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 221–230 of 1262 papers

TitleStatusHype
Provably Efficient Reinforcement Learning for Adversarial Restless Multi-Armed Bandits with Unknown Transitions and Bandit Feedback—0
Recommenadation aided Caching using Combinatorial Multi-armed Bandits—0
Disentangling Exploration from Exploitation—0
Causally Abstracted Multi-armed BanditsCode0
Structured Reinforcement Learning for Delay-Optimal Data Transmission in Dense mmWave Networks—0
Sequential Decision Making with Expert Demonstrations under Unobserved HeterogeneityCode0
Generalized Linear Bandits with Limited AdaptivityCode0
Feel-Good Thompson Sampling for Contextual Dueling Bandits—0
Hypothesis Generation with Large Language ModelsCode2
On the Importance of Uncertainty in Decision-Making with Large Language Models—0
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Benchmark Results

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