The Price of Decentralization in Top-$K$ Arm Identification
Cooperative teams often need to agree on the best few options rather than simply accumulate reward, and they must do so while each member sees only a fragment of the team's collective experience. We study this as top-$K$ joint-arm identification in multi-agent multi-armed bandits: at every round $M$ agents simultaneously choose individual actions that compose a joint arm, and the team must ultimately return the $K$ joint arms of highest mean reward. The difficulty is that an agent may not observe the actions of others, their rewards, or either. We treat three observability regimes---(A) shared rewards with hidden actions, (B) observed actions with private rewards, and (C) full asymmetry--...
| Authors | Larissa Xu, Jasmine Nguyen, William Chang |
|---|---|
| Primary category | cs.LG |
| Published | 22 August 2026 |
| Last updated at source | 22 August 2026 |
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