Multi-Armed Bandit
Multi-Armed Bandit (MAB) 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 involves an agent repeatedly choosing from a set of actions (arms) with unknown reward distributions, aiming to maximize cumulative reward over time. This framework is widely used in online optimization, A/B testing, and adaptive decision-making systems.
Developers should learn Multi-Armed Bandit algorithms when building systems that require adaptive decision-making under uncertainty, such as recommendation engines, online advertising, clinical trials, or dynamic pricing. It is particularly useful for scenarios where traditional A/B testing is inefficient, as it allows for continuous learning and optimization while minimizing regret (the loss from not choosing the optimal arm).