An Improved Minimax-Q Algorithm Based on Generalized Policy Iteration to Solve a Chaser-Invader Game 会议论文
, 线上, 2020-5
作者:  Liu MS(刘民颂);  Zhu YH(朱圆恒);  Zhao DB(赵冬斌)
Adobe PDF(727Kb)  |  收藏  |  浏览/下载：36/15  |  提交时间：2024/07/04
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：53/11  |  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  
A Hierarchical Deep Reinforcement Learning Framework for 6-DOF UCAV Air-to-Air Combat 期刊论文
IEEE Transactions on Systems, Man and Cybernetics: Systems, 2023, 页码: DOI: 10.1109/TSMC.2023.3270444
作者:  Jiajun Chai;  Wenzhang Chen;  Yuanheng Zhu;  Zong-xin Yao,;  Dongbin Zhao
Adobe PDF(9249Kb)  |  收藏  |  浏览/下载：299/130  |  提交时间：2023/04/26
Empirical Policy Optimization for n-Player Markov Games 期刊论文
IEEE Transactions on Cybernetics, 2022, 页码: doi={10.1109/TCYB.2022.3179775}
作者:  Yuanheng Zhu;  Weifan Li;  Mengchen Zhao;  Jianye Hao;  Dongbin Zhao
Adobe PDF(1739Kb)  |  收藏  |  浏览/下载：118/49  |  提交时间：2023/04/26
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：260/16  |  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  
Online reinforcement learning for continuous-state systems 专著章节/文集论文
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作者:  Yuanheng Zhu;  Zhao DB(赵冬斌)
Adobe PDF(24150Kb)  |  收藏  |  浏览/下载：289/38  |  提交时间：2017/09/13