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检索条件"任意字段=IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning"
1027 条 记 录,以下是901-910 订阅
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Optimal control applied to Wheeled Mobile Vehicles
Optimal control applied to Wheeled Mobile Vehicles
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ieee International symposium on Intelligent Signal Processing
作者: Gomez, M. Martinez, T. Sanchez, S. Meziat, D. Univ Alcala Escuela Politecn Super Dept Automat Alcala De Henares Spain Univ Alicante Escuela Politecn Super Ingn Sistemas Teoria Sefial Dept Fis Alicante Spain
The goal of the work described in this paper is to develop a particular optimal control technique based on a Cell. Mapping technique in combination with the Q-learning reinforcement learning method to control wheeled ... 详细信息
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Short-term stock market timing prediction under reinforcement learning schemes
Short-term stock market timing prediction under reinforcemen...
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2007 ieee symposium on Approximate dynamic programming and reinforcement learning, ADPRL 2007
作者: Hailin, Li Dagli, Cihan H. Enke, David Department of Engineering Management and Systems Engineering University of Missouri-Rolla Rolla MO 65409-0370 United States
There are fundamental difficulties when only using a supervised learning philosophy to predict financial stock short-term movements. We present a reinforcement-oriented forecasting framework in which the solution is c... 详细信息
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reinforcement-learning-based magneto-hydrodynamic control of hypersonic flows
Reinforcement-learning-based magneto-hydrodynamic control of...
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ieee International symposium on Approximate dynamic programming and reinforcement learning
作者: Kulkarni, Nilesh V. Phan, Minh Q. NASA Ames Res Ctr QSS Grp Inc Moffett Field CA 94035 USA Dartmouth Coll Thayer Sch Engn Hanover NH 03755 USA
In this work, we design a policy-iteration-based Q-learning approach for on-line optimal control of ionized hypersonic flow at the inlet of a scramjet engine. Magneto-hydrodynamics (MHD) has been recently proposed as ... 详细信息
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Kernel-based least squares policy iteration for reinforcement learning
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ieee TRANSACTIONS ON NEURAL NETWORKS 2007年 第4期18卷 973-992页
作者: Xu, Xin Hu, Dewen Lu, Xicheng Natl Univ Def Technol Coll Mechatron & Automat Inst Automat Changsha 410073 Peoples R China Natl Univ Def Technol Coll Mechatron & Automat Dept Automat Control Changsha 410073 Peoples R China Natl Univ Def Technol Sch Comp Changsha 410073 Peoples R China
In this paper, we present a kernel-based least squares policy iteration (KLSPI) algorithm for reinforcement learning (RL) in large or continuous state spaces, which can be used to realize adaptive feedback control of ... 详细信息
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2007 ieee ADPRL International Program Committee Members
2007 IEEE ADPRL International Program Committee Members
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ieee symposium on adaptive dynamic programming and reinforcement learning, (ADPRL)
Provides a listing of current committee members.
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programming and reinforcement learning
Programming and Reinforcement Learning
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ieee symposium on adaptive dynamic programming and reinforcement learning, (ADPRL)
Welcome to ADPRL 2007 - the very first ieee International symposium on Approximate dynamic programming and reinforcement learning. The area of approximate dynamic programming and reinforcement learning is a fusion of ...
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Particle Swarn Optimized adaptive dynamic programming
Particle Swarn Optimized Adaptive Dynamic Programming
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ieee symposium on adaptive dynamic programming and reinforcement learning, (ADPRL)
作者: Dongbin Zhao Jianqiang Yi Derong Liu Key Laboratory of Complex Systems and Intelligence Science Institute of Automation Chinese Academy and Sciences Beijing China Department of Electrical and Computer Engineering University of Illinois Chicago Chicago IL USA
Particle swarm optimization is used for the training of the action network and critic network of the adaptive dynamic programming approach. The typical structures of the adaptive dynamic programming and particle swarm... 详细信息
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Using ADP to Understand and Replicate Brain Intelligence: the Next Level Design
Using ADP to Understand and Replicate Brain Intelligence: th...
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ieee symposium on adaptive dynamic programming and reinforcement learning, (ADPRL)
作者: Paul J. Werbos National Science Foundation Arlington VA USA
Since the 1960's the author proposed that we could understand and replicate the highest level of intelligence seen in the brain, by building ever more capable and general systems for adaptive dynamic programming (... 详细信息
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Toward effective combination of off-line and on-line training in ADP framework
Toward effective combination of off-line and on-line trainin...
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ieee symposium on adaptive dynamic programming and reinforcement learning, (ADPRL)
作者: Danil Prokhorov Toyota Technical Center Ann Arbor MI USA
We are interested in finding the most effective combination between off-line and on-line/real-time training in approximate dynamic programming. We introduce our approach of combining proven off-line methods of trainin... 详细信息
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Convergence of Model-Based Temporal Difference learning for Control
Convergence of Model-Based Temporal Difference Learning for ...
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ieee symposium on adaptive dynamic programming and reinforcement learning, (ADPRL)
作者: Hado van Hasselt Marco A. Wiering Department of Information and Computing Sciences University of Utrecht Utrecht Netherlands
A theoretical analysis of model-based temporal difference learning for control is given, leading to a proof of convergence. This work differs from earlier work on the convergence of temporal difference learning by pro... 详细信息
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