This paper explores the application of a reinforcement learning (RL) framework using the Q-Learning algorithm to enhance dynamic pricing strategies in the retail sector. Unlike traditional pricing methods, which often rely on static demand models, our RL approach continuously adapts to evolving market dynamics, offering a more flexible and responsive pricing strategy. By creating a simulated retail environment, we demonstrate how RL effectively addresses real-time changes in consumer behavior and market conditions, leading to improved revenue outcomes. Our results illustrate that the RL model not only surpasses traditional methods in terms of revenue generation but also provides insights into the complex interplay of price elasticity and consumer demand. This research underlines the significant potential of applying artificial intelligence in economic decision-making, paving the way for more sophisticated, data-driven pricing models in various commercial domains.

本研究针对传统定价方法在适应市场变化方面的不足，提出了一种基于Q-Learning的强化学习框架用于动态定价策略的优化。通过模拟零售环境，研究表明该RL模型在实时应对消费者行为和市场条件变化方面显著优于传统方法，从而提高了收益并提供了价格弹性与消费者需求之间复杂关系的洞见。此研究突显了人工智能在经济决策中应用的巨大潜力，促进了更复杂、数据驱动的定价模型的发展。

基于Q-Learning的动态零售定价——增强收益管理的强化学习框架