Bandit Algorithms

Bandit algorithms are decision-making methods used when choosing between options with uncertain outcomes, aiming to maximize rewards over time. Imagine a gambler trying different slot machines (arms) to find the most rewarding one while still exploring new machines. These algorithms balance exploring new options to discover better rewards and exploiting known good options to maximize returns. They are used in online recommendations, adaptive marketing, and robotics, helping systems learn optimal choices through trial and error, efficiently managing the trade-off between exploring new possibilities and exploiting known successful ones.