Kalman filter control in the reinforcement learning framework

Istvan Szita; Andras Lorincz

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Kalman filter control in the reinforcement learning framework

Istvan Szita·Andras Lorincz

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Abstract

There is a growing interest in using Kalman-filter models in brain modelling. In turn, it is of considerable importance to make Kalman-filters amenable for reinforcement learning. In the usual formulation of optimal control it is computed off-line by solving a backward recursion. In this technical note we show that slight modification of the linear-quadratic-Gaussian Kalman-filter model allows the on-line estimation of optimal control and makes the bridge to reinforcement learning. Moreover, the learning rule for value estimation assumes a Hebbian form weighted by the error of the value estimation.

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BibTeX RIS

Istvan Szita, Andras Lorincz. 2003-01-09. Kalman filter control in the reinforcement learning framework. https://arxiv.org/abs/cs/0301007

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