Multi-Agent Deep Reinforcement Learning for Integrated Retail Operations Optimization

Research at the University of Southern California has introduced a novel multi-agent deep reinforcement learning framework that jointly optimizes demand forecasting and inventory management in retail supply chains. The framework leverages data from IoT sensors, RFID tracking systems, and smart shelf monitoring devices to improve inventory decisions. The approach combines transformer-based sequence modeling for demand patterns with hierarchical reinforcement learning agents that coordinate inventory decisions across distribution networks. The results show significant improvements in handling promotional events and seasonal transitions, with 18.2% lower forecast error and 23.5% reduced stockout rates compared to state-of-the-art baselines.

Key Takeaways:

• The retail industry faces challenges in matching supply with demand due to evolving consumer behaviors, market volatility, and supply chain disruptions.
• Existing demand forecasting approaches often fail to capture complex temporal dependencies and lack the ability to simultaneously optimize inventory decisions.
• The proposed multi-agent deep reinforcement learning framework integrates historical sales data and real-time sensor measurements to optimize demand forecasting and inventory management.
• The framework combines transformer-based sequence modeling for demand patterns with hierarchical reinforcement learning agents that coordinate inventory decisions.
• The approach achieves 18.2% lower forecast error and 23.5% reduced stockout rates compared to state-of-the-art baselines.
• The results show particular improvements in handling promotional events and seasonal transitions.
• The research provides new insights into leveraging deep reinforcement learning for integrated retail operations optimization.
• The proposed solution is scalable for modern sensor-enabled supply chain challenges.

Statistics:

• 18.2% lower forecast error achieved by the proposed framework compared to state-of-the-art baselines.
• 23.5% reduced stockout rates achieved by the proposed framework compared to state-of-the-art baselines.
• 25(8):2428 - The publication number of the research article in the journal Sensors.
• 2025 - The year in which the research is reported.

Sources:

• Multi-Agent Deep Reinforcement Learning for Integrated Demand Forecasting and Inventory Optimization in Sensor-Enabled Retail Supply Chains. Sensors, 2025,25(8):2428.
• Journal of Engineering. May 12, 2025; p 3052.