Thomas Hazenberg, Yao Ma, Seyed Sahand Mohammadi Ziabari, Marijn van Rijswijk
arXiv 3 Jul 2025 · Machine Learning
arXiv:2507.02698 · PDF · Extracted main text
This study investigates how Multi-Agent Reinforcement Learning (MARL) can improve dynamic pricing strategies in supply chains, particularly in contexts where traditional ERP systems rely on static, rule-based approaches that overlook strategic interactions among market actors. While recent research has applied reinforcement learning to pricing, most implementations remain single-agent and fail to model the interdependent nature of real-world supply chains. This study addresses that gap by evaluating the performance of three MARL algorithms: MADDPG, MADQN, and QMIX against static rule-based baselines, within a simulated environment informed by real e-commerce transaction data and a LightGBM demand prediction model. Results show that rule-based agents achieve near-perfect fairness (Jain's Index: 0.9896) and the highest price stability (volatility: 0.024), but they fully lack competitive dynamics. Among MARL agents, MADQN exhibits the most aggressive pricing behaviour, with the highest volatility and the lowest fairness (0.5844). MADDPG provides a more balanced approach, supporting market competition (share volatility: 9.5 pp) while maintaining relatively high fairness (0.8819) and stable pricing. These findings suggest that MARL introduces emergent strategic behaviour not captured by static pricing rules and may inform future developments in dynamic pricing.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Annie Wong, Thomas Bäck, Anna V Kononova, and Aske Plaat (2023) Deep multiagent reinforcement learning: Challenges and directions | 1.000 | 6 | 3 | 100% |
| 2 | Kallirroi Georgila, Claire Nelson, and David Traum (2014) Single-agent vs. multi-agent techniques for concurrent reinforcement learning of negotiation dialogue policies. In Proceedings o… | 0.737 | 3 | 2 | 100% |
| 3 | Ziyuan Zhou, Guanjun Liu, and Ying Tang (2023) Multi-agent reinforcement learning: Methods, applications, visionary prospects, and challenges | 0.737 | 3 | 2 | 100% |
| 4 | Lucian Busoniu, Robert Babuska, and Bart De Schutter (2008) A comprehensive survey of multiagent reinforcement learning | 0.644 | 2 | 2 | 100% |
| 5 | Le Li, Xiao Lin, Rudy R Negenborn, and Bart De Schutter (2015) Pricing intermodal freight transport services: A cost-plus-pricing strategy. In Computational Logistics: 6th International Confe… | 0.644 | 2 | 2 | 100% |
| 6 | Ryan Lowe, Yi Wu, Aviv Tamar, Jean Harb, Pieter Abbeel, and Igor Mor… (2017) Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments. In Advances in Neural Information Processing Systems, V… | 0.644 | 2 | 2 | 100% |
| 7 | Goncalo Neto (2005) From Single-Agent to Multi-Agent Reinforcement Learning: Foundational Concepts and Methods | 0.644 | 2 | 2 | 100% |
| 8 | Christopher JCH Watkins and Peter Dayan (1992) Q-learning | 0.585 | 3 | 1 | 100% |
| 9 | Jacob Hilton, Jie Tang, and John Schulman (2023) Scaling laws for single-agent reinforcement learning | 0.511 | 2 | 1 | 100% |
| 10 | Vipul Jain and Lyes Benyoucef (2008) Managing long supply chain networks: some emerging issues and challenges | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 61 scored citations.