A Comparative Study of Dynamic Programming and Reinforcement Learning in Finite Horizon Dynamic Pricing
On the map: Deep reinforcement learning methods → Adversarial robustness and RL in finance
Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Adversarial robustness and RL in finance, one of 15 directions in Deep reinforcement learning methods. Builds on 1 of its 12 references that are in the map. First posted on arXiv 15 Apr 2026.
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References · 1
- 2016Dynamic Pricing in High-dimensionscited by 55
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