Contextual Multi Armed Bandit

Contextual multi-armed bandits (CMABs) are a powerful framework for sequential decision-making under uncertainty, aiming to optimize cumulative rewards by selecting actions based on observed contextual information. Current research focuses on improving efficiency and robustness through advanced algorithms like Thompson Sampling and Upper Confidence Bound, often integrated with neural networks or tree ensembles for handling complex, non-linear relationships between context and reward. CMABs find significant application in diverse fields, including recommender systems, dynamic pricing, and autonomous robotics, where their ability to balance exploration and exploitation in personalized settings leads to improved performance and resource allocation.