Kalman filter control embedded into the reinforcement learning framework.

István Szita; András Lorincz

doi:10.1162/089976604772744884

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Kalman filter control embedded into the reinforcement learning framework.

István Szita·András Lorincz

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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.

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Algorithms Humans Learning Models, Neurological Neural Networks, Computer Reinforcement, Psychology Journal Article Research Support, Non-U.S. Gov't

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István Szita, András Lorincz. 2004. Kalman filter control embedded into the reinforcement learning framework.. https://doi.org/10.1162/089976604772744884

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