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Kalman Filter Control Embedded into the Reinforcement Learning Framework

szityu{at}eotvoscollegium.hu, Department of Information Systems, Eötvös Loránd University, Pázmány Péter sétány 1/C, H-1117 Budapest, Hungary

András Lrincz

alorincz{at}axelero.hu, Department of Information Systems, Eötvös Loránd University, Pázmány Péter sétány 1/C, H-1117 Budapest, Hungary

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.

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