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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91-100 订阅
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Multiagent reinforcement learning in extensive form games with complete information
Multiagent reinforcement learning in extensive form games wi...
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ieee symposium on adaptive dynamic programming and reinforcement learning
作者: Akramizadeh, Ali Menhaj, Mohammad-B. Afshar, Ahmad Polytech Univ Tehran EE Dept Ctr Computat Intelligence & Large Scale Syst Tehran Iran
Recent developments in multiagent reinforcement learning, mostly concentrate on normal form games or restrictive hierarchical form games. In this paper, we use the well known Q-learning in extensive form games which a... 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
adaptive dynamic programming for Finite-Horizon Optimal Control of Discrete-Time Nonlinear Systems with ε-Error Bound
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ieee TRANSACTIONS ON NEURAL NETWORKS 2011年 第1期22卷 24-36页
作者: Wang, Fei-Yue Jin, Ning Liu, Derong Wei, Qinglai Chinese Acad Sci Inst Automat Key Lab Complex Syst & Intelligence Sci Beijing 100190 Peoples R China Univ Illinois Dept Elect & Comp Engn Chicago IL 60607 USA
In this paper, we study the finite-horizon optimal control problem for discrete-time nonlinear systems using the adaptive dynamic programming (ADP) approach. The idea is to use an iterative ADP algorithm to obtain the... 详细信息
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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
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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
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Higher-level application of adaptive dynamic programming/reinforcement learning - A next phase for controls and system identification?
Higher-level application of Adaptive Dynamic Programming/Rei...
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ieee symposium on adaptive dynamic programming and reinforcement learning
作者: Lendaris, George G. Systems Science Graduate Program Portland State University Portland OR United States
In previous work it was shown that adaptive-Critic-type Approximate dynamic programming could be applied in a higher-level way to create autonomous agents capable of using experience to discern context and select opti... 详细信息
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Data-Driven Optimal Consensus Control for Discrete-Time Multi-Agent Systems With Unknown dynamics Using reinforcement learning Method
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ieee TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2017年 第5期64卷 4091-4100页
作者: Zhang, Huaguang Jiang, He Luo, Yanhong Xiao, Geyang Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China
This paper investigates the optimal consensus control problem for discrete-time multi-agent systems with completely unknown dynamics by utilizing a data-driven reinforcement learning method. It is known that the optim... 详细信息
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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
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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
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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
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