Vol. 4 No. 1 (2024): January−June 2024
Articles

DRL PricePro: A Deep Reinforcement Learning Framework for Personalized Dynamic Pricing in E-commerce Platforms with Supply Constraints

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  • Chenyao Zhu,
  • Caiqian Cheng,
  • Sisi Meng
Chenyao Zhu
zhu9175@gmail.com
Caiqian Cheng
Computer Science, University of California, San Diego, CA, USA
Sisi Meng
Accounting, University of Rochester, NY, USA

Published 2024-05-13

Keywords

  • Deep Reinforcement Learning,
  • Dynamic Pricing,
  • Supply Constraints,
  • Personalization

How to Cite

Zhu, C., Cheng, C., & Meng, S. (2024). DRL PricePro: A Deep Reinforcement Learning Framework for Personalized Dynamic Pricing in E-commerce Platforms with Supply Constraints. Spectrum of Research, 4(1). Retrieved from https://spectrumofresearch.com/index.php/sr/article/view/28

Copyright (c) 2024 Spectrum of Research

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

This paper introduces DRL-PricePro, a novel deep reinforcement learning framework for personalized dynamic pricing in e-commerce platforms that explicitly incorporates supply constraints. Traditional pricing optimization approaches often overlook inventory limitations, leading to suboptimal pricing strategies when product availability becomes a significant factor. The proposed framework formulates the dynamic pricing problem as a constrained Markov Decision Process (CMDP) and implements a modified Soft Actor-Critic algorithm optimized for e-commerce environments. DRL-PricePro integrates multi-level constraint handling mechanisms that combine hard inventory limitations with soft constraints driven by learned inventory patterns. The framework incorporates a personalization engine that segments customers based on behavioral patterns and adapts pricing strategies accordingly while respecting supply limitations. Experimental evaluation on a large-scale e-commerce dataset comprising 2.7 million transactions demonstrates that DRL-PricePro achieves a 12.3% revenue improvement over static pricing baselines and outperforms rule-based dynamic pricing approaches by 7.6%. The framework maintains a 98.7% constraint satisfaction rate, significantly exceeding the 76.4% compliance rate of unconstrained reinforcement learning approaches. Case studies across seasonal products, limited-stock premium items, and flash sale scenarios reveal practical business insights regarding the relationship between supply constraints and optimal pricing strategies, demonstrating the framework's effectiveness in real-world e-commerce applications.

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