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Decentralized Multi-player Multi-armed Bandits with No Collision Information
The decentralized stochastic multi-player multi-armed bandit (MP-MAB) pr...
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Non-Stochastic Multi-Player Multi-Armed Bandits: Optimal Rate With Collision Information, Sublinear Without
We consider the non-stochastic version of the (cooperative) multi-player...
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SIC-MMAB: Synchronisation Involves Communication in Multiplayer Multi-Armed Bandits
We consider the stochastic multiplayer multi-armed bandit problem, where...
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Multi-Player Bandits: A Trekking Approach
We study stochastic multi-armed bandits with many players. The players d...
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Contention Resolution Without Collision Detection
This paper focuses on the contention resolution problem on a shared comm...
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Multi-Player Bandits Models Revisited
Multi-player Multi-Armed Bandits (MAB) have been extensively studied in ...
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Player Modeling via Multi-Armed Bandits
This paper focuses on building personalized player models solely from pl...
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On No-Sensing Adversarial Multi-player Multi-armed Bandits with Collision Communications
We study the notoriously difficult no-sensing adversarial multi-player multi-armed bandits (MP-MAB) problem from a new perspective. Instead of focusing on the hardness of multiple players, we introduce a new dimension of hardness, called attackability. All adversaries can be categorized based on the attackability and we introduce Adversary-Adaptive Collision-Communication (A2C2), a family of algorithms with forced-collision communication among players. Both attackability-aware and unaware settings are studied, and information-theoretic tools of the Z-channel model and error-correction coding are utilized to address the challenge of implicit communication without collision information in an adversarial environment. For the more challenging attackability-unaware problem, we propose a simple method to estimate the attackability enabled by a novel error-detection repetition code and randomized communication for synchronization. Theoretical analysis proves that asymptotic attackability-dependent sublinear regret can be achieved, with or without knowing the attackability. In particular, the asymptotic regret does not have an exponential dependence on the number of players, revealing a fundamental tradeoff between the two dimensions of hardness in this problem.
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