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arXiv · 2511.00039

Graph-Attentive MAPPO for Dynamic Retail Pricing

Krishna Kumar Neelakanta Pillai Santha Kumari Amma
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Abstract

Dynamic pricing in retail requires policies that adapt to shifting demand while coordinating decisions across related products. We present a systematic empirical study of multi-agent reinforcement learning for retail price optimization, comparing a strong MAPPO baseline with a graph-attention-augmented variant (MAPPO+GAT) that leverages learned interactions among products. Using a simulated pricing environment derived from real transaction data, we evaluate profit, stability across random seeds, fairness across products, and training efficiency under a standardized evaluation protocol. The results indicate that MAPPO provides a robust and reproducible foundation for portfolio-level price control, and that MAPPO+GAT further enhances performance by sharing information over the product graph without inducing excessive price volatility. These results indicate that graph-integrated MARL provides a more scalable and stable solution than independent learners for dynamic retail pricing, offering practical advantages in multi-product decision-making.

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Krishna Kumar Neelakanta Pillai Santha Kumari Amma. 2025-10-28. Graph-Attentive MAPPO for Dynamic Retail Pricing. https://arxiv.org/abs/2511.00039

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