  We consider the problem of learning in single-player and multiplayer
multiarmed bandit models. Bandit problems are classes of online learning
problems that capture exploration versus exploitation tradeoffs. In a
multiarmed bandit model, players can pick among many arms, and each play of an
arm generates an i.i.d. reward from an unknown distribution. The objective is
to design a policy that maximizes the expected reward over a time horizon for a
single player setting and the sum of expected rewards for the multiplayer
setting. In the multiplayer setting, arms may give different rewards to
different players. There is no separate channel for coordination among the
players. Any attempt at communication is costly and adds to regret. We propose
two decentralizable policies, $\tt E^3$ ($\tt E$-$\tt cubed$) and $\tt
E^3$-$\tt TS$, that can be used in both single player and multiplayer settings.
These policies are shown to yield expected regret that grows at most as
O($\log^{1+\epsilon} T$). It is well known that $\log T$ is the lower bound on
the rate of growth of regret even in a centralized case. The proposed
algorithms improve on prior work where regret grew at O($\log^2 T$). More
fundamentally, these policies address the question of additional cost incurred
in decentralized online learning, suggesting that there is at most an
$\epsilon$-factor cost in terms of order of regret. This solves a problem of
relevance in many domains and had been open for a while.
