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SIC-MMAB: Synchronisation Involves Communication in Multiplayer Multi-Armed Bandits

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arxiv 1809.08151 v4 pith:RXOFQ4TK submitted 2018-09-21 cs.LG stat.ML

classification cs.LGstat.ML
keywords communicationplayersalgorithmcollisionsmodelmulti-armedmultiplayerpossible
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Motivated by cognitive radio networks, we consider the stochastic multiplayer multi-armed bandit problem, where several players pull arms simultaneously and collisions occur if one of them is pulled by several players at the same stage. We present a decentralized algorithm that achieves the same performance as a centralized one, contradicting the existing lower bounds for that problem. This is possible by "hacking" the standard model by constructing a communication protocol between players that deliberately enforces collisions, allowing them to share their information at a negligible cost. This motivates the introduction of a more appropriate dynamic setting without sensing, where similar communication protocols are no longer possible. However, we show that the logarithmic growth of the regret is still achievable for this model with a new algorithm.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Multiplayer Bandit Learning, from Competition to Cooperation

    cs.GT 2019-08 conditional novelty 6.0 of 10

    In a two-player bandit game where players see each other's actions but not rewards, competition reduces exploration, cooperation increases it, and neutral players can outperform a single player by observing each other.

  2. Accelerated learning from recommender systems using multi-armed bandit

    cs.IR 2019-08 conditional novelty 4.0 of 10

    A Vrbo team used daily Thompson sampling to rank four recommendation models by click-through rate, but the A/B validation they report is for a previous campaign's winner, not the current one.

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