Multiplayer Information Asymmetric Bandits in Metric Spaces

TMLR Paper5329 Authors

Abstract: 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.

Submission Length: Regular submission (no more than 12 pages of main content)

Assigned Action Editor: ~Stefan_Magureanu1

Submission Number: 5329