Research map / Bandits and online learning / Multi-armed bandits
Decentralized Stochastic Multi-Player Multi-Armed Walking Bandits
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| share reward estimates among neighboring players (ablation) | MPMAB-WA-UCB · MPMAB-WA | regret upper bound: a factor of O(N) | Helped |
| share reward estimates among neighboring players (ablation) | MPMAB-WA-UCB · Constructed instance | MSE of mean reward | Helped |
| share reward estimates among neighboring players (ablation) | MPMAB-WA-UCB · Constructed instance | accumulated regret | Helped |
| share local walking arm sets and use matching and ranking to coordinate arm choices | MPMAB-WA-UCB · MPMAB-WA | collisions | Helped |
Closest papers by meaning
- SIC-MMAB: Synchronisation Involves Communication in Multiplayer Multi-Armed Bandits (NeurIPS 2019 · similarity 0.93)
- Multi-Player Bandits – a Musical Chairs Approach (ICML 2016 · similarity 0.92)
- Decentralized Cooperative Stochastic Bandits (NeurIPS 2019 · similarity 0.91)
- Heterogeneous Multi-player Multi-armed Bandits: Closing the Gap and Generalization (NeurIPS 2021 · similarity 0.90)
- Optimal Algorithm for Max-Min Fair Bandit (ICML 2025 · similarity 0.88)
- Nash Social Welfare for Multi Armed Bandits: Trajectory-wise Expected and High Probability Regret (NeurIPS 2026 · similarity 0.88)
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