@Workshop{Supelec540,
author = {Matthieu Geist and Olivier Pietquin and Gabriel Fricout},
title = {{Bayesian Reward Filtering}},
year = {2008},
booktitle = {{8th European Workshop on Reinforcement Learning (EWRL 2008)}},
month = {June},
note = {14 pages},
address = {Lille (France)},
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.}
}