Summary of Simulation-based Benchmarking Of Reinforcement Learning Agents For Personalized Retail Promotions, by Yu Xia et al.
Simulation-Based Benchmarking of Reinforcement Learning Agents for Personalized Retail Promotions
by Yu Xia, Sriram Narayanamoorthy, Zhengyuan Zhou, Joshua Mabry
First submitted to arxiv on: 16 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG); Econometrics (econ.EM); Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a comprehensive simulation platform for benchmarking reinforcement learning (RL) agents that optimize coupon targeting in retail. By leveraging offline batch data comprising summarized customer purchase histories, the study trains RL agents to mitigate the effect of sparse customer purchase events. The results show that contextual bandit and deep RL methods are less prone to over-fitting and significantly outperform static policies. This study offers a practical framework for simulating AI agents that optimize the entire retail customer journey. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps create a new way to test how well artificial intelligence (AI) agents can help stores sell more products by giving them coupons. The authors use computer simulations to see how well different AI approaches work in this task. They found that some types of AI are better at making good decisions than others, especially when there isn’t much information available. This research is important because it helps us develop tools for testing and improving retail AI systems. |
Keywords
» Artificial intelligence » Reinforcement learning
