Collaborative Bandit

Collaborative bandit algorithms address the challenge of multiple agents learning optimal actions from shared, potentially heterogeneous, data. Current research focuses on developing algorithms that efficiently balance individual agent learning with collaborative information sharing, often employing techniques like clustering and phased elimination to manage communication costs and handle adversarial agents or budget constraints. This field is significant because it enables efficient and privacy-preserving learning in distributed systems, with applications ranging from personalized recommendations to resource allocation in networked environments. Recent work emphasizes achieving optimal regret bounds and addressing the complexities introduced by heterogeneous data and adversarial behavior.