This paper studies a cooperative multi-armed bandit problem with $ M $ agents cooperating
together to solve the same instance of a $ K $-armed stochastic bandit problem with the goal …

Motivated by the consideration of fairly sharing the cost of exploration between multiple
groups in learning problems, we develop the Nash bargaining solution in the context of multi …

This paper studies a cooperative multi-agent multi-armed stochastic bandit problem where
agents operate asynchronously–agent pull times and rates are unknown, irregular, and …

This paper studies a cooperative multi-agent bandit scenario in which the rewards observed
by agents are heterogeneous—one agent's meat can be another agent's poison …

Recently, there has been extensive study of cooperative multi-agent multi-armed bandits
where a set of distributed agents cooperatively play the same multi-armed bandit game. The …

Cooperative multi-agent multi-armed bandits (CMA2B) consider the collaborative efforts of
multiple agents in a shared multi-armed bandit game. We study latent vulnerabilities …

Today mobile users learn and share their traffic observations via crowdsourcing platforms
(eg, Google Maps and Waze). Yet such platforms simply cater to selfish users' myopic …

We study the federated pure exploration problem of multi-armed bandits and linear bandits,
where $ M $ agents cooperatively identify the best arm via communicating with the central …

We investigate linear bandits in a network setting in the presence of side-observations
across nodes in order to design recommendation algorithms for users connected via social …

Stochastic multi-agent multi-armed bandits typically assume that the rewards from each arm
follow a fixed distribution, regardless of which agent pulls the arm. However, in many real …