中文版 | English

在结果中检索

CASIA OpenIR
（本次检索基于用户作品认领结果）

浏览/检索结果: 共23条，第1-10条 帮助

限定条件        
已选(0)清除 条数/页：   排序方式：
NeuronsMAE: A Novel Multi-Agent Reinforcement Learning Environment for Cooperative and Competitive Multi-Robot Tasks 会议论文
, Queensland, Australia, 2023-6
作者:  Hu GZ(胡光政);  Li HR(李浩然);  Liu SS(刘莎莎);  Zhu YH(朱圆恒);  Zhao DB(赵冬斌)
Adobe PDF(2785Kb)  |  收藏  |  浏览/下载：33/9  |  提交时间：2024/07/04
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：22/10  |  提交时间：2024/07/04
Adaptive Multi-Agent Coordination among Different Team Attribute Tasks via Contextual Meta-Reinforcement Learning 会议论文
, 河南开封, 2024年5月17-19日
作者:  Huang, Shangjing;  Zhao, Zijie;  Zhu, Yuanheng;  Zhao, Dongbin
Adobe PDF(15515Kb)  |  收藏  |  浏览/下载：27/10  |  提交时间：2024/06/26
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：43/8  |  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  
NVIF: Neighboring Variational Information Flow for Cooperative Large-Scale Multiagent Reinforcement Learning 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2023, 页码: 13
作者:  Chai, Jiajun;  Zhu, Yuanheng;  Zhao, Dongbin
Adobe PDF(2469Kb)  |  收藏  |  浏览/下载：62/3  |  提交时间：2023/11/16
Large-scale multiagent  neighboring communication  reinforcement learning (RL)  variational information flow  
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：111/44  |  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：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  
Event-Triggered Communication Network With Limited-Bandwidth Constraint for Multi-Agent Reinforcement Learning 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2021, 页码: 13
作者:  Hu, Guangzheng;  Zhu, Yuanheng;  Zhao, Dongbin;  Zhao, Mengchen;  Hao, Jianye
Adobe PDF(4187Kb)  |  收藏  |  浏览/下载：264/12  |  提交时间：2022/01/27
Bandwidth  Protocols  Reinforcement learning  Task analysis  Optimization  Communication networks  Multi-agent systems  Event trigger  limited bandwidth  multi-agent communication  multi-agent reinforcement learning (MARL)  
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