Contextual Multi-Armed Bandit

A reinforcement learning framework in which an agent selects actions based on contextual information and optimizes its policy using reward signals. It is commonly applied to personalized recommendation and to learning decisions about timing. The framework models the trade-off between exploration and exploitation under context-dependent conditions, allowing action choices to adapt to observed features rather than relying on a single fixed policy.