Kalman Filter Control Embedded into the Reinforcement Learning Framework

István Szita, András Lorincz

Research output: Contribution to journalArticle

9 Citations (Scopus)

Abstract

There is a growing interest in using Kalman filter models in brain modeling. The question arises whether Kalman filter models can be used on-line not only for estimation but for control. The usual method of optimal control of Kalman filter makes use of off-line backward recursion, which is not satisfactory for this purpose. Here, it is shown that a slight modification of the linear-quadratic-gaussian Kalman filter model allows the on-line estimation of optimal control by using reinforcement learning and overcomes this difficulty. Moreover, the emerging learning rule for value estimation exhibits a Hebbian form, which is weighted by the error of the value estimation.

Original languageEnglish
Pages (from-to)491-499
Number of pages9
JournalNeural Computation
Volume16
Issue number3
DOIs
Publication statusPublished - Mar 1 2004

ASJC Scopus subject areas

  • Arts and Humanities (miscellaneous)
  • Cognitive Neuroscience

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