Offline Local Search for Online Stochastic Bandits
Combinatorial multi-armed bandits provide a fundamental online decision-making environment where a decision-maker interacts with an environment across $T$ time steps, each time selecting an action and learning the cost of that action. The goal is to minimize regret, defined as the loss compared to the optimal fixed action in hindsight under full-information. There has been substantial interest in leveraging what is known about offline algorithm design in this online setting. Offline greedy and linear optimization algorithms (both exact and approximate) have been shown to provide useful guarantees when deployed online. We investigate local search methods, a broad class of algorithms used w...
| Authors | Gerdus Benadè, Rathish Das, Thomas Lavastida |
|---|---|
| Primary category | cs.LG |
| Published | 10 April 2026 |
| Last updated at source | 10 April 2026 |
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