Multi Agent Multi

Multi-agent multi-armed bandits (MAB) research focuses on designing algorithms for multiple agents collaboratively or competitively learning optimal strategies in uncertain environments, often aiming to optimize overall system performance or individual agent fairness. Current research emphasizes developing efficient algorithms, such as those based on distributed auctions or follow-the-regularized-leader approaches, to minimize regret (the difference between optimal and achieved performance) while addressing challenges like asynchronous agent actions, communication delays, and fairness constraints. These advancements have significant implications for resource allocation in areas like wireless networks (e.g., O-RAN optimization) and offer improved theoretical understanding of collaborative learning in decentralized systems.