In this part of the series, we delve deeper into the Reinforcement Learning environment using the Multi-Armed Bandit problem as an example. I explain how to model different scenarios where each "arm" represents a user group segmented by attributes like age, gender, city, and phone operating system. Through a practical implementation in Python, I walk you through how we calculate initial probabilities and assign rewards based on user behavior.
Using a simple case where male users have a 70% chance of clicking and female users have a 30% chance, we model the reward system for each arm, showing how Reinforcement Learning can optimize decisions in real-world environments.
If you're keen on understanding how to set up and interact with an RL environment, this video will help you develop the right intuition. Stay tuned for more advanced modules!
Негізгі бет Understanding the Environment in Reinforcement Learning: Multi-Armed Bandit Problem Explained Ch. 2
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