Research map / Bandits and online learning / Multi-armed bandits
Multiple-Play Stochastic Bandits with Shareable Finite-Capacity Arms
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use OrchExplore instead of MP-SE-SA or ETC-UCB | MP-MAB-SA · Bernoulli-reward simulation | regret | Helped |
| Use the proposed uniform confidence interval (UCI) instead of Hoeffding-based intervals (ablation) | OrchExplore · Bernoulli-reward simulation | regret | Helped |
| Learn unknown capacities rather than use known capacities (ablation) | OrchExplore · Bernoulli-reward simulation | regret | Hurt |
| Model shareable arms explicitly and use OrchExplore instead of treating each allocation as an independent arm | MP-MAB-SA · Bernoulli-reward simulation | regret | Helped |
Closest papers by meaning
- Multiple-play Stochastic Bandits with Prioritized Arm Capacity Sharing (AAAI 2026 · similarity 0.93)
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- Multi-Player Bandits – a Musical Chairs Approach (ICML 2016 · similarity 0.90)
- Regret Minimisation in Multi-Armed Bandits Using Bounded Arm Memory (AAAI 2020 · similarity 0.89)
- Statistical and Computational Trade-off in Multi-Agent Multi-Armed Bandits (NeurIPS 2023 · similarity 0.89)
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