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检索条件"任意字段=2014 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning, ADPRL 2014"
247 条 记 录,以下是11-20 订阅
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Nonparametric Infinite Horizon Kullback-Leibler Stochastic Control
Nonparametric Infinite Horizon Kullback-Leibler Stochastic C...
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ieee symposium on adaptive dynamic programming and reinforcement learning (adprl)
作者: Pan, Yunpeng Theodorou, Evangelos A. Georgia Inst Technol Daniel Guggenheim Sch Aerosp Engn Atlanta GA 30332 USA
We present two nonparametric approaches to Kullback-Leibler (KL) control, or linearly-solvable Markov decision problem (LMDP) based on Gaussian processes (GP) and Nystrom approximation. Compared to recently developed ... 详细信息
来源: 评论
adaptive dynamic programming for Discrete-time LQR Optimal Tracking Control Problems with Unknown dynamics
Adaptive Dynamic Programming for Discrete-time LQR Optimal T...
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ieee symposium on adaptive dynamic programming and reinforcement learning (adprl)
作者: Liu, Yang Luo, Yanhong Zhang, Huaguang Northeastern Univ Sch Informat Sci & Engn Shenyang 110819 Liaoning Peoples R China
In this paper, an optimal tracking control approach based on adaptive dynamic programming (ADP) algorithm is proposed to solve the linear quadratic regulation (LQR) problems for unknown discrete-time systems in an onl... 详细信息
来源: 评论
Convergent reinforcement learning Control with Neural Networks and Continuous Action Search
Convergent Reinforcement Learning Control with Neural Networ...
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ieee symposium on adaptive dynamic programming and reinforcement learning (adprl)
作者: Lee, Minwoo Anderson, Charles W. Colorado State Univ Dept Comp Sci Ft Collins CO 80523 USA
We combine a convergent TD-learning method and direct continuous action search with neural networks for function approximation to obtain both stability and generalization over inexperienced state-action pairs. We exte... 详细信息
来源: 评论
Data-Driven Partially Observable dynamic Processes Using adaptive dynamic programming
Data-Driven Partially Observable Dynamic Processes Using Ada...
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ieee symposium on adaptive dynamic programming and reinforcement learning (adprl)
作者: Zhong, Xiangnan Ni, Zhen Tang, Yufei He, Haibo Univ Rhode Isl Dept Elect Comp & Biomed Engn Kingston RI 02881 USA
adaptive dynamic programming (ADP) has been widely recognized as one of the "core methodologies" to achieve optimal control for intelligent systems in Markov decision process (MDP). Generally, ADP control de... 详细信息
来源: 评论
Neural-Network-Based adaptive dynamic Surface Control for MIMO Systems with Unknown Hysteresis
Neural-Network-Based Adaptive Dynamic Surface Control for MI...
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ieee symposium on adaptive dynamic programming and reinforcement learning (adprl)
作者: Liu, Lei Wang, Zhanshan Shen, Zhengwei Northeastern Univ Coll Informat Sci & Engn Shenyang Liaoning Peoples R China
This paper focuses on the composite adaptive tracking control for a class of nonlinear multiple-input-multiple-output (MIMO) systems with unknown backlash-like hysteresis nonlinearities. A dynamic surface control meth... 详细信息
来源: 评论
Theoretical Analysis of a reinforcement learning based Switching Scheme
Theoretical Analysis of a Reinforcement Learning based Switc...
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ieee symposium on adaptive dynamic programming and reinforcement learning (adprl)
作者: Heydari, Ali South Dakota Sch Mines & Technol Dept Mech Engn Rapid City SD 57701 USA
A reinforcement learning based scheme for optimal switching with an infinite-horizon cost function is briefly proposed in this paper. Several theoretical questions are shown to arise regarding its convergence, optimal... 详细信息
来源: 评论
Model-Based Multi-Objective reinforcement learning
Model-Based Multi-Objective Reinforcement Learning
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ieee symposium on adaptive dynamic programming and reinforcement learning (adprl)
作者: Wiering, Marco A. Withagen, Maikel Drugan, Madalina M. Univ Groningen Inst Artificial Intelligence NL-9700 AB Groningen Netherlands Vrije Univ Brussel Artificial Intelligence Lab Ixelles Brunei
This paper describes a novel multi-objective reinforcement learning algorithm. The proposed algorithm first learns a model of the multi-objective sequential decision making problem, after which this learned model is u... 详细信息
来源: 评论
Model-free Q-learning over Finite Horizon for Uncertain Linear Continuous-time Systems
Model-free <i>Q</i>-learning over Finite Horizon for Uncerta...
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ieee symposium on adaptive dynamic programming and reinforcement learning (adprl)
作者: Xu, Hao Jagannathan, S. Texas A&M Univ Coll Sci & Engn Corpus Christi TX 78412 USA Missouri Univ Sci & Technol Dept Elect & Comp Engn Rolla MO USA
In this paper, a novel optimal control over finite horizon has been introduced for linear continuous-time systems by using adaptive dynamic programming (ADP). First, a new time-varying Q-function parameterization and ... 详细信息
来源: 评论
Continuous-Time Differential dynamic programming with Terminal Constraints
Continuous-Time Differential Dynamic Programming with Termin...
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ieee symposium on adaptive dynamic programming and reinforcement learning (adprl)
作者: Sun, Wei Theodorou, Evangelos A. Tsiotras, Panagiotis
In this work, we revisit the continuous-time Differential dynamic programming (DDP) approach for solving optimal control problems with terminal state constraints. We derive two algorithms, each for different order of ... 详细信息
来源: 评论
adaptive Fault Identification for a Class of Nonlinear dynamic Systems
Adaptive Fault Identification for a Class of Nonlinear Dynam...
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ieee symposium on adaptive dynamic programming and reinforcement learning (adprl)
作者: Wu, Li-Bing Ye, Dan Zhao, Xin-Gang Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Liaoning Peoples R China Univ Sci & Technol Liaoning Coll Sci Anshan 114051 Liaoning Peoples R China Chinese Acad Sci State Key Lab Robot Shenyang 110016 Liaoning Peoples R China Chinese Acad Sci Shenyang Inst Automat Shenyang 110016 Liaoning Peoples R China
This paper is concerned with the diagnosis problem of actuator faults for a class of nonlinear systems. It is assumed that the upper bound of the Lipschtiz constant of the nonlinearity in the faulty system is unknown.... 详细信息
来源: 评论