2016
ICML
ICML 2016
Anytime optimal algorithms in stochastic multi-armed bandits
Abstract
We introduce an anytime algorithm for stochastic multi-armed bandit with optimal distribution free and distribution dependent bounds (for a specific family of parameters). The performances of this algorithm (as well as another one motivated by the conjectured optimal bound) are evaluated empirically. A similar analysis is provided with full information, to serve as a benchmark.
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Interdisciplinary Bridge
- Artificial Intelligence and Mathematics & Optimization
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Hot Topic Early Bird
- reinforcement learning