Bandit Algorithms
Bandit algorithms are a class of online learning techniques used to solve the exploration-exploitation trade-off in sequential decision-making problems. They are inspired by the multi-armed bandit problem, where an agent must choose between multiple options (arms) with unknown rewards to maximize cumulative gain over time. These algorithms balance trying new options to gather information (exploration) and leveraging known high-reward options (exploitation).
Developers should learn bandit algorithms when building systems that require adaptive decision-making under uncertainty, such as A/B testing, recommendation engines, online advertising, and clinical trials. They are particularly useful in scenarios where decisions must be made in real-time with limited feedback, as they provide efficient strategies to optimize outcomes without requiring full knowledge of the environment upfront.