  We propose a novel algorithm for multi-player multi-armed bandits without
collision sensing information. Our algorithm circumvents two problems shared by
all state-of-the-art algorithms: it does not need as an input a lower bound on
the minimal expected reward of an arm, and its performance does not scale
inversely proportionally to the minimal expected reward. We prove a theoretical
regret upper bound to justify these claims. We complement our theoretical
results with numerical experiments, showing that the proposed algorithm
outperforms state-of-the-art in practice as well.
