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Multiplayer Information Asymmetric Bandits in Metric Spaces
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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.
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Cited by 2 Pith papers
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Coordinating the Unknown Lipschitz Constant in Multiplayer Bandits
Cooperative multiplayer Lipschitz bandits with an unknown Lipschitz constant achieve T^(Md+1)/(Md+2)-scale regret in three information structures, with dithering synchronizing the players' discretizations.
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Decentralized Multi-Player Q-Learning in Episodic Markov Decision Processes with Information Asymmetry
Decentralized players using pre-agreed deterministic tie-breaking can match centralized Q-learning regret when either actions or rewards are shared, but the fully asymmetric setting rests on an exploration argument th...
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