Heterogeneous Multi-agent Multi-armed Bandit on Stochastic Block Models

View Full Paper

Ask AI

Bookmark

Minimizing system-wide regret enhances performance across heterogeneous agents and varying network structures.
The framework integrates stochastic block models to account for heterogeneous rewards dependent on clusters.
The proposed algorithm leverages an averaging-based consensus approach, ensuring optimal performance across clusters with varying degrees of information.
Findings indicate that optimal regret bounds can be achieved even with sub-Gaussian rewards, supporting scalability and adaptability.

Discussion

No takes yet. Share an insight, caveat, or question.

Novel algorithm minimizes regret in heterogeneous multi-agent systems with stochastic block models, suggesting new approaches for cluster dynamics.

Xu et al. (2025) studied this question.

View Full Paper

Ask AI

Bookmark

Novel algorithm minimizes regret in heterogeneous multi-agent systems with stochastic block models, suggesting new approaches for cluster dynamics.