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The Multi-Armed Bandit Problem—A Beginner-Friendly Guide | by Saankhya Mondal | Dec, 2024

December 23, 2024
in Artificial Intelligence
Reading Time: 2 mins read
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Understanding the exploitation-exploration trade-off with an instance

Saankhya Mondal
Towards Data Science

A Multi-Armed Bandit (MAB) is a basic drawback in decision-making, the place an agent should select between a number of choices (known as “arms”) and maximize the entire reward over a sequence of trials. The issue will get its identify from a metaphor involving a gambler at a row of slot machines (one-armed bandits), every with a unique however unknown likelihood of paying out. The purpose is to search out the most effective technique to tug the arms (choose actions) and maximize the gambler’s total reward over time. The MAB drawback is a flowery identify for the exploitation-exploration trade-off.

The Multi-Armed Bandit drawback is a foundational drawback that arises in quite a few industrial functions. Let’s discover it and study attention-grabbing methods for fixing it.

Picture generated by Grok

You’ve simply arrived in a brand new metropolis. You’re a spy and plan to remain for 120 days to finish your subsequent task. There are three eating places on the town: Italian, Chinese language, and Mexican. You need to maximize your eating satisfaction throughout your keep. Nevertheless, you don’t know which restaurant would be the greatest for you. Right here’s how the three eating places stack up:

Italian restaurant: Common satisfaction rating of…

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Tags: BanditBeginnerFriendlyDecGuideMondalMultiArmedProblemASaankhya
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