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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 201–225 of 1262 papers

TitleStatusHype
Optimal Batched Linear BanditsCode0
Combinatorial Multivariant Multi-Armed Bandits with Applications to Episodic Reinforcement Learning and Beyond—0
Global Rewards in Restless Multi-Armed Bandits—0
Strategic Linear Contextual Bandits—0
A Batch Sequential Halving Algorithm without Performance Degradation—0
No-Regret Learning for Fair Multi-Agent Social Welfare Optimization—0
Understanding Memory-Regret Trade-Off for Streaming Stochastic Multi-Armed Bandits—0
Optimizing Sharpe Ratio: Risk-Adjusted Decision-Making in Multi-Armed Bandits—0
Causal Contextual Bandits with Adaptive ContextCode0
Multi-Armed Bandits with Network InterferenceCode0
Offline Oracle-Efficient Learning for Contextual MDPs via Layerwise Exploration-Exploitation Tradeoff—0
Multi-Player Approaches for Dueling Bandits—0
Indexed Minimum Empirical Divergence-Based Algorithms for Linear Bandits—0
Budgeted Recommendation with Delayed Feedback—0
No-Regret is not enough! Bandits with General Constraints through Adaptive Regret Minimization—0
Optimal Baseline Corrections for Off-Policy Contextual BanditsCode0
Federated Combinatorial Multi-Agent Multi-Armed Bandits—0
Imprecise Multi-Armed Bandits—0
Leveraging (Biased) Information: Multi-armed Bandits with Offline Data—0
Mathematics of statistical sequential decision-making: concentration, risk-awareness and modelling in stochastic bandits, with applications to bariatric surgery—0
Provably Efficient Reinforcement Learning for Adversarial Restless Multi-Armed Bandits with Unknown Transitions and Bandit Feedback—0
Recommenadation aided Caching using Combinatorial Multi-armed Bandits—0
Disentangling Exploration from Exploitation—0
Causally Abstracted Multi-armed BanditsCode0
Structured Reinforcement Learning for Delay-Optimal Data Transmission in Dense mmWave Networks—0
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Benchmark Results

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