  Age-of-Information (AoI) is a performance metric for scheduling systems that
measures the freshness of the data available at the intended destination. AoI
is formally defined as the time elapsed since the destination received the
recent most update from the source. We consider the problem of scheduling to
minimize the cumulative AoI in a multi-source multi-channel setting. Our focus
is on the setting where channel statistics are unknown and we model the problem
as a distributed multi-armed bandit problem. For an appropriately defined AoI
regret metric, we provide analytical performance guarantees of an existing
UCB-based policy for the distributed multi-armed bandit problem. In addition,
we propose a novel policy based on Thomson Sampling and a hybrid policy that
tries to balance the trade-off between the aforementioned policies. Further, we
develop AoI-aware variants of these policies in which each source takes its
current AoI into account while making decisions. We compare the performance of
various policies via simulations.
