DIY AI & ML: Solving The Multi-Armed Bandit Problem with Thompson Sampling

A tutorial explains how to implement Thompson Sampling in Python to solve the multi-armed bandit problem, a classic reinforcement learning challenge. The article covers building a custom algorithm object and applying it to a hypothetical real-world scenario, emphasizing data-driven decision-making. It positions Thompson Sampling as an alternative to traditional A/B testing for optimizing choices under uncertainty.

Member-only story DIY AI & ML: Solving The Multi-Armed Bandit Problem with Thompson Sampling How you can build your own Thompson Sampling Algorithm object in Python and apply it to a hypothetical yet real-life example Introduction We live in a golden age of data-driven decision-making. Not only do most organizations maintain massive databases of information, but they also have countless teams that rely on this data to inform their decision-making. From clickstream traffic to wearable edge devices, telemetry, and much more, the speed and scale of data-driven decision-making are increasing exponentially, driving the popularity of integrating machine learning and AI frameworks. Speaking of data-driven decision-making frameworks, one of the most reliable and time-tested approaches is A/B testing. A/B testing is especially popular among websites, digital products, and similar outlets where customer feedback in the form of clicks, orders, etc., is received nearly instantly and at scale. What makes A/B testing such a powerful decision framework is its ability to control for countless variables, allowing a stakeholder to see the effect of the element they are introducing in the test on a key performance indicator KPI .