The study compares dynamic programming and reinforcement learning performance on revenue, stability, constraint satisfaction, and scaling in dynamic pricing environments from single to multi-product settings with heterogeneous demand.
Dynamic pric- ing without knowing the demand function: Risk bounds and near-optimal algorithms.Op- erations Research, 57(6):1407–1420
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A Comparative Study of Dynamic Programming and Reinforcement Learning in Finite Horizon Dynamic Pricing
The study compares dynamic programming and reinforcement learning performance on revenue, stability, constraint satisfaction, and scaling in dynamic pricing environments from single to multi-product settings with heterogeneous demand.