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.
Cite
Text
Szita and Lörincz. "Kalman Filter Control Embedded into the Reinforcement Learning Framework." Neural Computation, 2004. doi:10.1162/089976604772744884Markdown
[Szita and Lörincz. "Kalman Filter Control Embedded into the Reinforcement Learning Framework." Neural Computation, 2004.](https://mlanthology.org/neco/2004/szita2004neco-kalman/) doi:10.1162/089976604772744884BibTeX
@article{szita2004neco-kalman,
title = {{Kalman Filter Control Embedded into the Reinforcement Learning Framework}},
author = {Szita, Istvan and Lörincz, András},
journal = {Neural Computation},
year = {2004},
pages = {491-499},
doi = {10.1162/089976604772744884},
volume = {16},
url = {https://mlanthology.org/neco/2004/szita2004neco-kalman/}
}