6 papers · 1 filter
The Price of Decentralization in Top- Arm Identification
Larissa Xu, Jasmine Nguyen, William Chang
Cooperative teams often need to agree on the best few options rather than simply accumulate reward, and they must do so while each member sees only a fragment of the team's collect…
Decentralized Multi-Player Q-Learning in Episodic Markov Decision Processes with Information Asymmetry
Larissa Xu, King Bi, William Chang
We study decentralized multi-player reinforcement learning in episodic tabular Markov decision processes (MDPs) under three forms of information asymmetry: (A) unobserved actions w…
DCM Bandits: Multiplayer Information Asymmetric Cascading Bandits for Multiple Clicks
Andy Wang, Charlton Shih, William Chang
In this work, we extend the Dependent Click Model (DCM) Bandits to a multiplayer information-asymmetric setting, where multiple agents interact with a shared ranked list and may ob…
Coordinating the Unknown Lipschitz Constant in Multiplayer Bandits
Ricardo Parada, Chenzhang Zhao, William Chang
Motivated by decentralized applications, we study cooperative multi-agent bandits in continuous (Lipschitz) action spaces when the Lipschitz constant is unknown. We consider three…
Multiplayer Information Asymmetric Contextual Bandits
William Chang, Yuanhao Lu
Single-player contextual bandits are a well-studied problem in reinforcement learning that has seen applications in various fields such as advertising, healthcare, and finance. In…
Multiplayer Information Asymmetric Bandits in Metric Spaces
William Chang, Aditi Karthik
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…