We study a new type of K-armed bandit problem where the expected return of
one arm may depend on the returns of other arms. We present a new algorithm for
this general class of problems and show that under certain circumstances it is
possible to achieve finite expected cumulative regret...
We also give
problem-dependent lower bounds on the cumulative regret showing that at least
in special cases the new algorithm is nearly optimal.