A Comparative Study of Dynamic Programming and Reinforcement Learning in Finite-Horizon Dynamic Pricing
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摘要
This paper empirically compares fitted dynamic programming (DP) and model-free reinforcement learning (DQN, A2C, PPO) on four finite-horizon dynamic pricing environments (single and multi-typology, with/without cumulative revenue constraints), evaluating revenue, stability, constraint satisfaction and computational scaling; results show DP is a stable, high-performing benchmark while RL (PPO/A2C) can approach DP given sufficient episodes but exhibits higher variance and slower convergence in low-data or higher-dimensional settings [page::0][page::9].
