AI RESEARCH
A Comparative Study of Dynamic Programming and Reinforcement Learning in Finite Horizon Dynamic Pricing
arXiv CS.LG
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ArXi:2604.14059v1 Announce Type: cross This paper provides a systematic comparison between Fitted Dynamic Programming (DP), where demand is estimated from data, and Reinforcement Learning (RL) methods in finite-horizon dynamic pricing problems. We analyze their performance across environments of increasing structural complexity, ranging from a single typology benchmark to multi-typology settings with heterogeneous demand and inter-temporal revenue constraints.