title: Kalman filter control embedded into the reinforcement learning framework.
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.
authors.affiliation: Department of Information Systems, Eötvos Löránd University, Pázmány Péter sétány 1/C, H-1117 Budapest, Hungary. szityu@eotvoscollegium.hu
authors.forename: István
authors.initials: I
authors.lastname: Szita
authors.forename: András
authors.initials: A
authors.lastname: Lorincz
mesh_terms: D000465:Algorithms; D006801:Humans; D007858:Learning / Q000502:physiology*; D008959:Models, Neurological; D016571:Neural Networks, Computer; D012054:Reinforcement, Psychology