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 251–260 of 1262 papers

TitleStatusHype
Communication Efficient Distributed Learning for Kernelized Contextual Bandits—0
Adversarial Bandits with Knapsacks—0
Computationally Efficient Horizon-Free Reinforcement Learning for Linear Mixture MDPs—0
Concurrent Decentralized Channel Allocation and Access Point Selection using Multi-Armed Bandits in multi BSS WLANs—0
Adapting to Delays and Data in Adversarial Multi-Armed Bandits—0
Combining Online Learning and Offline Learning for Contextual Bandits with Deficient Support—0
A Survey of Learning in Multiagent Environments: Dealing with Non-Stationarity—0
Combining Difficulty Ranking with Multi-Armed Bandits to Sequence Educational Content—0
Combinatorial Semi-Bandits with Knapsacks—0
A Sleeping, Recovering Bandit Algorithm for Optimizing Recurring Notifications—0
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Benchmark Results

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