Multi Player Multi Armed Bandit

Multi-player multi-armed bandits (MPMABs) model scenarios where multiple agents simultaneously learn and compete for resources, analogous to content creators vying for audience attention or devices competing for network bandwidth. Research focuses on developing decentralized algorithms that allow agents to maximize their individual or collective rewards despite limited communication and potential resource conflicts (collisions), often employing techniques like Upper Confidence Bound (UCB) and exploring different reward allocation schemes (e.g., averaging, proportional payoff). These models offer valuable insights into competitive learning dynamics in distributed systems and have implications for optimizing resource allocation in various applications, including online content platforms and wireless networks.