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检索条件"任意字段=IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning"
1023 条 记 录,以下是711-720 订阅
adaptive sample collection using active learning for kernel-based approximate policy iteration
Adaptive sample collection using active learning for kernel-...
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ieee symposium on adaptive dynamic programming and reinforcement learning
作者: Liu, Chunming Xu, Xin Haiyun Hu Dai, Bin College of Mechatronics and Automation National University of Defense Technology Changsha 410073 China
Approximate policy iteration (API) has been shown to be a class of reinforcement learning methods with stability and sample efficiency. However, sample collection is still an open problem which is critical to the perf... 详细信息
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Bayesian active learning with basis functions
Bayesian active learning with basis functions
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ieee symposium on adaptive dynamic programming and reinforcement learning
作者: Ryzhov, Ilya O. Powell, Warren B. Operations Research and Financial Engineering Princeton University Princeton NJ 08544 United States
A common technique for dealing with the curse of dimensionality in approximate dynamic programming is to use a parametric value function approximation, where the value of being in a state is assumed to be a linear com... 详细信息
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Feedback controller parameterizations for reinforcement learning
Feedback controller parameterizations for Reinforcement Lear...
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ieee symposium on adaptive dynamic programming and reinforcement learning
作者: Roberts, John W. Manchester, Ian R. Tedrake, Russ CSAIL MIT Cambridge MA 02139 United States
reinforcement learning offers a very general framework for learning controllers, but its effectiveness is closely tied to the controller parameterization used. Especially when learning feedback controllers for weakly ... 详细信息
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Grounding subgoals in information transitions
Grounding subgoals in information transitions
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ieee symposium on adaptive dynamic programming and reinforcement learning
作者: Van Dijk, Sander G. Polani, Daniel Adaptive Systems Research Group University of Hertfordshire Hatfield United Kingdom
In reinforcement learning problems, the construction of subgoals has been identified as an important step to speed up learning and to enable skill transfer. For this purpose, one typically extracts states from various... 详细信息
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Protecting against evaluation overfitting in empirical reinforcement learning
Protecting against evaluation overfitting in empirical reinf...
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ieee symposium on adaptive dynamic programming and reinforcement learning
作者: Whiteson, Shimon Tanner, Brian Taylor, Matthew E. Stone, Peter Informatics Institute University of Amsterdam Netherlands Department of Computing Science University of Alberta Canada Department of Computer Science Lafayette College United States Department of Computer Science University of Texas Austin United States
Empirical evaluations play an important role in machine learning. However, the usefulness of any evaluation depends on the empirical methodology employed. Designing good empirical methodologies is difficult in part be... 详细信息
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Active exploration by searching for experiments that falsify the computed control policy
Active exploration by searching for experiments that falsify...
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ieee symposium on adaptive dynamic programming and reinforcement learning
作者: Fonteneau, Raphael Murphy, Susan A. Wehenkel, Louis Ernst, Damien Department of Electrical Engineering and Computer Science University of Liège Belgium Department of Statistics University of Michigan United States
We propose a strategy for experiment selection - in the context of reinforcement learning - based on the idea that the most interesting experiments to carry out at some stage are those that are the most liable to fals... 详细信息
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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
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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
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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
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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
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