SOTAVerified

Multi-Armed Bandits

Multi-armed bandits refer to a task where a fixed amount of resources must be allocated between competing resources that maximizes expected gain. Typically these problems involve an exploration/exploitation trade-off.

( Image credit: Microsoft Research )

Papers

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Showing 821–830 of 1262 papers

TitleStatusHype
Multi-agent Multi-armed Bandits with Stochastic Sharable Arm Capacities—0
Multi-Agent Multi-Armed Bandits with Limited Communication—0
Multi-agent Multi-armed Bandit with Fully Heavy-tailed Dynamics—0
Multi-Agent Stochastic Bandits Robust to Adversarial Corruptions—0
Multi-armed Bandit Learning for TDMA Transmission Slot Scheduling and Defragmentation for Improved Bandwidth Usage—0
Multi-Armed Bandits and Quantum Channel Oracles—0
Multi-armed Bandits: Competing with Optimal Sequences—0
Multi-Armed Bandits for Correlated Markovian Environments with Smoothed Reward Feedback—0
Multi-Armed Bandits for Intelligent Tutoring Systems—0
Multi-armed Bandits for Link Configuration in Millimeter-wave Networks—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1NeuralLinear FullPosterior-MRCumulative regret1.92—Unverified
2Linear FullPosterior-MRCumulative regret1.82—Unverified