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 841–850 of 1262 papers

TitleStatusHype
Restless Multi-Armed Bandits under Exogenous Global Markov Process—0
Restless Multi-armed Bandits under Frequency and Window Constraints for Public Service Inspections—0
Revisiting Simple Regret: Fast Rates for Returning a Good Arm—0
Reward Biased Maximum Likelihood Estimation for Reinforcement Learning—0
Reward Maximization for Pure Exploration: Minimax Optimal Good Arm Identification for Nonparametric Multi-Armed Bandits—0
Reward Teaching for Federated Multi-armed Bandits—0
Rising Rested Bandits: Lower Bounds and Efficient Algorithms—0
Risk-Averse Multi-Armed Bandits with Unobserved Confounders: A Case Study in Emotion Regulation in Mobile Health—0
Risk averse non-stationary multi-armed bandits—0
Risk-Aversion in Multi-armed Bandits—0
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Benchmark Results

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