Dynamic Retail Pricing via Q-Learning - A Reinforcement Learning Framework for Enhanced Revenue Management

This study examines how a reinforcement learning (RL) framework, utilizing the Q-Learning algorithm, can improve dynamic pricing techniques in retail. Contrary to conventional pricing methodologies, which often depend on fixed demand assumptions, our RL-based strategy adapts continuously to changing market situations, thereby providing a more adaptable and responsive pricing mechanism. By constructing a simulated retail environment, we show how RL efficiently tackles real-time shifts in consumer behavior and market conditions, resulting in superior revenue performance. Our findings highlight not only the RL model's ability to outperform traditional methods in revenue generation, but also its capacity to reveal intricate relationships between price sensitivity and demand. This research demonstrates the significant promise of artificial intelligence-driven approaches to economic decision-making, and it outlines how more advanced, data-focused pricing strategies can be used across varied commercial fields.