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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：40/8  |  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  
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：250/12  |  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  
Reinforcement Learning for Build-Order Production in StarCraft II 会议论文
, Cordoba, Granada, and Seville, Spain, 30 June-6 July 2018
作者:  Zhentao Tang;  Dongbin Zhao;  Yuanheng Zhu;  Ping Guo
Adobe PDF(2680Kb)  |  收藏  |  浏览/下载：195/59  |  提交时间：2021/07/07
深度强化学习进展: 从 AlphaGo 到 AlphaGo Zero 期刊论文
控 制 理 论 与 应 用, 2017, 卷号: 34, 期号: 12, 页码: 1529-1546
作者:  唐振韬;  邵 坤;  赵冬斌;  朱圆恒
Adobe PDF(8232Kb)  |  收藏  |  浏览/下载：280/46  |  提交时间：2021/07/05
深度强化学习  AlphaGo Zero  深度学习  强化学习  人工智能  
Enhanced Rolling Horizon Evolution Algorithm With Opponent Model Learning: Results for the Fighting Game AI Competition 期刊论文
IEEE TRANSACTIONS ON GAMES, 2023, 卷号: 5, 期号: 1, 页码: 5 - 15
作者:  Zhentao Tang;  Yuanheng Zhu;  Dongbin Zhao;  Simon M. Lucas
Adobe PDF(7686Kb)  |  收藏  |  浏览/下载：353/74  |  提交时间：2021/07/05
Rolling horizon evolution  opponent model  reinforcement learning  supervised learning  fighting game  
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
浏览  |  Adobe PDF(4274Kb)  |  收藏  |  浏览/下载：409/125  |  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  
A Review of Computational Intelligence for StarCraft AI 会议论文
, Bangalore, India, 18-21 Nov. 2018
作者:  Tang, Zhentao;  Shao, Kun;  Zhu, Yuanheng;  Li, Dong;  Zhao, Dongbin;  Huang, Tingwen
浏览  |  Adobe PDF(131Kb)  |  收藏  |  浏览/下载：526/236  |  提交时间：2019/04/25
Visual navigation with Actor-Critic deep reinforcement learning 会议论文
, Rio, Brazil, 2018-01
作者:  Kun Shao;  Dongbin Zhao;  Yuanheng Zhu;  Qichao Zhang
浏览  |  Adobe PDF(1827Kb)  |  收藏  |  浏览/下载：341/138  |  提交时间：2019/04/22
Learning battles in ViZDoom via deep reinforcement learning 会议论文
, Maastricht, The Netherlands, 2018-08
作者:  Kun Shao;  Dongbin Zhao;  Nannan Li;  Yuanheng Zhu
浏览  |  Adobe PDF(446Kb)  |  收藏  |  浏览/下载：342/137  |  提交时间：2019/04/22
Reinforcement Learning, Deep Learning, Game Ai  
StarCraft Micromanagement With Reinforcement Learning and Curriculum Transfer Learning 期刊论文
IEEE Transactions on Emerging Topics in Computational Intelligence, 2019, 卷号: 3, 期号: 1, 页码: 73-84
作者:  Kun Shao;  Yuanheng Zhu;  Dongbin Zhao
浏览  |  Adobe PDF(4125Kb)  |  收藏  |  浏览/下载：362/136  |  提交时间：2019/04/22
Reinforcement Learning, Transfer Learning, Curriculum Learning, Neural Network, Game Ai