CASIA OpenIR
(本次检索基于用户作品认领结果)

浏览/检索结果: 共14条,第1-10条 帮助

限定条件            
已选(0)清除 条数/页:   排序方式:
Deep Reinforcement Learning-Based Driving Policy at Intersections Utilizing Lane Graph Networks 期刊论文
IEEE Transactions on Cognitive and Developmental Systems, 2024, 页码: 1 - 16
作者:  Liu, Yuqi;  Zhang, Qichao;  Gao, Yinfeng;  Zhao, Dongbin
Adobe PDF(22863Kb)  |  收藏  |  浏览/下载:43/15  |  提交时间:2024/06/03
Reinforcement Learning  Autonomous Driving  Intersection Navigating  
BViT: Broad Attention-Based Vision Transformer 期刊论文
IEEE Transactions on Neural Networks and Learning Systems, 2023, 页码: 1 - 12
作者:  Nannan Li;  Yaran Chen;  Weifan Li;  Zixiang Ding;  Dongbin Zhao;  Shuai Nie
Adobe PDF(2171Kb)  |  收藏  |  浏览/下载:235/64  |  提交时间:2023/06/27
Broad attention  broad connection  image classification  parameter-free attention  vision transformer  
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:292/128  |  提交时间:2023/04/26
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:355/75  |  提交时间:2021/07/05
Rolling horizon evolution  opponent model  reinforcement learning  supervised learning  fighting game  
Invariant Adaptive Dynamic Programming for Discrete-Time Optimal Control 期刊论文
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, 2020, 卷号: 50, 期号: 11, 页码: 3959-3971
作者:  Zhu, Yuanheng;  Zhao, Dongbin;  He, Haibo
Adobe PDF(2079Kb)  |  收藏  |  浏览/下载:217/17  |  提交时间:2021/01/07
Optimal control  Discrete-time systems  Heuristic algorithms  Dynamic programming  Convergence  Artificial intelligence  Nonlinear systems  Adaptive dynamic programming  discrete-time systems  invariant admissibility  optimal control  policy iteration  sum of squares  
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:412/126  |  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  
Control-Limited Adaptive Dynamic Programming for Multi-Battery Energy Storage Systems 期刊论文
IEEE TRANSACTIONS ON SMART GRID, 2019, 卷号: 10, 期号: 4, 页码: 4235-4244
作者:  Zhu, Yuanheng;  Zhao, Dongbin;  Li, Xiangjun;  Wang, Ding
Adobe PDF(973Kb)  |  收藏  |  浏览/下载:320/16  |  提交时间:2019/09/30
Microgrid  energy storage system  multi-battery management system  adaptive dynamic programming  control-limited optimization  
Data-Based Reinforcement Learning for Nonzero-Sum Games With Unknown Drift Dynamics 期刊论文
IEEE TRANSACTIONS ON CYBERNETICS, 2019, 卷号: 49, 期号: 8, 页码: 2874-2885
作者:  Zhang, Qichao;  Zhao, Dongbin
浏览  |  Adobe PDF(1021Kb)  |  收藏  |  浏览/下载:458/136  |  提交时间:2019/07/12
Integral reinforcement learning (IRL)  neural network (NN)  nonzero-sum (NZS) games  off-policy  single-critic  unknown drift dynamics  
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:364/137  |  提交时间:2019/04/22
Reinforcement Learning, Transfer Learning, Curriculum Learning, Neural Network, Game Ai  
Clique-based cooperative multiagent reinforcement learning using factor graphs 期刊论文
IEEE/CAA Journal of Automatica Sinica, 2015, 卷号: 3, 期号: 1, 页码: 248-256
作者:  Zhang,Zhen;  Zhao DB(赵冬斌)
浏览  |  Adobe PDF(707Kb)  |  收藏  |  浏览/下载:237/96  |  提交时间:2017/12/30
Reinforcement Learning  Factor Graphs