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arXiv · 2604.14059

A Comparative Study of Dynamic Programming and Reinforcement Learning in Finite Horizon Dynamic Pricing

Lev Razumovskiy·Nikolay Karenin
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Abstract

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. Unlike simplified comparisons that restrict DP to low-dimensional settings, we apply dynamic programming in richer, multi-dimensional environments with multiple product types and constraints. We evaluate revenue performance, stability, constraint satisfaction behavior, and computational scaling, highlighting the trade-offs between explicit expectation-based optimization and trajectory-based learning.

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Lev Razumovskiy, Nikolay Karenin. 2026-04-15. A Comparative Study of Dynamic Programming and Reinforcement Learning in Finite Horizon Dynamic Pricing. https://arxiv.org/abs/2604.14059

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