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 511–520 of 1262 papers

TitleStatusHype
Decision Automation for Electric Power Network Recovery—0
Decentralized Smart Charging of Large-Scale EVs using Adaptive Multi-Agent Multi-Armed Bandits—0
Batched Thompson Sampling—0
An Adaptive Method for Contextual Stochastic Multi-armed Bandits with Rewards Generated by a Linear Dynamical System—0
Decentralized Multi-player Multi-armed Bandits with No Collision Information—0
Decentralized Upper Confidence Bound Algorithms for Homogeneous Multi-Agent Multi-Armed Bandits—0
Batched Online Contextual Sparse Bandits with Sequential Inclusion of Features—0
Decentralized Exploration in Multi-Armed Bandits -- Extended version—0
Batched Nonparametric Contextual Bandits—0
Decentralized Cooperative Reinforcement Learning with Hierarchical Information Structure—0
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Benchmark Results

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