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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421-430 订阅
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SOSA: Self-Optimizing learning with Self-adaptive Control for Hierarchical System-on-Chip Management  52
SOSA: Self-Optimizing Learning with Self-Adaptive Control fo...
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52nd Annual ieee/ACM International symposium on Microarchitecture (MICRO)
作者: Donyanavard, Bryan Muck, Tiago Rahmani, Amir M. Dutt, Nikil Sadighi, Armin Maurer, Florian Herkersdorf, Andreas UC Irvine Irvine CA 92697 USA Tech Univ Munich Munich Germany
Resource management strategies for many-core systems dictate the sharing of resources among applications such as power, processing cores, and memory bandwidth in order to achieve system goals. System goals require con... 详细信息
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Virtual Network Function Embedding under Nodal Outage using reinforcement learning
Virtual Network Function Embedding under Nodal Outage using ...
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International symposium on Advanced Networks and Telecommunication Systems (ANTS)
作者: Swarna Bindu Chetty Hamed Ahmadi Avishek Nag School of Electrical and Electronic Engineering University College Dublin Dublin Ireland University of York United Kingdom
With the emergence of various types of applications such as delay-sensitive applications, future communication networks are expected to be increasingly complex and dynamic. Network Function Virtualization (NFV) provid... 详细信息
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UCT-ADP Progressive Bias Algorithm for Solving Gomoku
UCT-ADP Progressive Bias Algorithm for Solving Gomoku
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ieee symposium Series on Computational Intelligence (SSCI)
作者: Xu Cao Yanghao Lin School of Data Science Fudan University Shanghai China
We combine adaptive dynamic programming (ADP), a reinforcement learning method and UCB applied to trees (UCT) algorithm with a more powerful heuristic function based on Progressive Bias method and two pruning strategi... 详细信息
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learning-Based Predictive Control for Discrete-Time Nonlinear Systems With Stochastic Disturbances
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ieee TRANSACTIONS ON NEURAL NETWORKS AND learning SYSTEMS 2018年 第12期29卷 6202-6213页
作者: Xu, Xin Chen, Hong Lian, Chuanqiang Li, Dazi Natl Univ Def Technol Coll Intelligence Sci Changsha 410073 Hunan Peoples R China Jilin Univ NanLing State Key Lab Automot Simulat & Control Changchun 130025 Jilin Peoples R China Jilin Univ NanLing Dept Control Sci & Engn Changchun 130025 Jilin Peoples R China Naval Univ Engn Natl Key Lab Sci & Technol Vessel Integrated Powe Wuhan 430032 Hubei Peoples R China Beijing Univ Chem Technol Dept Automat Beijing 100029 Peoples R China
In this paper, a learning-based predictive control (LPC) scheme is proposed for adaptive optimal control of discrete-time nonlinear systems under stochastic disturbances. The proposed LPC scheme is different from conv... 详细信息
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Online Approximate Optimal Station Keeping of a Marine Craft in the Presence of an Irrotational Current
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ieee TRANSACTIONS ON ROBOTICS 2018年 第2期34卷 486-496页
作者: Walters, Patrick Kamalapurkar, Rushikesh Voight, Forrest Schwartz, Eric M. Dixon, Warren E. Univ Florida Dept Mech & Aerosp Engn Gainesville FL 32611 USA Oklahoma State Univ Dept Mech & Aerosp Engn Stillwater OK 74074 USA Univ Florida Dept Elect & Comp Engn Gainesville FL 32611 USA
Online approximation of the optimal station-keeping strategy for a marine craft subject to an irrotational current is considered. An approximate policy that minimizes a user-defined cost function over an infinite time... 详细信息
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A Low-Power Circuit for adaptive dynamic programming  31
A Low-Power Circuit for Adaptive Dynamic Programming
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31st International Conference on VLSI Design / 17th International Conference on Embedded Systems (VLSID & ES))
作者: Zheng, Nan Mazumder, Pinaki Univ Michigan Elect Engn & Comp Sci Dept Ann Arbor MI 48109 USA
This paper presents a low-power CMOS design for accelerating an adaptive dynamic programming algorithm, called action-dependent heuristic dynamic programming, which is widely employed in many real-life control problem... 详细信息
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On Model-Free reinforcement learning of Reduced-order Optimal Control for Singularly Perturbed Systems  57
On Model-Free Reinforcement Learning of Reduced-order Optima...
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57th ieee Conference on Decision and Control (CDC)
作者: Mukherjee, Sayak Bai, He Chakrabortty, Aranya North Carolina State Univ Dept Elect & Comp Engn Raleigh NC 27695 USA Oklahoma State Univ Sch Mech & Aerosp Engn Stillwater OK 74078 USA
We propose a model-free reduced-order optimal control design for linear time-invariant singularly perturbed (SP) systems using reinforcement learning (RL). Both the state and input matrices of the plant model are assu... 详细信息
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ieee SSCI 2011: symposium Series on Computational Intelligence - ADPRL 2011: 2011 ieee symposium on adaptive dynamic programming and reinforcement learning
IEEE SSCI 2011: Symposium Series on Computational Intelligen...
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symposium Series on Computational Intelligence, ieee SSCI2011 - 2011 ieee symposium on adaptive dynamic programming and reinforcement learning, ADPRL 2011
The proceedings contain 45 papers. The topics discussed include: active learning for personalizing treatment;active exploration by searching for experiments that falsify the computed control policy;optimistic planning...
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Model-Free Value Iteration Solution for dynamic Graphical Games  23
Model-Free Value Iteration Solution for Dynamic Graphical Ga...
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ieee International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)
作者: Abouheaf, Mohammed Gueaieb, Wail Univ Ottawa Sch Elect Engn & Comp Sci Ottawa ON Canada
The dynamic graphical game is a special class of games where agents interact within a communication graph. This paper introduces an online model-free adaptive learning solution for dynamic graphical games. A reinforce... 详细信息
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H Control of Constrained-Input Nonlinear Systems with Unknown Model Based on adaptive dynamic programming  30
H<sub>∞</sub> Control of Constrained-Input Nonlinear System...
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30th Chinese Control and Decision Conference (CCDC)
作者: Pu, Jun Ma, Qingliang Gu, Fan Yu, Zexiang Xian Res Inst High Tech Dept Control Engn Xian 710025 Peoples R China
An adaptive dynamic programming(ADP) algorithm that contain online measurement and off-policy learning two phase is proposed to solve the H-infinity control problem of continuous-time nonlinear system with constrained... 详细信息
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