Multi-armed bandit problems with heavy-tailed reward distributions

In the Multi-Armed Bandit (MAB) problem, a player selects one out of a set of arms to play at each time without knowing the arm reward statistics. The essence of the problem is the tradeoff between exploration and exploitation: playing a less explored arm to learn its reward statistics or playing the arm appearing to be the best. An approach based on a Deterministic Sequencing of Exploration and Exploitation (DSEE) is developed for constructing sequential arm selection policies. It is shown that when the moment-generating functions of the arm reward distributions are properly bounded, the optimal logarithmic order of the regret can be achieved by DSEE. The condition on the reward distributions can be gradually relaxed at a cost of a higher (nevertheless, sublinear) regret order: for any positive integer p, O(T ^1/p) regret can be achieved by DSEE when the moments of the reward distributions exist (only) up to the pth order. The proposed DSEE approach complements existing work on MAB by providing corresponding results under a set of relaxed conditions on the reward distributions. Furthermore, with a clearly defined tunable parameter - the cardinality of the exploration sequence, the DSEE approach is easily extendable to variations of MAB, including decentralized MAB with partial reward observations and restless MAB with unknown Markov dynamics. Potential applications include dynamic spectrum access, multi-agent systems, Internet advertising and Web search.