Multiplayer Information Asymmetric Bandits in Metric Spaces
This work is incremental, extending prior research on information asymmetry in bandits to multiplayer scenarios without introducing new methods.
The paper tackles the multiplayer information asymmetric bandits problem in metric spaces by applying existing algorithms to handle asymmetry in rewards, actions, or both, achieving regret bounds of the same order across all settings.
In recent years the information asymmetric Lipschitz bandits In this paper we studied the Lipschitz bandit problem applied to the multiplayer information asymmetric problem studied in \cite{chang2022online, chang2023optimal}. More specifically we consider information asymmetry in rewards, actions, or both. We adopt the CAB algorithm given in \cite{kleinberg2004nearly} which uses a fixed discretization to give regret bounds of the same order (in the dimension of the action) space in all 3 problem settings. We also adopt their zooming algorithm \cite{ kleinberg2008multi}which uses an adaptive discretization and apply it to information asymmetry in rewards and information asymmetry in actions.