  Various approaches have emerged for multi-armed bandits in distributed
systems. The multiplayer dueling bandit problem, common in scenarios with only
preference-based information like human feedback, introduces challenges related
to controlling collaborative exploration of non-informative arm pairs, but has
received little attention. To fill this gap, we demonstrate that the direct use
of a Follow Your Leader black-box approach matches the lower bound for this
setting when utilizing known dueling bandit algorithms as a foundation.
Additionally, we analyze a message-passing fully distributed approach with a
novel Condorcet-winner recommendation protocol, resulting in expedited
exploration in many cases. Our experimental comparisons reveal that our
multiplayer algorithms surpass single-player benchmark algorithms, underscoring
their efficacy in addressing the nuanced challenges of the multiplayer dueling
bandit setting.
