NDLI: Model-Free Dual Heuristic Dynamic Programming

Content Provider	IEEE Xplore Digital Library
Author	Zhen Ni Haibo He Xiangnan Zhong Prokhorov, D.V.
Copyright Year	2012
Abstract	Model-based dual heuristic dynamic programming (MB-DHP) is a popular approach in approximating optimal solutions in control problems. Yet, it usually requires offline training for the model network, and thus resulting in extra computational cost. In this brief, we propose a model-free DHP (MF-DHP) design based on finite-difference technique. In particular, we adopt multilayer perceptron with one hidden layer for both the action and the critic networks design, and use delayed objective functions to train both the action and the critic networks online over time. We test both the MF-DHP and MB-DHP approaches with a discrete time example and a continuous time example under the same parameter settings. Our simulation results demonstrate that the MF-DHP approach can obtain a control performance competitive with that of the traditional MB-DHP approach while requiring less computational resources.
Starting Page	1834
Ending Page	1839
Page Count	6
File Size	986800
File Format	PDF
ISSN	2162237X
Volume Number	26
Issue Number	8
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computational modeling Dynamic programming Convergence Approximation methods Mathematical model Linear programming Learning systems reinforcement learning. Action-dependent dual heuristic dynamic programming (DHP) adaptive critic designs (ACDs) adaptive dynamic programming (ADP) online learning reinforcement learning
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Networks and Communications Computer Science Applications Software

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