We investigate robust data aggregation in a multiagent online learning setting. In reality, multiple online learning agents are often deployed to perform similar tasks and receive similar feedback. We study how agents can improve their collective performance by sharing information among each other. In this paper, we formulate the εmultiplayer multiarmed bandit problem, in which a set of M players that have similar reward distributions for each arm play concurrently. We develop an upper confidence boundbased algorithm that adaptively aggregates rewards collected by different players. To our best knowledge, we are the first to develop such a scheme in a multiplayer bandit learning setting. We show that under the assumption that pairwise distances between the means of the playerdependent distributions for each arm are small, we improve the (collective) regret bound by nearly a factor of M , in comparison with a baseline algorithm in which the players learn individually using the UCB1 algorithm (Auer et al., 2002). Our algorithm also exhibits a fallback guarantee, namely, if our task similarity assumption fails to hold, our algorithm still has a performance guarantee that cannot be worse than the baseline by a constant factor. Empirically, we validate our algorithm on synthetic data.
Multitask Bandit Learning Through Heterogeneous Feedback Aggregation
In many realworld 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 the ϵmultiplayer multiarmed bandit problem, in which a set of players concurrently interact with a set of arms, and for each arm, the reward distributions for all players are similar but not necessarily identical. We develop an upper confidence boundbased algorithm, RobustAgg(ϵ), that adaptively aggregates rewards collected by different players. In the setting where an upper bound on the pairwise dissimilarities of reward distributions between players is known, we achieve instancedependent regret guarantees that depend on the amenability of information sharing across players. We complement these upper bounds with nearly matching lower bounds. In the setting where pairwise dissimilarities are unknown, we provide a lower bound, as well as an algorithm that trades off minimax regret guarantees for adaptivity to unknown similarity structure.
 Publication Date:
 NSFPAR ID:
 10285171
 Journal Name:
 Proceedings of the 24th International Conference on Artificial Intelligence and Statistics (AISTATS) 2021
 Sponsoring Org:
 National Science Foundation
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