Learning and Optimization with Seasonal Patterns

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A standard assumption adopted in the multi-armed bandit (MAB) framework is that the mean rewards are constant over time. This assumption can be restrictive in the business world as decision-makers often face an evolving environment where the mean rewards are time-varying. Ningyuan Chen discusses a non-stationary MAB model with K arms whose mean rewards vary over time in a periodic manner. 

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