Implementation of the Multi-Armed Bandit where each arm returns continuous numerical rewards. Covers Epsilon-Greedy, UCB1, and Thompson Sampling with detailed explanations.
This repository explores the Multi-Armed Bandit problem using numerical data instead of the traditional Bernoulli distribution (which returns only 0 or 1). It provides a comprehensive overview of the fundamental concepts, alongside practical implementations of three popular algorithms:
✅ Epsilon-Greedy – Balancing exploration and exploitation through a probability-based approach. ✅ UCB1 (Upper Confidence Bound) – Optimizing decision-making with confidence intervals. ✅ Thompson Sampling – A Bayesian approach to adaptive learning.
The notebook includes detailed explanations, code implementations, and visualizations to help you understand how these algorithms work in real-world scenarios.
📌 Ideal for: Data scientists, AI researchers, and anyone interested in reinforcement learning.
Feel free to explore, experiment, and contribute! 🚀