Today I read a paper titled “Kalman filter control in the reinforcement learning framework”
The abstract is:
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.