The knowledge gradient algorithm for online subset selection

We derive a one-period look-ahead policy for online subset selection problems, where learning about one subset also gives us information about other subsets. The subset selection problem is treated as a multi-armed bandit problem with correlated prior beliefs. We show that our decision rule is easily computable, and present experimental evidence that the policy is competitive against other online learning policies.