The development of open benchmarking platforms could greatly accelerate the
adoption of AI agents in retail. This paper presents comprehensive simulations
of customer shopping behaviors for the purpose of benchmarking reinforcement
learning (RL) agents that optimize coupon targeting. The difficulty of this
learning problem is largely driven by the sparsity of customer purchase events.
We trained agents using offline batch data comprising summarized customer
purchase histories to help mitigate this effect. Our experiments revealed that
contextual bandit and deep RL methods that are less prone to over-fitting the
sparse reward distributions significantly outperform static policies. This
study offers a practical framework for simulating AI agents that optimize the
entire retail customer journey. It aims to inspire the further development of
simulation tools for retail AI systems.

本研究介绍了一个开放的基准测试平台，可以加速零售领域中人工智能代理的应用。通过对顾客购物行为进行综合模拟，对优化优惠券目标的强化学习（RL）代理进行了基准测试。该研究通过使用离线批量数据训练代理，以减少由于顾客购买事件稀疏性带来的学习困难。实验结果显示，相对于静态策略，上下文决策和深度强化学习方法在稀疏奖励分布上过拟合的问题上较少，表现出更好的性能。本研究提供了一个实用的框架，用于模拟优化整个零售顾客旅程的人工智能代理，并鼓励进一步发展零售人工智能系统的模拟工具。