@InCollection{Supelec423,
author = {Matthieu Geist and Olivier Pietquin and Gabriel Fricout},
title = {{Bayesian Reward Filtering}},
year = {2008},
booktitle = {{Recent Advances in Reinforcement Learning}},
publisher = {Springer Verlag},
volume = {5323},
pages = {96-109},
month = {June},
note = {Revised and selected papers of EWRL 2008},
editor = {S. Girgin et al.},
series = {Lecture Notes in Computer Science (LNCS)},
url = {http://www.metz.supelec.fr/metz/recherche/publis_pdf/Supelec423.pdf},
doi = {10.1007/978-3-540-89722-4_8},
abstract = {A wide variety of function approximation schemes have been
applied to reinforcement learning. However, Bayesian filtering
approaches,which have been shown efficient in other fields such
as neural network training, have been little studied.We propose
a general Bayesian filtering framework for reinforcement
learning, as well as a specific implementation based on sigma
point Kalman
filtering and kernel machines. This allows us to derive an
efficient off-policy model-free approximate temporal differences
algorithm which will be demonstrated on two simple benchmarks.}
}