Academic

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

arXiv:2603.16888v1 Announce Type: new Abstract: Dynamic pricing in competitive retail markets requires strategies that adapt to fluctuating demand and competitor behavior. In this work, we present a systematic empirical evaluation of multi-agent reinforcement learning (MARL) approaches-specifically MAPPO and MADDPG-for dynamic price optimization under competition. Using a simulated marketplace environment derived from real-world retail data, we benchmark these algorithms against an Independent DDPG (IDDPG) baseline, a widely used independent learner in MARL literature. We evaluate profit performance, stability across random seeds, fairness, and training efficiency. Our results show that MAPPO consistently achieves the highest average returns with low variance, offering a stable and reproducible approach for competitive price optimization, while MADDPG achieves slightly lower profit but the fairest profit distribution among agents. These findings demonstrate that MARL methods-particula

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Krishna Kumar Neelakanta Pillai Santha Kumari Amma · · 1 min read · 35 views
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arXiv:2603.16888v1 Announce Type: new Abstract: Dynamic pricing in competitive retail markets requires strategies that adapt to fluctuating demand and competitor behavior. In this work, we present a systematic empirical evaluation of multi-agent reinforcement learning (MARL) approaches-specifically MAPPO and MADDPG-for dynamic price optimization under competition. Using a simulated marketplace environment derived from real-world retail data, we benchmark these algorithms against an Independent DDPG (IDDPG) baseline, a widely used independent learner in MARL literature. We evaluate profit performance, stability across random seeds, fairness, and training efficiency. Our results show that MAPPO consistently achieves the highest average returns with low variance, offering a stable and reproducible approach for competitive price optimization, while MADDPG achieves slightly lower profit but the fairest profit distribution among agents. These findings demonstrate that MARL methods-particularly MAPPO-provide a scalable and stable alternative to independent learning approaches for dynamic retail pricing.

Executive Summary

This article presents a systematic empirical evaluation of multi-agent reinforcement learning (MARL) approaches for dynamic price optimization in competitive retail markets. The authors benchmark MAPPO and MADDPG against an Independent DDPG baseline, evaluating profit performance, stability, fairness, and training efficiency using a simulated marketplace environment derived from real-world retail data. The results show that MAPPO achieves the highest average returns with low variance, offering a stable and reproducible approach for competitive price optimization, while MADDPG achieves slightly lower profit but the fairest profit distribution among agents. The findings demonstrate that MARL methods provide a scalable and stable alternative to independent learning approaches for dynamic retail pricing.

Key Points

  • ▸ The article evaluates the performance of MAPPO and MADDPG in dynamic price optimization under competition.
  • ▸ The results show that MAPPO achieves the highest average returns with low variance and offers a stable and reproducible approach.
  • ▸ MADDPG achieves slightly lower profit but the fairest profit distribution among agents.
  • ▸ The findings demonstrate the scalability and stability of MARL methods in dynamic retail pricing.

Merits

Scalability and Stability

The study demonstrates the scalability and stability of MARL methods in dynamic retail pricing, making them a viable alternative to independent learning approaches.

Empirical Evaluation

The article presents a comprehensive empirical evaluation of MARL approaches, providing valuable insights into their performance in competitive retail markets.

Fairness and Profitability

The findings show that MADDPG achieves a fairer profit distribution among agents while MAPPO achieves higher average returns, highlighting the trade-offs between fairness and profitability.

Demerits

Limited Generalizability

The study is limited to a simulated marketplace environment, and its findings may not generalize to real-world retail markets with different characteristics and dynamics.

Assumptions and Simplifications

The article assumes a simplified competitive retail market with fixed demand and competitor behavior, which may not reflect the complexities of real-world markets.

Lack of Real-World Data

The study uses simulated data derived from real-world retail data, and its findings may not be representative of real-world market dynamics.

Expert Commentary

The article presents a comprehensive and systematic evaluation of MARL approaches for dynamic price optimization in competitive retail markets. The findings demonstrate the scalability and stability of MARL methods, making them a viable alternative to independent learning approaches. However, the study is limited by its assumptions and simplifications, and its findings may not generalize to real-world retail markets. Furthermore, the study's use of simulated data derived from real-world retail data may not be representative of real-world market dynamics. Nevertheless, the study's findings have important implications for the development of dynamic pricing strategies in competitive retail markets and highlight the potential of AI and MARL methods to improve the efficiency and effectiveness of retail pricing strategies.

Recommendations

  • ✓ Future studies should aim to replicate the study's findings using real-world data and test the generalizability of MARL methods to different retail market characteristics and dynamics.
  • ✓ Researchers should explore the potential applications of MARL methods in other domains, such as supply chain management and inventory control.

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