Neuro-dynamic programming: an overview

We discuss a relatively new class of dynamic programming methods for control and sequential decision making under uncertainty. These methods have the potential of dealing with problems that for a long time were thought to be intractable due to either a large state space or the lack of an accurate mo...

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Bibliographic Details
Published inProceedings of 1995 34th IEEE Conference on Decision and Control Vol. 1; pp. 560 - 564 vol.1
Main Authors Bertsekas, D.P., Tsitsiklis, J.N.
Format Conference Proceeding
LanguageEnglish
Published IEEE Control Systems Society 1995
Subjects
Artificial intelligence
Artificial neural networks
Control systems
Cost function
Dynamic programming
Equations
Laboratories
Optimal control
State-space methods
Uncertainty
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Summary:We discuss a relatively new class of dynamic programming methods for control and sequential decision making under uncertainty. These methods have the potential of dealing with problems that for a long time were thought to be intractable due to either a large state space or the lack of an accurate model. The methods discussed combine ideas from the fields of neural networks, artificial intelligence, cognitive science, simulation, and approximation theory. We delineate the major conceptual issues, survey a number of recent developments, describe some computational experience, and address a number of open questions.
ISBN:0780326857
9780780326859
ISSN:0191-2216
DOI:10.1109/CDC.1995.478953