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 31–40 of 1262 papers

TitleStatusHype
Competing for Shareable Arms in Multi-Player Multi-Armed BanditsCode1
Multiplayer Multi-armed Bandits for Optimal Assignment in Heterogeneous NetworksCode1
Deep Bandits Show-Off: Simple and Efficient Exploration with Deep NetworksCode1
Deep Reinforcement Learning based Recommendation with Explicit User-Item Interactions ModelingCode1
Neural Exploitation and Exploration of Contextual BanditsCode1
EE-Net: Exploitation-Exploration Neural Networks in Contextual BanditsCode1
Equitable Restless Multi-Armed Bandits: A General Framework Inspired By Digital HealthCode1
Bayesian Optimisation over Multiple Continuous and Categorical InputsCode0
Adapting multi-armed bandits policies to contextual bandits scenariosCode0
Best Arm Identification with Fixed Budget: A Large Deviation PerspectiveCode0
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Benchmark Results

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