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 21–30 of 1262 papers

TitleStatusHype
Deep Reinforcement Learning based Recommendation with Explicit User-Item Interactions ModelingCode1
Discovering Minimal Reinforcement Learning EnvironmentsCode1
Efficient Contextual Bandits with Continuous ActionsCode1
Equitable Restless Multi-Armed Bandits: A General Framework Inspired By Digital HealthCode1
A unifying framework for generalised Bayesian online learning in non-stationary environmentsCode1
Hierarchical Adaptive Contextual Bandits for Resource Constraint based RecommendationCode1
In-Context Reinforcement Learning for Variable Action SpacesCode1
Indexability is Not Enough for Whittle: Improved, Near-Optimal Algorithms for Restless BanditsCode1
LASeR: Learning to Adaptively Select Reward Models with Multi-Armed BanditsCode1
EE-Net: Exploitation-Exploration Neural Networks in Contextual BanditsCode1
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Benchmark Results

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