  Restless multi-armed bandits (RMABs) generalize the multi-armed bandits where
each arm exhibits Markovian behavior and transitions according to their
transition dynamics. Solutions to RMAB exist for both offline and online cases.
However, they do not consider the distribution of pulls among the arms. Studies
have shown that optimal policies lead to unfairness, where some arms are not
exposed enough. Existing works in fairness in RMABs focus heavily on the
offline case, which diminishes their application in real-world scenarios where
the environment is largely unknown. In the online scenario, we propose the
first fair RMAB framework, where each arm receives pulls in proportion to its
merit. We define the merit of an arm as a function of its stationary reward
distribution. We prove that our algorithm achieves sublinear fairness regret in
the single pull case $O(\sqrt{T\ln T})$, with $T$ being the total number of
episodes. Empirically, we show that our algorithm performs well in the
multi-pull scenario as well.
