Multi-Agent Reinforcement Learning for Dynamic Pricing: Balancing Profitability,Stability and Fairness

2026-03-19 · Source: cs.LG updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, E-commerce & Digital Commerce · Depth: Advanced, short

Summary

A study systematically evaluates multi-agent reinforcement learning (MARL) approaches, specifically MAPPO and MADDPG, for dynamic price optimization in competitive retail markets. Utilizing a simulated marketplace environment derived from real-world retail data, the research benchmarks these algorithms against an Independent DDPG (IDDPG) baseline. The evaluation focuses on profit performance, stability across random seeds, fairness, and training efficiency. Results indicate that MAPPO consistently achieves the highest average returns with low variance, demonstrating a stable and reproducible method for competitive pricing. MADDPG, while yielding slightly lower profits, provides the fairest profit distribution among agents. These findings suggest MARL methods, particularly MAPPO, offer a scalable and stable alternative to independent learning for dynamic retail pricing.

Key takeaway

For AI Scientists and Research Scientists developing dynamic pricing strategies, consider integrating Multi-Agent Reinforcement Learning (MARL) methods like MAPPO. Your systems could achieve higher average returns with greater stability compared to independent learning approaches. Evaluate both MAPPO for profit maximization and MADDPG for fairness in profit distribution to align with specific business objectives in competitive retail environments.

Key insights

MARL methods, especially MAPPO, offer stable and scalable dynamic pricing solutions in competitive retail.

Principles

• MAPPO provides high returns with low variance.
• MADDPG ensures fairer profit distribution.
• MARL outperforms independent learning for dynamic pricing.

Method

The study empirically evaluates MAPPO and MADDPG against an IDDPG baseline in a simulated retail market, assessing profit, stability, fairness, and training efficiency.

In practice

• Use MAPPO for maximum profit stability.
• Consider MADDPG for equitable profit sharing.
• Apply MARL in competitive retail pricing.

Topics

• Multi-Agent Reinforcement Learning
• Dynamic Pricing
• MAPPO
• MADDPG
• Retail Market Optimization

Best for: AI Scientist, Research Scientist, AI Researcher, Machine Learning Engineer, Data Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.LG updates on arXiv.org.