We address multi-agent best arm identification with privacy guarantees. In this setting,
agents collaborate by communicating to find the optimal arm. To avoid leaking sensitive data …

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 …

In many real-world applications, multiple agents seek to learn how to perform highly related
yet slightly different tasks in an online bandit learning protocol. We formulate this problem as …

Previous work has shown the unreliability of existing algorithms in the batch Reinforcement
Learning setting, and proposed the theoretically-grounded Safe Policy Improvement with …

We investigate the benefits of heterogeneity in multi-agent explore-exploit decision making
where the goal of the agents is to maximize cumulative group reward. To do so we study a …

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 …

This paper studies the multi-armed bandit problem with a requirement of differential privacy
guarantee or global differential privacy guarantee. We first prove that, the lower bound for …

More and more manufacturers, as part of the transition towards Industry 4.0, are using
Internet of Things (IoT) networks for more efficient production. The wide and extensive …

We introduce a framework for decentralized on-line learning for multi-armed bandits (MAB)
with multiple cooperative players, where the reward obtained by the players each round …

The stochastic multi-armed bandit is a classical reinforcement learning model, where a
learning agent sequentially chooses an action (pull a bandit arm) and the environment …