Hierarchical Bayesian Bandit

Hierarchical Bayesian bandits address the challenge of efficiently learning optimal actions across multiple related tasks or levels of decision-making. Research focuses on developing algorithms, such as hierarchical Thompson sampling, that leverage shared information between these tasks to improve learning efficiency and reduce regret, often within a multi-armed bandit framework. This approach finds applications in diverse fields, including network management and resource allocation, where efficient exploration and exploitation of options across hierarchical structures are crucial for optimal performance. The resulting theoretical analyses and practical algorithms offer a powerful framework for solving complex sequential decision problems with inherent structure.