Kalman filter control in the reinforcement learning framework
<|reference_start|>Kalman filter control in the reinforcement learning framework: 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.<|reference_end|>
@article{szita2003kalman,
    title={Kalman filter control in the reinforcement learning framework},
    author={Istvan Szita, Andras Lorincz},
    journal={arXiv preprint arXiv:cs/0301007},
    year={2003},
    archivePrefix={arXiv},
    eprint={cs/0301007},
    primaryClass={cs.LG cs.AI}
}