Summary of Reinforcement Learning with Intrinsically Motivated Feedback Graph For Lost-sales Inventory Control, by Zifan Liu et al.
Reinforcement Learning with Intrinsically Motivated Feedback Graph for Lost-sales Inventory Control
by Zifan Liu, Xinran Li, Shibo Chen, Gen Li, Jiashuo Jiang, Jun Zhang
First submitted to arxiv on: 26 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 The proposed decision framework combines reinforcement learning with feedback graph and intrinsically motivated exploration to boost sample efficiency in inventory control. The limitations of online experience, including high costs and potential for lost sales, are addressed by designing a feedback graph specifically for lost-sales IC problems to generate abundant side experiences aiding RL updates. Theoretical analysis demonstrates how the designed FG reduces the sample complexity of RL methods, enabling the design of an intrinsic reward directing the RL agent to explore the state-action space with more side experiences. Experimental results show that the method improves the sample efficiency of applying RL in IC. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Reinforcement learning is used for inventory control, but there are challenges. Online experience is expensive and might not reflect real demand. To fix this, a new decision framework combines RL with feedback graph and intrinsically motivated exploration. This helps by creating extra experiences to learn from. The new method works better than before and can be used in real-world situations. |
Keywords
» Artificial intelligence » Reinforcement learning
