@InProceedings{Supelec913,
author = {Matthieu Geist},
title = {{A multiplicative UCB strategy for Gamma rewards}},
year = {2015},
booktitle = {{European Workshop on Reinforcement Learning (EWRL)}},
url = {http://www.metz.supelec.fr//metz/personnel/geist_mat/pdfs/gamma_ucb.pdf},
abstract = {We consider the stochastic multi-armed bandit problem where
rewards are distributed according to Gamma probability measures
(unknown up to a lower bound on the form factor). To handle this
problem, we propose an UCB-like strategy where indexes are
multiplicative (sampled mean times a scaling factor). An
upper-bound for the associated regret is provided and the
proposed strategy is illustrated on some simple experiments.}
}