Contextual Bandits

Contextual bandits are a machine learning framework that extends multi-armed bandits by incorporating contextual information (features) to make decisions. They are used for sequential decision-making problems where an agent must choose actions (arms) based on observed contexts to maximize cumulative reward over time, while balancing exploration and exploitation. This approach is widely applied in personalized recommendations, online advertising, and clinical trials.

Also known as: Contextual Multi-Armed Bandits, Contextual MAB, Contextual Bandit Algorithms, Contextual Decision Making, Contextual Reinforcement Learning (simplified)

Why learn Contextual Bandits?

Developers should learn contextual bandits when building systems that require adaptive, real-time decision-making with feedback, such as recommendation engines, dynamic pricing, or A/B testing platforms. They are particularly useful in scenarios where data is limited or expensive to collect, as they efficiently explore options while exploiting known information to optimize outcomes. This makes them ideal for applications in e-commerce, content personalization, and healthcare interventions.

Learning Resources

• docs: Contextual Bandits: An Introduction
• tutorial: Contextual Bandits Tutorial on Kaggle
• book: Reinforcement Learning: An Introduction (Chapter on Bandits)
• tutorial: Contextual Bandits in Python with Vowpal Wabbit
• course: Coursera: Reinforcement Learning Specialization (includes bandits)