  Motivated by cognitive radio networks, we consider the stochastic multiplayer
multi-armed bandit problem, where several players pull arms simultaneously and
collisions occur if one of them is pulled by several players at the same stage.
We present a decentralized algorithm that achieves the same performance as a
centralized one, contradicting the existing lower bounds for that problem. This
is possible by "hacking" the standard model by constructing a communication
protocol between players that deliberately enforces collisions, allowing them
to share their information at a negligible cost. This motivates the
introduction of a more appropriate dynamic setting without sensing, where
similar communication protocols are no longer possible. However, we show that
the logarithmic growth of the regret is still achievable for this model with a
new algorithm.
