Learning to Control Renewal Processes with Bandit Feedback

We consider a bandit problem with K task types from which the controller activates one task at a time. Each task takes a random and possibly heavy-tailed completion time, and a reward is obtained only after the task is completed. The task types are independent from each other, and have distinct and unknown distributions for completion time and reward. For a given time horizon τ, the goal of the controller is to schedule tasks adaptively so as to maximize the reward collected until τ expires. In addition, we allow the controller to interrupt a task and initiate a new one. In addition to the traditional exploration-exploitation dilemma, this interrupt mechanism introduces a new one: should the controller complete the task and get the reward, or interrupt the task for a possibly shorter and more rewarding alternative? We show that for all heavy-tailed and some light-tailed completion time distributions, this interruption mechanism improves the reward linearly over time. From a learning perspective, the interrupt mechanism necessitates implicitly learning statistics beyond the mean from truncated observations. For this purpose, we propose a robust learning algorithm named UCB-BwI based on the median-of-means estimator for possibly heavy-tailed reward and completion time distributions. We show that, in a K-armed bandit setting with an arbitrary set of L possible interrupt times, UCB-BwI achieves O(Kłog(τ)+KL) regret. We also prove that the regret under any admissible policy is Ømega(Kłog(τ)), which implies that UCB-BwI is order optimal.