The Multi-Armed Bandit Problem—A Beginner-Friendly Guide | by Saankhya Mondal | Dec, 2024
[ad_1]
Understanding the exploitation-exploration trade-off with an example
A Multi-Armed Bandit (MAB) is a classic problem in decision-making, where an agent must choose between multiple options (called “arms”) and maximize the total reward over a series of trials. The problem gets its name from a metaphor involving a gambler at a row of slot machines (one-armed bandits), each with a different but unknown probability of paying out. The goal is to find the best strategy to pull the arms (select actions) and maximize the gambler’s overall reward over time. The MAB problem is a fancy name for the exploitation-exploration trade-off.
The Multi-Armed Bandit problem is a foundational problem that arises in numerous industrial applications. Let’s explore it and examine interesting strategies for solving it.
You’ve just arrived in a new city. You’re a spy and plan to stay for 120 days to complete your next assignment. There are three restaurants in town: Italian, Chinese, and Mexican. You want to maximize your dining satisfaction during your stay. However, you don’t know which restaurant will be the best for you. Here’s how the three restaurants stack up:
- Italian restaurant: Average satisfaction score of…
[ad_2]
