@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.}

}