Bayesian Anytime mtop Exploration We introduce Boundary Focused Thompson sampling (BFTS), a new Bayesian algorithm to solve the anytime mtop exploration problem, where the objective is to identify the m best arms in a multiarmed bandit. First, we consider a set of existing benchmark problems that consider subGaussian reward distributions (i.e., Gaussian with fixed variance and categorical reward). Next, we introduce a new environment inspired by a real world decision problem concerning insect control for organic agriculture. This new environment encodes a Poisson rewards distribution. For all these benchmarks, we experimentally show that BFTS consistently outperforms ATLUCB, the current state of the art algorithm.

An ArmWise Randomization Approach to Combinatorial Linear SemiBandits Combinatorial linear semibandits (CLS) are widely applicable frameworks of sequential decisionmaking, in which a learner chooses a subset of arms from a given set of arms associated with feature vectors. Existing algorithms work poorly for the clustered case, in which the feature vectors form several large clusters. This shortcoming is critical in practice because it can be found in many applications, including recommender systems. In this paper, we clarify why such a shortcoming occurs, and we introduce a key technique of armwise randomization to overcome it. We propose two algorithms with this technique: the perturbed C2 UCB (PC2 UCB) and the Thompson sampling (TS). Our empirical evaluation with artificial and realworld datasets demonstrates that the proposed algorithms with the armwise randomization technique outperform the existing algorithms without this technique, especially for the clustered case. Our contributions also include theoretical analyses that provide high probability asymptotic regret bounds for our algorithms.