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Core
Multi-Armed Bandits
A multi-armed bandit chooses among options to maximize reward while learning which is best, balancing exploration (try options to learn) against exploitation (use the best-known). Algorithms include epsilon-greedy, UCB, and Thompson sampling. Bandits beat fixed A/B tests when you want to minimize regret (stop wasting traffic on losers during the test) or have many options; A/B tests win when you need a clean, unbiased measured effect. Applied-AI interviews probe it because the explore-exploit trade-off appears in ranking, recommendation, and as a simple form of reinforcement learning.
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