Contextual Bandit Problem

The contextual bandit problem focuses on sequential decision-making where an agent selects actions based on observed contextual information to maximize cumulative rewards. Current research emphasizes efficient algorithms, including those leveraging Thompson sampling, upper confidence bounds, and neural networks, to address challenges like sparsity, high-dimensionality, and unbounded context distributions. These advancements are improving the performance and applicability of contextual bandit methods across diverse fields, such as personalized recommendations, online advertising, and resource allocation, by enabling more effective learning from limited feedback. Furthermore, research is actively exploring extensions to handle constraints, offline settings, and multi-agent scenarios.