Integrating Reinforcement Learning and Game Theory for Enhancing Supply Chain Optimization

Abstract

Supply chain optimization remains a complex and critical challenge due to the dynamic interactions among multiple stakeholders, evolving market demands, and uncertainties in supply and demand. Traditional optimization approaches often struggle to capture these complexities, especially in multi-agent settings. This paper presents an integrated framework that combines Reinforcement Learning (RL) and Game Theory (GT) to address supply chain optimization in a competitive, multi-agent environment. Reinforcement Learning, known for its ability to adapt and make sequential decisions, provides a dynamic approach to agent behavior, while Game Theory models the strategic interactions between competing and cooperating supply chain entities. We implement Multi-Agent Reinforcement Learning (MARL) with game-theoretic constructs to allow each agent (supplier, manufacturer, distributor, retailer) to learn and adapt strategies that are responsive to the behaviors of other agents. Through a series of experiments on simulated supply chain scenarios, we demonstrate that this combined RL-GT framework achieves significant improvements in inventory management, demand forecasting, and logistics coordination compared to standalone approaches. The experimental results reveal the framework’s capability to reduce costs, enhance lead times, and improve service levels. This study highlights the potential of integrating machine learning and economic theory to create resilient, adaptable, and strategically optimized supply chains.