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The Adaptive Dynamic Programming Theorem
2003The centerpiece of the theory of dynamic programming is the HamiltonJacobi-Bellman (HJB) equation, which can be used to solve for the optimal cost functional Vo for a nonlinear optimal control problem, while one can solve a second partial differential equation for the corresponding optimal control law ko.Although the direct solution of the HJB equation
John J. Murray +2 more
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Adaptive dynamic programming as a theory of sensorimotor control
Biological Cybernetics, 2012Many characteristics of sensorimotor control can be explained by models based on optimization and optimal control theories. However, most of the previous models assume that the central nervous system has access to the precise knowledge of the sensorimotor system and its interacting environment.
Yu Jiang 0003, Zhong-Ping Jiang
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Overview of Adaptive Dynamic Programming
2017This chapter reviews the development of adaptive dynamic programming (ADP). It starts with a background overview of reinforcement learning and dynamic programming. It then moves on to the basic forms of ADP and then to the iterative forms. ADP is an emerging advanced control technology developed for nonlinear dynamical systems.
Derong Liu +4 more
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Self-teaching adaptive dynamic programming for Gomoku
Neurocomputing, 2012In this paper adaptive dynamic programming (ADP) is applied to learn to play Gomoku. The critic network is used to evaluate board situations. The basic idea is to penalize the last move taken by the loser and reward the last move selected by the winner at the end of a game.
Dongbin Zhao, Zhen Zhang 0009, Yujie Dai
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Adaptive dynamic programming for linear impulse systems
Journal of Zhejiang University SCIENCE C, 2014We investigate the optimization of linear impulse systems with the reinforcement learning based adaptive dynamic programming (ADP) method. For linear impulse systems, the optimal objective function is shown to be a quadric form of the pre-impulse states. The ADP method provides solutions that iteratively converge to the optimal objective function.
Xiaohua Wang 0003 +4 more
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Supervised adaptive dynamic programming based adaptive cruise control
2011 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL), 2011This paper proposes a supervised adaptive dynamic programming (SADP) algorithm for the full range Adaptive cruise control (ACC) system. The full range ACC system considers both the ACC situation in highway system and the stop and go (SG) situation in urban street way system.
Dongbin Zhao, Zhaohui Hu
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Adaptive Learning and Control for MIMO System Based on Adaptive Dynamic Programming
IEEE Transactions on Neural Networks, 2011Adaptive dynamic programming (ADP) is a promising research field for design of intelligent controllers, which can both learn on-the-fly and exhibit optimal behavior. Over the past decades, several generations of ADP design have been proposed in the literature, which have demonstrated many successful applications in various benchmarks and industrial ...
Fu, Jian, He, Haibo, Zhou, Xinmin
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Adaptive Data Refinement for Parallel Dynamic Programming Applications
2012 IEEE 26th International Parallel and Distributed Processing Symposium Workshops & PhD Forum, 2012Load balancing is a challenging work for parallel dynamic programming due to its intrinsically strong data dependency. Two issues are mainly involved and equally important, namely, the partitioning method as well as scheduling and distribution policy of subtasks.
Shanjiang Tang +4 more
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Providing Dynamic Instructional Adaptation in Programming Learning
2008This paper describes an approach to create an Intelligent Tutoring System that provides dynamic personalization and learning activities sequencing adaptation by combining eLearning standards and Artificial Intelligent techniques. The work takes advantage of the functionalities provided by an open source Learning Management System, dotLRN, which ...
Francisco Jurado 0001 +4 more
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Dynamic Programming Training Period for an MSE Adaptive Equalizer
IEEE Transactions on Communications, 1971This paper concerns itself with the design of an algorithm that will shorten the training period and adaptation time of an adaptive equalizer. Time-invariant or slowly varying channels with white additive Gaussian noise are considered. An adaptive equalizer in the form of a nonrecursive transversal filter reduces the intersymbol interference.
Steven H. Richman, Mischa Schwartz
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