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 571–580 of 1262 papers

TitleStatusHype
Contextual Pandora's Box—0
Information-Directed Selection for Top-Two AlgorithmsCode0
Neural Contextual Bandits Based Dynamic Sensor Selection for Low-Power Body-Area Networks—0
Computationally Efficient Horizon-Free Reinforcement Learning for Linear Mixture MDPs—0
Falsification of Multiple Requirements for Cyber-Physical Systems Using Online Generative Adversarial Networks and Multi-Armed Bandits—0
Contextual Information-Directed Sampling—0
Pessimism for Offline Linear Contextual Bandits using _p Confidence Sets—0
Stability Enforced Bandit Algorithms for Channel Selection in Remote State Estimation of Gauss-Markov Processes—0
Multi-Armed Bandits in Brain-Computer InterfacesCode0
Breaking the T Barrier: Instance-Independent Logarithmic Regret in Stochastic Contextual Linear Bandits—0
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Benchmark Results

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