Summary of Dual-agent Deep Reinforcement Learning For Dynamic Pricing and Replenishment, by Yi Zheng et al.
Dual-Agent Deep Reinforcement Learning for Dynamic Pricing and Replenishment
by Yi Zheng, Zehao Li, Peng Jiang, Yijie Peng
First submitted to arxiv on: 28 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: General Economics (econ.GN)
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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 In this paper, researchers tackle the complex problem of dynamic pricing and replenishment under inconsistent decision frequencies. By incorporating a machine learning approach trained on market data and a two-timescale stochastic approximation scheme, they address the discrepancies in decision frequencies between pricing and replenishment. The authors further refine their methodology by incorporating deep reinforcement learning techniques, proposing a fast-slow dual-agent DRL algorithm that handles pricing and inventory updates on different scales. Numerical results from both single and multiple products scenarios validate the effectiveness of their methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how to make smart decisions when it comes to pricing and stocking products in stores. The problem is tricky because the number of people who want something changes depending on how much you charge for it, and you need to balance that with how many items you have in stock. The researchers use special computer programs to help figure out the best prices and inventory levels to keep customers happy while also making a profit. They test their ideas using real market data and find that they work well. |
Keywords
* Artificial intelligence * Machine learning * Reinforcement learning
