Summary of Dynamic Retail Pricing Via Q-learning — a Reinforcement Learning Framework For Enhanced Revenue Management, by Mohit Apte et al.
Dynamic Retail Pricing via Q-Learning – A Reinforcement Learning Framework for Enhanced Revenue Management
by Mohit Apte, Ketan Kale, Pranav Datar, Pratiksha Deshmukh
First submitted to arxiv on: 27 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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Summary difficulty | Written by | Summary |
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High | Paper authors | High Difficulty Summary Read the original abstract here |
Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper develops a reinforcement learning (RL) framework using Q-Learning to enhance dynamic pricing strategies in retail. The RL approach continuously adapts to evolving market dynamics, offering a more flexible and responsive pricing strategy compared to traditional methods relying on static demand models. By simulating a retail environment, the authors demonstrate how RL effectively addresses real-time changes in consumer behavior and market conditions, leading to improved revenue outcomes. The results show that the RL model surpasses traditional methods in terms of revenue generation while providing insights into the complex interplay of price elasticity and consumer demand. |
Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses a special kind of computer learning called reinforcement learning to help stores set better prices. Normally, stores use old-fashioned ways to figure out how much to charge for things based on what people might buy in the future. But this new way of setting prices can change its strategy based on what’s happening right now. The researchers tested this idea by creating a fake store and seeing how well it did compared to the usual way of doing things. They found that the new method worked better and even helped them understand why people made certain choices about what they wanted to buy. |
Keywords
* Artificial intelligence * Reinforcement learning