Dynamic Programming and RL have different strengths in pricing: DP optimizes based on estimated demand patterns but struggles with computational complexity, while RL learns from trial-and-error but may be less stable—the best choice depends on your problem's complexity and constraints.
This paper compares two approaches to dynamic pricing: Fitted Dynamic Programming (which estimates demand from data) and Reinforcement Learning.