PAC Battling Bandits in the Plackett-Luce Model.

We introduce the probably approximately correct (PAC) emph{Battling-Bandit} problem with the Plackett-Luce (PL) subset choice model--an online learning framework where at each trial the learner chooses a subset of $k$ arms from a fixed set of $n$ arms, and subsequently observes a stochastic feedback indicating preference information of the items in the chosen subset, e.g., the most preferred item or ranking of the top $m$ most preferred items etc. The objective is to identify a near-best item in the underlying PL model with high confidence. This generalizes the well-studied PAC emph{Dueling-Bandit} problem over $n$ arms, which aims to recover the emph{best-arm} from pairwise preference information, and is known to require $O(frac{n}{epsilon^2} ln frac{1}{delta})$ sample complexity citep{Busa_pl,Busa_top}. We study the sample complexity of this problem under various feedback models: (1) Winner of the subset (WI), and (2) Ranking of top-$m$ items (TR) for $2le m le k$. We show, surprisingly, that with winner information (WI) feedback over subsets of size $2 leq k leq n$, the best achievable sample complexity is still $Oleft( frac{n}{epsilon^2} ln frac{1}{delta}right)$, independent of $k$, and the same as that in the Dueling Bandit setting ($k=2$). For the more general top-$m$ ranking (TR) feedback model, we show a significantly smaller lower bound on sample complexity of $Omegabigg( frac{n}{mepsilon^2} ln frac{1}{delta}bigg)$, which suggests a multiplicative reduction by a factor ${m}$ owing to the additional information revealed from preferences among $m$ items instead of just $1$. We also propose two algorithms for the PAC problem with the TR feedback model with optimal (upto logarithmic factors) sample complexity guarantees, establishing the increase in statistical efficiency from exploiting rank-ordered feedback.