What is multi-arm bandit evaluation in online learning systems?

It evaluates decision policies that balance exploration and exploitation.

Multi-armed bandits evaluate strategies that dynamically allocate traffic among competing options. Evaluation metrics include regret minimization and cumulative reward. Unlike A/B testing, bandits continuously adapt allocation based on observed performance.