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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721-730 订阅
排序:
Approximate reinforcement learning: An overview
Approximate reinforcement learning: An overview
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ieee symposium on adaptive dynamic programming and reinforcement learning
作者: Buşoniu, Lucian Ernst, Damien De Schutter, Bart Babuška, Robert Delft Center for Systems and Control Delft Univ. of Technology Netherlands Research Associate of the FRS-FNRS Systems and Modeling Unit University of Liège Liège Belgium
reinforcement learning (RL) allows agents to learn how to optimally interact with complex environments. Fueled by recent advances in approximation-based algorithms, RL has obtained impressive successes in robotics, ar... 详细信息
来源: 评论
dynamic lead time promising
Dynamic lead time promising
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ieee symposium on adaptive dynamic programming and reinforcement learning
作者: Reindorp, Matthew J. Fu, Michael C. Department of Industrial Engineering and Innovation Sciences Eindhoven University of Technology Netherlands Robert H. Smith School of Business Institute for Systems Research University of Maryland United States
We consider a make-to-order business that serves customers in multiple priority classes. Orders from customers in higher classes bring greater revenue, but they expect shorter lead times than customers in lower classe... 详细信息
来源: 评论
On learning with imperfect representations
On learning with imperfect representations
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ieee symposium on adaptive dynamic programming and reinforcement learning
作者: Kalyanakrishnan, Shivaram Stone, Peter Department of Computer Science University of Texas at Austin 1616 Guadalupe St Austin TX 78701 United States
In this paper we present a perspective on the relationship between learning and representation in sequential decision making tasks. We undertake a brief survey of existing real-world applications, which demonstrates t... 详细信息
来源: 评论
Application of reinforcement learning-based algorithms in CO2 allowance and electricity markets
Application of reinforcement learning-based algorithms in CO...
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ieee symposium on adaptive dynamic programming and reinforcement learning
作者: Nanduri, Vishnuteja Department of Industrial and Manufacturing Engineering University of Wisconsin-Milwaukee Milwaukee WI 53211 United States
Climate change is one of the most important challenges faced by the world this century. In the U.S., the electric power industry is the largest emitter of CO2, contributing to the climate crisis. Federal emissions con... 详细信息
来源: 评论
Higher order Q-learning
Higher order Q-Learning
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ieee symposium on adaptive dynamic programming and reinforcement learning
作者: Edwards, Ashley Pottenger, William M. Department of Computer Science University of Georgia Athens GA 30606 United States Department of Computer Science and DIMACS Rutgers University Piscataway NJ 08854 United States
Higher order learning is a statistical relational learning framework in which relationships between different instances of the same class are leveraged (Ganiz, Lytkin and Pottenger, 2009). learning can be supervised o... 详细信息
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Improved neural fitted Q iteration applied to a novel computer gaming and learning benchmark
Improved neural fitted Q iteration applied to a novel comput...
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ieee symposium on adaptive dynamic programming and reinforcement learning
作者: Gabel, Thomas Lutz, Christian Riedmiller, Martin Machine Learning Lab Department of Computer Science University of Freiburg 79110 Freiburg Germany
Neural batch reinforcement learning (RL) algorithms have recently shown to be a powerful tool for model-free reinforcement learning problems. In this paper, we present a novel learning benchmark from the realm of comp... 详细信息
来源: 评论
reinforcement learning algorithms for solving classification problems
Reinforcement learning algorithms for solving classification...
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ieee symposium on adaptive dynamic programming and reinforcement learning
作者: Wiering, Marco A. Van Hasselt, Hado Pietersma, Auke-Dirk Schomaker, Lambert Dept. of Artificial Intelligence University of Groningen Netherlands Multi-agent and Adaptive Computation Centrum Wiskunde en Informatica Netherlands
We describe a new framework for applying reinforcement learning (RL) algorithms to solve classification tasks by letting an agent act on the inputs and learn value functions. This paper describes how classification pr... 详细信息
来源: 评论
Transformation Invariant On-Line Target Recognition
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ieee TRANSACTIONS ON NEURAL NETWORKS 2011年 第6期22卷 906-918页
作者: Iftekharuddin, Khan M. Univ Memphis Dept Elect & Comp Engn Intelligent Syst & Image Proc Lab Memphis TN 38152 USA
Transformation invariant automatic target recognition (ATR) has been an active research area due to its widespread applications in defense, robotics, medical imaging and geographic scene analysis. The primary goal for... 详细信息
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Safe reinforcement learning in high-risk tasks through policy improvement
Safe reinforcement learning in high-risk tasks through polic...
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ieee symposium on adaptive dynamic programming and reinforcement learning
作者: Garcia Polo, Francisco Javier Fernandez Rebollo, Fernando Computer Science Department Universidad Carlos III de Madrid Avenida de la Universidad 30 28911 Leganés Madrid Spain
reinforcement learning (RL) methods are widely used for dynamic control tasks. In many cases, these are high risk tasks where the trial and error process may select actions which execution from unsafe states can be ca... 详细信息
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An adaptive-learning framework for semi-cooperative multi-agent coordination
An adaptive-learning framework for semi-cooperative multi-ag...
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ieee symposium on adaptive dynamic programming and reinforcement learning
作者: Boukhtouta, Abdeslem Berger, Jean Powell, Warren B. George, Abraham Defence Research and Development Canada-Valcartier Quebec QC G3J 1X5 Canada Department of Operations Research and Financial Engineering Princeton University Princeton NJ 08544 United States
Complex problems involving multiple agents exhibit varying degrees of cooperation. The levels of cooperation might reflect both differences in information as well as differences in goals. In this research, we develop ... 详细信息
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