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检索条件"任意字段=IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning"
1018 条 记 录,以下是441-450 订阅
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Online Finite-Horizon ADP Algorithm for Solving Non-Cooperative Differential Games  36
Online Finite-Horizon ADP Algorithm for Solving Non-Cooperat...
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36th Chinese Control and Decision Conference (CCDC)
作者: Gao, Yingning Ma, Kemao Harbin Inst Technol Control & Simulat Ctr Harbin Peoples R China
In this paper, online reinforcement learning solution to finite-horizon non-cooperative differential games is investigated. The main challenges are that, the Hamilton-Jacobi-Isaacs equation is time-varying, and in ter... 详细信息
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Autonomous learning with Automatically Created Models and a Novel Model Selection
Autonomous Learning with Automatically Created Models and a ...
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ieee symposium Series on Computational Intelligence (ieee SSCI)
作者: Bharatia, Harshal, V ACM Student Member Plano United States
An autonomous learning approach is presented here for expansive problem domains that may undergo frequent changes. It is hard to train and adapt learning-models to changes when the problem domain is very large. With t... 详细信息
来源: 评论
Online Off-Policy reinforcement learning for Optimal Control of Unknown Nonlinear Systems Using Neural Networks
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ieee TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2024年 第8期54卷 5112-5122页
作者: Zhu, Liao Wei, Qinglai Guo, Ping Beijing Normal Univ Int Acad Ctr Complex Syst Zhuhai 519087 Peoples R China Beijing Normal Univ Sch Syst Sci Beijing 100875 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Multimodal Artificial Intelligence S Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Macau Univ Sci & Technol Inst Syst Engn Macau Peoples R China
In this article, a real-time online off-policy reinforcement learning (RL) method is developed for the optimal control problem of unknown continuous-time nonlinear systems. First, by applying the temporal difference t... 详细信息
来源: 评论
Energy- and Cost-Efficient Transmission Strategy for UAV Trajectory Tracking Control: A Deep reinforcement learning Approach
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ieee INTERNET OF THINGS JOURNAL 2023年 第10期10卷 8958-8970页
作者: Zhang, Minkai Wu, Shaohua Jiao, Jian Zhang, Ning Zhang, Qinyu Harbin Inst Technol Shenzhen Dept Elect & Informat Engn Shenzhen 518055 Peoples R China Harbin Inst Technol Shenzhen Guangdong Prov Key Lab Aerosp Commun & Networking Shenzhen 518055 Peoples R China Peng Cheng Lab Dept Broadband Commun Shenzhen 518055 Peoples R China Univ Windsor Dept Elect & Comp Engn Windsor ON N9B 3P4 Canada
this article, we consider a networked control system (NCS) with network-induced delay, in which the control center needs to control the remote unmanned aerial vehicle (UAV) to complete the trajectory tracking task. Th... 详细信息
来源: 评论
Optimal Control for Multi-agent Systems Using Off-Policy reinforcement learning  4
Optimal Control for Multi-agent Systems Using Off-Policy Rei...
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4th International Conference on Control and Robotics (ICCR)
作者: Wang, Hao Chen, Zhiru Wang, Jun Lu, Lijun Li, Mingzhe Henan XJ Metering Co Ltd Xuchang Peoples R China State Grid Shandong Elect Power Co Mkt Serv Ctr Metering Ctr Jinan Peoples R China
To achieve the consensus for discrete-time multi-agent systems, an optimal control policy is designed based on off-policy reinforcement learning. By utilizing centralized learning and decentralized execution, we first... 详细信息
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Evolutionary adaptive dynamic programming Algorithm for Converter Gas Scheduling of Steel Industry  6
Evolutionary Adaptive Dynamic Programming Algorithm for Conv...
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6th International symposium on Advanced Control of Industrial Processes (AdCONIP)
作者: Wang, Tianyu Wang, Linqing Zhao, Jun Wang, Wei Liu, Ying Dalian Univ Technol Sch Control Sci & Engn Dalian 116024 Peoples R China
It is significant to perform an effective scheduling of byproduct gas system in steel industry for reducing cost and protecting environment. The existing studies largely focused on extracting specific knowledge from h... 详细信息
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adaptive dynamic programming and Data-Driven Cooperative Optimal Output Regulation with adaptive Observers  61
Adaptive Dynamic Programming and Data-Driven Cooperative Opt...
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ieee 61st Conference on Decision and Control (CDC)
作者: Qasem, Omar Jebari, Khalid Gao, Weinan Florida Inst Tech nology Dept Mech & Civil Engn Coll Engn & Sci Melbourne FL 32901 USA Florida Inst Technol Dept Aerosp Engn Coll Engn & Sci Melbourne FL 32901 USA
In this paper, a novel adaptive optimal control strategy is proposed to achieve the cooperative optimal output regulation of continuous-time linear multi-agent systems based on adaptive dynamic programming (ADP). The ... 详细信息
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A Biologically-Inspired Computational Model for Transformation Invariant Target Recognition
A Biologically-Inspired Computational Model for Transformati...
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International Joint Conference on Neural Networks
作者: Iftekharuddin, Khan M. Li, Yaqin Univ Memphis Dept Elect & Comp Engn Intelligence Syst & Image Proc Lab Memphis TN 38152 USA
Transformation invariant image recognition has been an active research area due to its widespread applications in a variety of fields such as military operations, robotics' medical practices, geographic scene anal... 详细信息
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Optimal Elevator Group Control via Deep Asynchronous Actor-Critic learning
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ieee TRANSACTIONS ON NEURAL NETWORKS AND learning SYSTEMS 2020年 第12期31卷 5245-5256页
作者: Wei, Qinglai Wang, Lingxiao Liu, Yu Polycarpou, Marios M. Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Qingdao Acad Intelligent Ind Qingdao 266109 Peoples R China Chinese Acad Sci Inst Automat Beijing 100190 Peoples R China Univ Cyprus KIOS Res & Innovat Ctr Excellence CY-1678 Nicosia Cyprus Univ Cyprus Dept Elect & Comp Engn CY-1678 Nicosia Cyprus
In this article, a new deep reinforcement learning (RL) method, called asynchronous advantage actor-critic (A3C) method, is developed to solve the optimal control problem of elevator group control systems (EGCSs). The... 详细信息
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Direct adaptive control of a flexible robot using reinforcement learning
Direct adaptive control of a flexible robot using reinforcem...
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International Conference on Industrial Electronics, Control and Robotics
作者: Subudhi, Bidyadhar Pradhan, Santanu Kumar
This paper proposes a new adaptive control using the concept of reinforcement learning to address adaptivity for varied payload conditions for a two-link flexible manipulator (TLFM). The application of reinforcement l... 详细信息
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