CASIA OpenIR
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Multi-Agent Reinforcement Learning Based on Clustering in Two-Player Games 会议论文
, Xiamen, China, 2019-12-6
作者:  Li WF(李伟凡);  Zhu YH(朱圆恒);  Zhao DB(赵冬斌)
Adobe PDF(488Kb)  |  收藏  |  浏览/下载:122/40  |  提交时间:2023/06/28
reinforcement learning  unsupervised clustering  matrix game  
Dynamic-horizon model-based value estimation with latent imagination 期刊论文
IEEE Transactions on Neural Networks and Learning Systems, 2022, 页码: 1-14
作者:  Wang JJ(王俊杰);  Zhang QC(张启超);  Zhao DB(赵冬斌)
Adobe PDF(2305Kb)  |  收藏  |  浏览/下载:162/60  |  提交时间:2023/05/30
Latent world model  model-based value expansion (MVE)  reinforcement learning  reinforcement learning  
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:223/113  |  提交时间:2023/04/26
Soft Contrastive Learning with Q-irrelevance Abstraction for Reinforcement Learning 期刊论文
IEEE Transactions on Cognitive and Developmental Systems, 2022, 页码: doi={10.1109/TCDS.2022.3218940}
作者:  Minsong Liu;  Luntong Li;  Shuai Hao;  Yuanheng Zhu;  Dongbin Zhao
Adobe PDF(12013Kb)  |  收藏  |  浏览/下载:75/19  |  提交时间:2023/04/26
Boost 3-D Object Detection via Point Clouds Segmentation and Fused 3-D GIoU-L-1 Loss 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2022, 卷号: 33, 期号: 2, 页码: 762-773
作者:  Chen, Yaran;  Li, Haoran;  Gao, Ruiyuan;  Zhao, Dongbin
Adobe PDF(2082Kb)  |  收藏  |  浏览/下载:245/51  |  提交时间:2022/03/17
3-D object detection  generalized Intersection of Union (GIoU) loss  segmentation  
Missile guidance with assisted deep reinforcement learning for head-on interception of maneuvering target 期刊论文
COMPLEX & INTELLIGENT SYSTEMS, 2021, 页码: 12
作者:  Li, Weifan;  Zhu, Yuanheng;  Zhao, Dongbin
Adobe PDF(1431Kb)  |  收藏  |  浏览/下载:286/51  |  提交时间:2021/12/28
Reinforcement learning  Missile guidance  Auxiliary learning  Self-imitation learning  
Comparison of methods to efficient graph SLAM under general optimization framework 会议论文
YAC 2017
作者:  Haoran Li;  Qichao Zhang;  Dongbin Zhao
浏览  |  Adobe PDF(151Kb)  |  收藏  |  浏览/下载:903/517  |  提交时间:2017/12/31
Optimization  Slam  Pose Graph  
深度强化学习综述:兼论计算机围棋的发展 期刊论文
控制理论与应用, 2016, 卷号: 33, 期号: 6, 页码: 701-717
作者:  赵冬斌;  邵坤;  朱圆恒;  李栋;  陈亚冉;  王海涛;  刘德荣;  周彤;  王成红
浏览  |  Adobe PDF(2816Kb)  |  收藏  |  浏览/下载:1754/647  |  提交时间:2017/09/13
深度强化学习  初弈号  深度学习  强化学习  人工智能  
Online reinforcement learning for continuous-state systems 专著章节/文集论文
出自: Frontiers of Intelligent Control and Information Processing, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore:World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, 2014
作者:  Yuanheng Zhu;  Zhao DB(赵冬斌)
Adobe PDF(24150Kb)  |  收藏  |  浏览/下载:249/27  |  提交时间:2017/09/13
Deep Reinforcement Learning With Visual Attention for Vehicle Classification 期刊论文
IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS, 2017, 卷号: 9, 期号: 4, 页码: 356-367
作者:  Zhao, Dongbin;  Chen, Yaran;  Lv, Le
浏览  |  Adobe PDF(3192Kb)  |  收藏  |  浏览/下载:1036/539  |  提交时间:2017/05/08
Convolutional Neural Network (Cnn)  Reinforcement Learning  Vehicle Classification  Visual Attention