This paper introduces a general multi-agent bandit model in which each agent is facing a
finite set of arms and may communicate with other agents through a central controller in …

Collecting and leveraging data with good coverage properties plays a crucial role in different
aspects of reinforcement learning (RL), including reward-free exploration and offline …

We consider the best-k-arm identification problem for multi-armed bandits, where the
objective is to select the exact set of k arms with the highest mean rewards by sequentially …

Elimination algorithms for bandit identification, which prune the plausible correct answers
sequentially until only one remains, are computationally convenient since they reduce the …

This paper considers the problem of online clustering with bandit feedback. A set of arms (or
items) can be partitioned into various groups that are unknown. Within each group, the …

Several recent works have proposed instance-dependent upper bounds on the number of
episodes needed to identify, with probability $1-\delta $, an $\varepsilon $-optimal policy in …

We consider regret minimization in a general collaborative multi-agent multi-armed bandit
model, in which each agent faces a finite set of arms and may communicate with other …

In contextual linear bandits, the reward function is assumed to be a linear combination of an
unknown reward vector and a given embedding of context-arm pairs. In practice, the …

In pure-exploration problems, information is gathered sequentially to answer a question on
the stochastic environment. While best-arm identification for linear bandits has been …

We consider pure-exploration problems in the context of stochastic sequential adaptive
experiments with a finite set of alternative options. The goal of the decision-maker is to …