@Article{Supelec523,
author = {Matthieu Geist and Olivier Pietquin and Gabriel Fricout},
title = {{From Supervised to Reinforcement Learning: a Kernel-based Bayesian Filtering Framework}},
journal = {International Journal On Advances in Software},
year = {2009},
volume = {2},
number = {1},
pages = {101-116},
url = {http://hal-supelec.archives-ouvertes.fr/hal-00429891/en/},
isbn = {1942-2628},
abstract = {In a large number of applications, engineers have to estimate a
function linked to the state of a dynamic system. To do so, a
sequence of samples drawn from this unknown function is observed
while the system is transiting from state to state and the
problem is to generalize these observations to unvisited states.
Several solutions can be envisioned among which regressing a
family of parameterized functions so as to make it fit at best
to
the observed samples. This is the first problem addressed with
the proposed kernel-based Bayesian filtering approach, which
also
allows quantifying uncertainty reduction occurring when
acquiring
more samples. Classical methods cannot handle the case where
actual samples are not directly observable but only a non linear
mapping of them is available, which happens when a special
sensor
has to be used or when solving the Bellman equation in order to
control the system. However the approach proposed in this paper
can be extended to this tricky case. Moreover, an application of
this indirect function approximation scheme to reinforcement
learning is presented. A set of experiments is also proposed in
order to demonstrate the efficiency of this kernel-based
Bayesian
approach.}
}