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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：255/12  |  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  
Stacked BNAS: Rethinking Broad Convolutional Neural Network for Neural Architecture Search 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2022, 卷号: 0, 期号: 0, 页码: 0
作者:  Zixiang, Ding;  Yaran, Chen;  Nannan, Li;  Dongbin, Zhao;  C.L.Philip Chen,
Adobe PDF(764Kb)  |  收藏  |  浏览/下载：246/42  |  提交时间：2022/01/07
broad neural architecture search, stacked broad convolutional neural network, knowledge embedding search, image classification.  
Deep Reinforcement Learning-Based Automatic Exploration for Navigation in Unknown Environment 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2020, 卷号: 31, 期号: 6, 页码: 2064-2076
作者:  Li, Haoran;  Zhang, Qichao;  Zhao, Dongbin
Adobe PDF(4274Kb)  |  收藏  |  浏览/下载：414/127  |  提交时间：2020/08/03
Robot sensing systems  Navigation  Entropy  Neural networks  Task analysis  Planning  Automatic exploration  deep reinforcement learning (DRL)  optimal decision  partial observation  
Iterative Adaptive Dynamic Programming for Solving Unknown Nonlinear Zero-Sum Game Based on Online Data 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2017, 卷号: 28, 期号: 3, 页码: 714-725
作者:  Zhu, Yuanheng;  Zhao, Dongbin;  Li, Xiangjun
浏览  |  Adobe PDF(547Kb)  |  收藏  |  浏览/下载：477/195  |  提交时间：2017/05/05
Adaptive Dynamic Programming (Adp)  H-infinity Control  Policy Iteration (Pi)  Zero-sum Game (Zsg)  
MEC-A Near-Optimal Online Reinforcement Learning Algorithm for Continuous Deterministic Systems 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2015, 卷号: 26, 期号: 2, 页码: 346-356
作者:  Zhao, Dongbin;  Zhu, Yuanheng
浏览  |  Adobe PDF(2156Kb)  |  收藏  |  浏览/下载：297/116  |  提交时间：2015/09/18
Efficient Exploration  Probably Approximately Correct (Pac)  Reinforcement Learning (Rl)  State Aggregation