In this paper, we study Federated Bandit, a decentralized Multi-Armed Bandit problem with a
set of N agents, who can only communicate their local data with neighbors described by a …

We consider a budget-constrained bandit problem where each arm pull incurs a random
cost, and yields a random reward in return. The objective is to maximize the total expected …

We consider the task scheduling scenario where the controller activates one from task types
at each time. Each task induces a random completion time, and a reward is obtained only …

The theory of discrete-time online learning has been successfully applied in many problems
that involve sequential decision-making under uncertainty. However, in many applications …

This study considers a novel problem setting, referred to as\textit {bandit task assignment},
that incorporates the processing time of each task in the bandit setting. In this problem …

Solving a Task-and-Motion Planning (TAMP) problem can be represented as a sequential
(meta-) decision process, where early decisions concern the skeleton (sequence of logic …

This paper considers online optimization of a renewal-reward system. A controller performs
a sequence of tasks back-to-back. Each task has a random vector of parameters, called the …

There is a rising interest in industrial online applications where data becomes available
sequentially. Inspired by the recommendation of playlists to users where their preferences …

Online task scheduling serves an integral role for task-intensive applications in cloud
computing and crowdsourcing. Optimal scheduling can enhance system performance …

We study a variant of the contextual bandit problem where an agent can intervene through a
set of stochastic expert policies. Given a fixed context, each expert samples actions from a …