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Frontiers of Mechanical Engineering 2022, Volume 17, Issue 2, doi: 10.1007/s11465-022-0673-7
Keywords: deep reinforcement learning hyper parameter optimization convolutional neural network fault diagnosis
Automated synthesis of steady-state continuous processes using reinforcement learning
Frontiers of Chemical Science and Engineering 2022, Volume 16, Issue 2, Pages 288-302 doi: 10.1007/s11705-021-2055-9
Keywords: automated process synthesis flowsheet synthesis artificial intelligence machine learning reinforcementlearning
Shaojun ZHU; Makoto OHSAKI; Kazuki HAYASHI; Shaohan ZONG; Xiaonong GUO
Frontiers of Structural and Civil Engineering 2022, Volume 16, Issue 11, Pages 1397-1414 doi: 10.1007/s11709-022-0860-y
Keywords: progressive collapse alternate load path demolition planning reinforcement learning graph embedding
Toward Trustworthy Decision-Making for Autonomous Vehicles: A Robust Reinforcement Learning Approach
Xiangkun He,Wenhui Huang,Chen Lv,
Engineering doi: 10.1016/j.eng.2023.10.005
Keywords: Autonomous vehicle Decision-making Reinforcement learning Adversarial attack Safety guarantee
Jian Wu,Yang Yan,Yulong Liu,Yahui Liu,
Engineering doi: 10.1016/j.eng.2023.07.018
Keywords: Obstacle avoidance trajectory planning Inverse reinforcement theory Anthropomorphic Adaptive driving
Recent development on statistical methods for personalized medicine discovery
Yingqi Zhao, Donglin Zeng
Frontiers of Medicine 2013, Volume 7, Issue 1, Pages 102-110 doi: 10.1007/s11684-013-0245-7
It is well documented that patients can show significant heterogeneous responses to treatments so the best treatment strategies may require adaptation over individuals and time. Recently, a number of new statistical methods have been developed to tackle the important problem of estimating personalized treatment rules using single-stage or multiple-stage clinical data. In this paper, we provide an overview of these methods and list a number of challenges.
Keywords: dynamic treatment regimes personalized medicine reinforcement learning Q-learning
Oguzhan Dogru, Kirubakaran Velswamy, Biao Huang
Engineering 2021, Volume 7, Issue 9, Pages 1248-1261 doi: 10.1016/j.eng.2021.04.027
Keywords: Interface tracking Object tracking Occlusion Reinforcement learning Uniform manifold approximation
Embedding expert demonstrations into clustering buffer for effective deep reinforcement learning Research Article
Shihmin WANG, Binqi ZHAO, Zhengfeng ZHANG, Junping ZHANG, Jian PU
Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 11, Pages 1541-1556 doi: 10.1631/FITEE.2300084
Keywords: Reinforcement learning Sample efficiency Sampling process Clustering methods Autonomous driving
A home energy management approach using decoupling value and policy in reinforcement learning
熊珞琳,唐漾,刘臣胜,毛帅,孟科,董朝阳,钱锋
Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 9, Pages 1261-1272 doi: 10.1631/FITEE.2200667
Keywords: Home energy system Electric vehicle Reinforcement learning Generalization
Jingda Wu, Zhiyu Huang, Zhongxu Hu, Chen Lv
Engineering 2023, Volume 21, Issue 2, Pages 75-91 doi: 10.1016/j.eng.2022.05.017
Due to its limited intelligence and abilities, machine learning is currentlytraining loop of artificial intelligence (AI), leveraging human intelligence to further advance machine learningIn this study, a real-time human-guidance-based (Hug)-deep reinforcement learning (DRL) method is developedvalidated by human-in-the-loop experiments with 40 subjects and compared with other state-of-the-art learning
Keywords: Human-in-the-loop AI Deep reinforcement learning Human guidance Autonomous driving
Coach-assisted multi-agent reinforcement learning framework for unexpected crashed agents Research Article
Jian ZHAO, Youpeng ZHAO, Weixun WANG, Mingyu YANG, Xunhan HU, Wengang ZHOU, Jianye HAO, Houqiang LI
Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 7, Pages 1032-1042 doi: 10.1631/FITEE.2100594
Keywords: Multi-agent system Reinforcement learning Unexpected crashed agents
Cooperative channel assignment for VANETs based on multiagent reinforcement learning Research Articles
Yun-peng Wang, Kun-xian Zheng, Da-xin Tian, Xu-ting Duan, Jian-shan Zhou,ypwang@buaa.edu.cn,zhengkunxian@buaa.edu.cn,dtian@buaa.edu.cn,duanxuting@buaa.edu.cn
Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 7, Pages 1047-1058 doi: 10.1631/FITEE.1900308
Keywords: Vehicular ad-hoc networks Reinforcement learning Dynamic channel assignment Multichannel
Stochastic pedestrian avoidance for autonomous vehicles using hybrid reinforcement learning Research Article
Huiqian LI, Jin HUANG, Zhong CAO, Diange YANG, Zhihua ZHONG,lihq20@mails.tsinghua.edu.cn,huangjin@tsinghua.edu.cn,caoc15@mails.tsinghua.edu.cn,ydg@tsinghua.edu.cn
Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 1, Pages 131-140 doi: 10.1631/FITEE.2200128
Keywords: Pedestrian Hybrid reinforcement learning Autonomous vehicles Decision-making
Behavioral control task supervisor with memory based on reinforcement learning for human–multi-robot Research Article
Jie HUANG, Zhibin MO, Zhenyi ZHANG, Yutao CHEN,yutao.chen@fzu.edu.cn
Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 8, Pages 1174-1188 doi: 10.1631/FITEE.2100280
Keywords: Human– multi-robot coordination systems Null-space-based behavioral control Task supervisor Reinforcementlearning Knowledge base
MDLB: a metadata dynamic load balancing mechanism based on reinforcement learning Research Articles
Zhao-qi Wu, Jin Wei, Fan Zhang, Wei Guo, Guang-wei Xie,17034203@qq.com
Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 7, Pages 963-1118 doi: 10.1631/FITEE.1900121
Keywords: 面向对象的存储系统;元数据;动态负载均衡;强化学习;Q_learning
Title Author Date Type Operation
A new automatic convolutional neural network based on deep reinforcement learning for fault diagnosis
Journal Article
Automated synthesis of steady-state continuous processes using reinforcement learning
Journal Article
Deep reinforcement learning-based critical element identification and demolition planning of frame structures
Shaojun ZHU; Makoto OHSAKI; Kazuki HAYASHI; Shaohan ZONG; Xiaonong GUO
Journal Article
Toward Trustworthy Decision-Making for Autonomous Vehicles: A Robust Reinforcement Learning Approach
Xiangkun He,Wenhui Huang,Chen Lv,
Journal Article
Anthropomorphic Obstacle Avoidance Trajectory Planning for Adaptive Driving Scenarios Based on Inverse ReinforcementLearning Theory
Jian Wu,Yang Yan,Yulong Liu,Yahui Liu,
Journal Article
Recent development on statistical methods for personalized medicine discovery
Yingqi Zhao, Donglin Zeng
Journal Article
Actor–Critic Reinforcement Learning and Application in Developing Computer-Vision-Based Interface Tracking
Oguzhan Dogru, Kirubakaran Velswamy, Biao Huang
Journal Article
Embedding expert demonstrations into clustering buffer for effective deep reinforcement learning
Shihmin WANG, Binqi ZHAO, Zhengfeng ZHANG, Junping ZHANG, Jian PU
Journal Article
A home energy management approach using decoupling value and policy in reinforcement learning
熊珞琳,唐漾,刘臣胜,毛帅,孟科,董朝阳,钱锋
Journal Article
Toward Human-in-the-loop AI: Enhancing Deep Reinforcement Learning Via Real-time Human Guidance for Autonomous
Jingda Wu, Zhiyu Huang, Zhongxu Hu, Chen Lv
Journal Article
Coach-assisted multi-agent reinforcement learning framework for unexpected crashed agents
Jian ZHAO, Youpeng ZHAO, Weixun WANG, Mingyu YANG, Xunhan HU, Wengang ZHOU, Jianye HAO, Houqiang LI
Journal Article
Cooperative channel assignment for VANETs based on multiagent reinforcement learning
Yun-peng Wang, Kun-xian Zheng, Da-xin Tian, Xu-ting Duan, Jian-shan Zhou,ypwang@buaa.edu.cn,zhengkunxian@buaa.edu.cn,dtian@buaa.edu.cn,duanxuting@buaa.edu.cn
Journal Article
Stochastic pedestrian avoidance for autonomous vehicles using hybrid reinforcement learning
Huiqian LI, Jin HUANG, Zhong CAO, Diange YANG, Zhihua ZHONG,lihq20@mails.tsinghua.edu.cn,huangjin@tsinghua.edu.cn,caoc15@mails.tsinghua.edu.cn,ydg@tsinghua.edu.cn
Journal Article
Behavioral control task supervisor with memory based on reinforcement learning for human–multi-robot
Jie HUANG, Zhibin MO, Zhenyi ZHANG, Yutao CHEN,yutao.chen@fzu.edu.cn
Journal Article