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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311-320 订阅
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A scalable model-free recurrent neural network framework for solving POMDPs
A scalable model-free recurrent neural network framework for...
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ieee International symposium on Approximate dynamic programming and reinforcement learning
作者: Liu, Zhenzhen Elhanany, Itamar Univ Tennessee Dept Elect & Comp Engn Knoxville TN 37996 USA
This paper presents a framework for obtaining an optimal policy in model-free Partially Observable Markov Decision Problems (POMDPs) using a recurrent neural network (RNN). A Q-function approximation approach is taken... 详细信息
来源: 评论
A Two Stage learning Technique for Dual learning in the Pursuit-Evasion Differential Game
A Two Stage Learning Technique for Dual Learning in the Purs...
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ieee symposium on adaptive dynamic programming and reinforcement learning (ADPRL)
作者: Al-Talabi, Ahmad A. Schwartz, Howard M. Carleton Univ Dept Syst & Comp Engn 1125 Colonel By Dr Ottawa ON K1S 5B6 Canada Univ Baghdad Al Khwarizmi Coll Engn Mechatron Engn Dept Baghdad Iraq
This paper addresses the case of dual learning in the pursuit-evasion (PE) differential game and examines how fast the players can learn their default control strategies. The players should learn their default control... 详细信息
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Event-Driven H-Constrained Control Using adaptive Critic learning
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ieee TRANSACTIONS ON CYBERNETICS 2021年 第10期51卷 4860-4872页
作者: Yang, Xiong He, Haibo Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China Univ Rhode Isl Dept Elect Comp & Biomed Engn Kingston RI 02881 USA
This article considers an event-driven H-infinity control problem of continuous-time nonlinear systems with asymmetric input constraints. Initially, the H-infinity-constrained control problem is converted into a two-p... 详细信息
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Computing and Communication Cost-Aware Service Migration Enabled by Transfer reinforcement learning for dynamic Vehicular Edge Computing Networks
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ieee TRANSACTIONS ON MOBILE COMPUTING 2024年 第1期23卷 257-269页
作者: Peng, Yan Tang, Xiaogang Zhou, Yiqing Li, Jintao Qi, Yanli Liu, Ling Lin, Hai Inst Comp Technol State Key Lab Processors Beijing 100190 Peoples R China Beijing Key Lab Mobile Comp & Pervas Device Beijing 100190 Peoples R China Space Engn Univ Sch Aerosp Informat Beijing 100015 Peoples R China
Due to the high mobility of vehicles, service migration is inevitable in vehicular edge computing (VEC) networks. Frequent service migrations incur prohibitive migration cost including the computing cost (e.g., increa... 详细信息
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Hamiltonian-Driven adaptive dynamic programming With Approximation Errors
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ieee TRANSACTIONS ON CYBERNETICS 2022年 第12期52卷 13762-13773页
作者: Yang, Yongliang Modares, Hamidreza Vamvoudakis, Kyriakos G. He, Wei Xu, Cheng-Zhong Wunsch, Donald C. Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100083 Peoples R China Univ Sci & Technol Beijing Inst Artificial Intelligence Beijing 100083 Peoples R China Michigan State Univ Mech Engn Dept E Lansing MI 48824 USA Georgia Tech Daniel Guggenheim Sch Aerosp Engn Atlanta GA 30332 USA Univ Macau Fac Sci & Technol State Key Lab Internet Things Smart City Macau Peoples R China Missouri Univ Sci & Technol Dept Elect & Comp Engn Rolla MO 65401 USA
In this article, we consider an iterative adaptive dynamic programming (ADP) algorithm within the Hamiltonian-driven framework to solve the Hamilton-Jacobi-Bellman (HJB) equation for the infinite-horizon optimal contr... 详细信息
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Online adaptive learning of optimal control solutions using integral reinforcement learning
Online adaptive learning of optimal control solutions using ...
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作者: Vamvoudakis, Kyriakos G. Vrabie, Draguna Lewis, Frank L. Automation and Robotics Research Institute University of Texas at Arlington Fort Worth TX 76118 United States
In this paper we introduce an online algorithm that uses integral reinforcement knowledge for learning the continuous-time optimal control solution for nonlinear systems with infinite horizon costs and partial knowled... 详细信息
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reinforcement learning Control of Robotic Knee With Human-in-the-Loop by Flexible Policy Iteration
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ieee TRANSACTIONS ON NEURAL NETWORKS AND learning SYSTEMS 2022年 第10期33卷 5873-5887页
作者: Gao, Xiang Si, Jennie Wen, Yue Li, Minhan Huang, He Arizona State Univ Dept Elect Comp & Energy Engn Tempe AZ 85287 USA North Carolina State Univ Dept Biomed Engn Raleigh NC 27695 USA Univ N Carolina Chapel Hill NC 27599 USA
We are motivated by the real challenges presented in a human-robot system to develop new designs that are efficient at data level and with performance guarantees, such as stability and optimality at system level. Exis... 详细信息
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adaptive Configuration with Deep reinforcement learning in Software-Defined Time-Sensitive Networking
Adaptive Configuration with Deep Reinforcement Learning in S...
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ieee/IFIP Network Operations and Management symposium (NOMS)
作者: Guo, Mengjie Shou, Guochu Liu, Yaqiong Hu, Yihong Beijing Univ Posts & Telecommun Sch Informat & Commun Engn Beijing Peoples R China
Time-sensitive networking (TSN) is very appealing to industrial networks due to its support for deterministic transmission based on Ethernet. The implementation of determinism typically demands for precise configurati... 详细信息
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Mutual reinforcement learning with Heterogenous Agents  7
Mutual Reinforcement Learning with Heterogenous Agents
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7th ieee International Conference on Smart Computing (SMARTCOMP)
作者: Reid, Cameron Mukhopadhyay, Snehasis Indiana Univ Purdue Univ Sch Comp & Informat Sci Indianapolis IN 46202 USA
Mutual learning is an emerging technique for allowing intelligent systems to learn from each other, giving rise to improved performance. In this paper, we explore mutual reinforcement learning between systems which us... 详细信息
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Approximate Nash Solutions for Multiplayer Mixed-Zero-Sum Game With reinforcement learning
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ieee TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2019年 第12期49卷 2739-2750页
作者: Lv, Yongfeng Ren, Xuemei Beijing Inst Technol Sch Automat Beijing 100081 Peoples R China
Inspired by Nash game theory, a multiplayer mixed-zero-sum (MZS) nonlinear game considering both two situations [zero-sum and nonzero-sum (NZS) Nash games] is proposed in this paper. A synchronous reinforcement learni... 详细信息
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