cfoh/Multi-Armed-Bandit-Example
Learning Multi-Armed Bandits by Examples. Currently covering MAB, UCB, Boltzmann Exploration, Thompson Sampling, Contextual MAB, LinUCB, Deep MAB.
This project helps marketers and product managers optimize decisions in real-time by showing the best option among several choices. For example, you feed in different ad creatives, and it tells you which one customers click most. This helps you quickly learn and adapt your strategy to maximize positive outcomes like ad clicks or product purchases.
Use this if you need to continuously learn which of several options performs best (e.g., ad variations, product recommendations, or website layouts) and adjust your strategy on the fly.
Not ideal if your decisions don't have immediate, measurable feedback, or if you need a static, one-time recommendation rather than continuous learning.
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45
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7
Language
Python
License
MIT
Category
Last pushed
Nov 25, 2025
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