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 671–680 of 1262 papers

TitleStatusHype
Fundamental Limits of Online and Distributed Algorithms for Statistical Learning and Estimation—0
Garbage In, Reward Out: Bootstrapping Exploration in Multi-Armed Bandits—0
Gaussian Process bandits with adaptive discretization—0
Generalized Policy Elimination: an efficient algorithm for Nonparametric Contextual Bandits—0
Generalized Risk-Aversion in Stochastic Multi-Armed Bandits—0
Generalized Thompson Sampling for Contextual Bandits—0
Generalized Translation and Scale Invariant Online Algorithm for Adversarial Multi-Armed Bandits—0
Generalizing distribution of partial rewards for multi-armed bandits with temporally-partitioned rewards—0
Genetic multi-armed bandits: a reinforcement learning approach for discrete optimization via simulation—0
GINO-Q: Learning an Asymptotically Optimal Index Policy for Restless Multi-armed Bandits—0
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Benchmark Results

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