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

A new research paper titled "Multi-Agent Reinforcement Learning for Dynamic Pricing: Balancing Profitability, Stability and Fairness" presents a comprehensive evaluation of how AI agents can optimize pricing in competitive retail markets. The study, authored by Krishna Kumar Neelakanta Pillai Santha Kumari Amma and published on arXiv, systematically compares two multi-agent reinforcement learning (MARL) approaches—MAPPO (Multi-Agent Proximal Policy Optimization) and MADDPG (Multi-Agent Deep Deterministic Policy Gradient)—against an Independent DDPG baseline. Using a simulated marketplace environment built from real-world retail data, the researchers measured profit performance, training stability across random seeds, fairness among competing agents, and overall training efficiency.

The results reveal clear advantages for MARL approaches over traditional independent learning methods. MAPPO consistently achieved the highest average returns with remarkably low variance, making it the most stable and reproducible approach for competitive price optimization. Meanwhile, MADDPG delivered slightly lower overall profits but produced the fairest profit distribution among competing agents. This trade-off between maximum profitability and equitable outcomes gives retailers practical options depending on their strategic priorities. The findings demonstrate that coordinated multi-agent systems can effectively navigate complex competitive dynamics where independent agents often struggle with instability.

This research provides empirical evidence that MARL methods offer scalable solutions for real-world pricing challenges. By balancing multiple objectives—profitability, stability, and fairness—these AI systems can adapt to fluctuating demand and competitor behavior more effectively than previous approaches. The study's use of realistic retail data and comprehensive evaluation metrics makes its findings particularly relevant for e-commerce platforms, retail chains, and any business operating in competitive pricing environments where traditional rule-based systems fall short.