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.

翻译：本文探讨了利用Q-Learning算法的强化学习框架在零售领域增强动态定价策略的应用。与通常依赖静态需求模型的传统定价方法不同，我们的强化学习方法能够持续适应不断变化的市场动态，提供更灵活、响应更快的定价策略。通过构建一个模拟零售环境，我们展示了强化学习如何有效应对消费者行为和市场条件的实时变化，从而改善收益结果。我们的结果表明，该强化学习模型不仅在收益生成方面超越了传统方法，而且为价格弹性与消费者需求之间复杂的相互作用提供了深刻见解。这项研究凸显了人工智能在经济决策中应用的巨大潜力，为在各个商业领域开发更复杂、数据驱动的定价模型铺平了道路。