Multi-Armed Bandits with Bounded Arm-Memory: Near-Optimal Guarantees for Best-Arm Identification and Regret Minimization

Part of Advances in Neural Information Processing Systems 34 pre-proceedings (NeurIPS 2021)

Paper Supplemental

Bibtek download is not available in the pre-proceeding


Arnab Maiti, Vishakha Patil, Arindam Khan


We study the Stochastic Multi-armed Bandit problem under bounded arm-memory. In this setting, the arms arrive in a stream, and the number of arms that can be stored in the memory at any time, is bounded. The decision-maker can only pull arms that are present in the memory. We address the problem from the perspective of two standard objectives: 1) regret minimization, and 2) best-arm identification. For regret minimization, we settle an important open question by showing an almost tight guarantee. We show $\Omega(T^{2/3})$ cumulative regret in expectation for single-pass algorithms for arm-memory size of $(n-1)$, where $n$ is the number of arms. For best-arm identification, we provide an $(\varepsilon, \delta)$-PAC algorithm with arm memory size of $O(\log^*n)$ and $O(\frac{n}{\varepsilon^2}\cdot \log(\frac{1}{\delta}))$ optimal sample complexity.