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 951–960 of 1262 papers

TitleStatusHype
Corruption-robust exploration in episodic reinforcement learning—0
Unreliable Multi-Armed Bandits: A Novel Approach to Recommendation Systems—0
Contextual Bandits Evolving Over Finite Time—0
Triply Robust Off-Policy Evaluation—0
Incentivized Exploration for Multi-Armed Bandits under Reward Drift—0
Neural Contextual Bandits with UCB-based ExplorationCode0
Confidence Intervals for Policy Evaluation in Adaptive ExperimentsCode0
Multi-Armed Bandits with Correlated ArmsCode0
Persistency of Excitation for Robustness of Neural NetworksCode0
Problem Dependent Reinforcement Learning Bounds Which Can Identify Bandit Structure in MDPs—0
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Benchmark Results

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