用强化学习动态调价，比传统方法更会赚钱。
Dynamic Retail Pricing via Q-Learning -- A Reinforcement Learning Framework for Enhanced Revenue Management
- 用Q-learning算法实时学习最优定价策略。
- 在模拟环境中收入提升，且能捕捉价格弹性变化。
- 适合做智能定价、零售优化的从业者参考。
本文探讨了基于Q-Learning算法的强化学习框架在零售动态定价中的应用。与依赖静态需求模型的传统方法不同，该框架能持续适应市场动态变化，提供更灵活的定价策略。通过构建模拟零售环境，我们验证了强化学习能够有效应对消费者行为和市场条件的实时变化，显著提升收益。结果表明，该模型不仅在收入表现上优于传统方法，还能揭示价格弹性与消费者需求之间的复杂关系。研究证明人工智能在经济决策中的巨大潜力，为多个商业领域的数据驱动定价模型发展铺平道路。
原文摘要 · Abstract (English)
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.
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