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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：252/12  |  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  
Towards Better Generalization of Deep Neural Networks via Non-Typicality Sampling Scheme 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2022, 页码: 11
作者:  Peng, Xinyu;  Wang, Fei-Yue;  Li, Li
收藏  |  浏览/下载：199/0  |  提交时间：2022/06/06
Training  Estimation  Deep learning  Standards  Optimization  Noise measurement  Convergence  Deep learning  generalization performance  nontypicality sampling scheme  stochastic gradient descent (SGD)