Bandit

A Bandit is a type of algorithm used in machine learning and decision-making that balances trying new options (exploration) with sticking to what’s known to work (exploitation). Imagine a slot machine (bandit) with multiple arms; the goal is to maximize rewards by learning which arm to pull. The Bandit algorithm systematically tests different options, gradually favoring the best-performing ones while still exploring others to avoid missing out on potentially better choices. This approach is useful in areas like online advertising, recommendation systems, and resource management, where optimizing outcomes with limited information is crucial.