Irrevocable Multi-Armed Bandit Policies
  This paper considers the multi-armed bandit problem with multiple simultaneous arm pulls. We develop a new `irrevocable' heuristic for this problem. In particular, we do not allow recourse to arms that were pulled at some point in the past but then discarded. This irrevocable property is highly desirable from a practical perspective. As a consequence of this property, our heuristic entails a minimum amount of `exploration'. At the same time, we find that the price of irrevocability is limited for a broad useful class of bandits we characterize precisely. This class includes one of the most common applications of the bandit model, namely, bandits whose arms are `coins' of unknown biases. Computational experiments with a generative family of large scale problems within this class indicate losses of up to 5 to 10% relative to an upper bound on the performance of an optimal policy with no restrictions on exploration. We also provide a worst-case theoretical analysis that shows that for this class of bandit problems, the price of irrevocability is uniformly bounded: our heuristic earns expected rewards that are always within a factor of 1/8 of an optimal policy with no restrictions on exploration. In addition to being an indicator of robustness across all parameter regimes, this analysis sheds light on the structural properties that afford a low price of irrevocability. 
Vivek Farias and Ritesh Madan