Tight Policy Regret Bounds for Improving and Decaying Bandits.

We consider a variant of the well-studied multiarmed bandit problem in which the reward from each action evolves monotonically in the number of times the decision maker chooses to take that action. We are motivated by settings in which we must give a series of homogeneous tasks to a finite set of arms (workers) whose performance may improve (due to learning) or decay (due to loss of interest) with repeated trials. We assume that the arm-dependent rates at which the rewards change are unknown to the decision maker, and propose algorithms with provably optimal policy regret bounds, a much stronger notion than the often-studied external regret. For the case where the rewards are increasing and concave, we give an algorithm whose policy regret is sublinear and has a (provably necessary) dependence on the time required to distinguish the optimal arm from the rest. We illustrate the behavior and performance of this algorithm via simulations. For the decreasing case, we present a simple greedy approach and show that the policy regret of this algorithm is constant and upper bounded by the number of arms.