Multi-Armed Bandit
Multi-Armed Bandit is a classic problem in probability theory and reinforcement learning that models the trade-off between exploration (trying new options to gather information) and exploitation (choosing the best-known option to maximize reward). It is often used in scenarios like A/B testing, clinical trials, or online advertising to optimize decisions over time. The name derives from slot machines ('one-armed bandits'), where each 'arm' represents a choice with an unknown probability of reward.
Developers should learn Multi-Armed Bandit algorithms when building systems that require adaptive decision-making under uncertainty, such as recommendation engines, dynamic pricing models, or adaptive user interfaces. It is particularly useful in online settings where you need to balance learning about new options with maximizing immediate performance, offering more efficient alternatives to traditional A/B testing by reducing regret over time.