  The target of $\mathcal{X}$-armed bandit problem is to find the global
maximum of an unknown stochastic function $f$, given a finite budget of $n$
evaluations. Recently, $\mathcal{X}$-armed bandits have been widely used in
many situations. Many of these applications need to deal with large-scale data
sets. To deal with these large-scale data sets, we study a distributed setting
of $\mathcal{X}$-armed bandits, where $m$ players collaborate to find the
maximum of the unknown function. We develop a novel anytime distributed
$\mathcal{X}$-armed bandit algorithm. Compared with prior work on
$\mathcal{X}$-armed bandits, our algorithm uses a quite different searching
strategy so as to fit distributed learning scenarios. Our theoretical analysis
shows that our distributed algorithm is $m$ times faster than the classical
single-player algorithm. Moreover, the number of communication rounds of our
algorithm is only logarithmic in $mn$. The numerical results show that our
method can make effective use of every players to minimize the loss. Thus, our
distributed approach is attractive and useful.
