Collaborative Multi-Agent Heterogeneous Multi-Armed Bandits
The study of collaborative multi-agent bandits has attracted significant attention recently. In light of this, we initiate the study of a new collaborative setting, consisting of $N$ agents such that each agent is learning one of $M$ stochastic multi-armed bandits to minimize their group cumulative regret. We develop decentralized algorithms which facilitate collaboration between the agents under two scenarios. We characterize the performance of these algorithms by deriving the per agent cumulative regret and group regret upper bounds. We also prove lower bounds for the group regret in this setting, which demonstrates the near-optimal behavior of the proposed algorithms.
Code (0)
등록된 구현이 없습니다.
Tasks
Multi-Armed BanditsSimilar Papers 제목 키워드 기반
Adversarial Attacks on Cooperative Multi-agent Bandits
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 exposed by this collaboration and consider ad…
Multi-Armed BanditsCommunication-Efficient Collaborative Regret Minimization in Multi-Armed Bandits
In this paper, we study the collaborative learning model, which concerns the tradeoff between parallelism and communication overhead in multi-agent multi-armed bandits. For regret minimization in multi-armed bandits, we …
Multi-agent Reinforcement LearningMulti-Armed Banditsreinforcement-learningReinforcement Learning (RL)Distributed Optimization via Kernelized Multi-armed Bandits
Multi-armed bandit algorithms provide solutions for sequential decision-making where learning takes place by interacting with the environment. In this work, we model a distributed optimization problem as a multi-agent ke…
Decision MakingDistributed OptimizationMulti-Armed BanditsSequential Decision MakingLearning with Limited Shared Information in Multi-agent Multi-armed Bandit
Multi-agent multi-armed bandit (MAMAB) is a classic collaborative learning model and has gained much attention in recent years. However, existing studies do not consider the case where an agent may refuse to share all he…
Multi-Agent Bandit Learning through Heterogeneous Action Erasure Channels
Multi-Armed Bandit (MAB) systems are witnessing an upswing in applications within multi-agent distributed environments, leading to the advancement of collaborative MAB algorithms. In such settings, communication between …
Scheduling