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A new automatic convolutional neural network based on deep reinforcement learning for fault diagnosis

Frontiers of Mechanical Engineering 2022, Volume 17, Issue 2, doi: 10.1007/s11465-022-0673-7

Abstract: CNN (ACNN) for fault diagnosis, which can automatically tune its three key hyper parameters, namely, learningFirst, a new deep reinforcement learning (DRL) is developed, and it constructs an agent aiming at controllingACNN is also compared with other published machine learning (ML) and deep learning (DL) methods.

Keywords： deep reinforcement learning     hyper parameter optimization     convolutional neural network     fault diagnosis    

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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

Abstract: The present work demonstrates how reinforcement learning can be used for automated flowsheet synthesis

Keywords： automated process synthesis     flowsheet synthesis     artificial intelligence     machine learning     reinforcementlearning    

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Deep reinforcement learning-based critical element identification and demolition planning of frame structures

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

Abstract: quantitative indices considering the severity of the ultimate collapse scenario are proposed using reinforcementlearning and graph embedding.index-based methods, it is demonstrated that the computational cost is considerably reduced because the reinforcementlearning model is trained offline.Besides, it is proved that the Q values produced by the reinforcement learning agent can make

Keywords： progressive collapse     alternate load path     demolition planning     reinforcement learning     graph embedding    

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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

Abstract: Therefore, we present a novel robust reinforcement learning approach with safety guarantees to attain

Keywords： Autonomous vehicle     Decision-making     Reinforcement learning     Adversarial attack     Safety guarantee    

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Anthropomorphic Obstacle Avoidance Trajectory Planning for Adaptive Driving Scenarios Based on Inverse ReinforcementLearning Theory

Jian Wu,Yang Yan,Yulong Liu,Yahui Liu,

Engineering doi: 10.1016/j.eng.2023.07.018

Abstract: , a trajectory expectation feature-matching algorithm is proposed that uses maximum entropy inverse reinforcementlearning theory to learn the extracted expert-demonstrated trajectories and achieve automatic acquisition

Keywords： Obstacle avoidance trajectory planning     Inverse reinforcement theory     Anthropomorphic     Adaptive driving    

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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

Abstract:

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    

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Actor–Critic Reinforcement Learning and Application in Developing Computer-Vision-Based Interface Tracking Article

Oguzhan Dogru, Kirubakaran Velswamy, Biao Huang

Engineering 2021, Volume 7, Issue 9,   Pages 1248-1261 doi: 10.1016/j.eng.2021.04.027

Abstract: A reinforcement learning (RL) agent successfully tracks an interface between two liquids, which is oftenUnlike supervised learning (SL) methods that rely on a huge number of parameters, this approach requires

Keywords： Interface tracking     Object tracking     Occlusion     Reinforcement learning     Uniform manifold approximation    

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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

Abstract: As one of the most fundamental topics in (RL), is essential to the deployment of deep RL algorithms. Unlike most existing exploration methods that sample an action from different types of posterior distributions, we focus on the policy and propose an efficient selective sampling approach to improve by modeling the internal hierarchy of the environment. Specifically, we first employ in the policy to generate an action candidate set. Then we introduce a clustering buffer for modeling the internal hierarchy, which consists of on-policy data, off-policy data, and expert data to evaluate actions from the clusters in the action candidate set in the exploration stage. In this way, our approach is able to take advantage of the supervision information in the expert demonstration data. Experiments on six different continuous locomotion environments demonstrate superior performance and faster convergence of selective sampling. In particular, on the LGSVL task, our method can reduce the number of convergence steps by 46.7% and the convergence time by 28.5%. Furthermore, our code is open-source for reproducibility. The code is available at https://github.com/Shihwin/SelectiveSampling.

Keywords： Reinforcement learning     Sample efficiency     Sampling process     Clustering methods     Autonomous driving    

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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

Abstract: In this paper, a novel home energy management (HEM) approach is proposed based on a data-driven deep reinforcementlearning method.

Keywords： Home energy system     Electric vehicle     Reinforcement learning     Generalization    

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Toward Human-in-the-loop AI: Enhancing Deep Reinforcement Learning Via Real-time Human Guidance for Autonomous Article

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

Abstract:

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    

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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

Abstract: We have designed three coaching strategies (fixed crash rate, curriculum learning, and adaptive crashdemonstrate the efficacy of the adaptive strategy compared with the fixed crash rate strategy and curriculum learning

Keywords： Multi-agent system     Reinforcement learning     Unexpected crashed agents    

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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

Abstract: (DCA) plays a key role in extending vehicular ad-hoc network capacity and mitigating congestion. However, channel assignment under vehicular direct communication scenarios faces mutual influence of large-scale nodes, the lack of centralized coordination, unknown global state information, and other challenges. To solve this problem, a multiagent (RL) based cooperative DCA (RL-CDCA) mechanism is proposed. Specifically, each vehicular node can successfully learn the proper strategies of channel selection and backoff adaptation from the real-time channel state information (CSI) using two cooperative RL models. In addition, neural networks are constructed as nonlinear Q-function approximators, which facilitates the mapping of the continuously sensed input to the mixed policy output. Nodes are driven to locally share and incorporate their individual rewards such that they can optimize their policies in a distributed collaborative manner. Simulation results show that the proposed multiagent RL-CDCA can better reduce the one-hop packet delay by no less than 73.73%, improve the packet delivery ratio by no less than 12.66% on average in a highly dense situation, and improve the fairness of the global network resource allocation.

Keywords： Vehicular ad-hoc networks     Reinforcement learning     Dynamic channel assignment     Multichannel    

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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

Abstract: Classical avoidance strategies cannot handle uncertainty, and learning-based methods lack performanceThe method integrates the rule-based strategy and reinforcement learning strategy.

Keywords： Pedestrian     Hybrid reinforcement learning     Autonomous vehicles     Decision-making    

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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

Abstract: In this study, a novel (RLTS) with memory in a behavioral control framework is proposed for ; (HMRCSs). Existing HMRCSs suffer from high decision-making time cost and large task tracking errors caused by repeated human intervention, which restricts the autonomy of multi-robot systems (MRSs). Moreover, existing s in the (NSBC) framework need to formulate many priority-switching rules manually, which makes it difficult to realize an optimal behavioral priority adjustment strategy in the case of multiple robots and multiple tasks. The proposed RLTS with memory provides a detailed integration of the deep Q-network (DQN) and long short-term memory (LSTM) within the NSBC framework, to achieve an optimal behavioral priority adjustment strategy in the presence of task conflict and to reduce the frequency of human intervention. Specifically, the proposed RLTS with memory begins by memorizing human intervention history when the robot systems are not confident in emergencies, and then reloads the history information when encountering the same situation that has been tackled by humans previously. Simulation results demonstrate the effectiveness of the proposed RLTS. Finally, an experiment using a group of mobile robots subject to external noise and disturbances validates the effectiveness of the proposed RLTS with memory in uncertain real-world environments.

Keywords： Human–     multi-robot coordination systems     Null-space-based behavioral control     Task supervisor     Reinforcementlearning     Knowledge base    

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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

Abstract: With the growing amount of information and data, s have been widely used in many applications, including the Google File System, Amazon S3, Hadoop Distributed File System, and Ceph, in which load balancing of plays an important role in improving the input/output performance of the entire system. Unbalanced load on the server leads to a serious bottleneck problem for system performance. However, most existing load balancing strategies, which are based on subtree segmentation or hashing, lack good dynamics and adaptability. In this study, we propose a (MDLB) mechanism based on (RL). We learn that the algorithm and our RL-based strategy consist of three modules, i.e., the policy selection network, load balancing network, and parameter update network. Experimental results show that the proposed MDLB algorithm can adjust the load dynamically according to the performance of the servers, and that it has good adaptability in the case of sudden change of data volume.

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    

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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

MDLB: a metadata dynamic load balancing mechanism based on reinforcement learning

Zhao-qi Wu, Jin Wei, Fan Zhang, Wei Guo, Guang-wei Xie,17034203@qq.com

Journal Article