  The multi-armed restless bandit problem is studied in the case where the
pay-off distributions are stationary $\varphi$-mixing. This version of the
problem provides a more realistic model for most real-world applications, but
cannot be optimally solved in practice, since it is known to be PSPACE-hard.
The objective of this paper is to characterize a sub-class of the problem where
{\em good} approximate solutions can be found using tractable approaches.
Specifically, it is shown that under some conditions on the $\varphi$-mixing
coefficients, a modified version of UCB can prove effective. The main challenge
is that, unlike in the i.i.d. setting, the distributions of the sampled
pay-offs may not have the same characteristics as those of the original bandit
arms. In particular, the $\varphi$-mixing property does not necessarily carry
over. This is overcome by carefully controlling the effect of a sampling policy
on the pay-off distributions. Some of the proof techniques developed in this
paper can be more generally used in the context of online sampling under
dependence. Proposed algorithms are accompanied with corresponding regret
analysis.
