Summary of Attention-based Reinforcement Learning For Combinatorial Optimization: Application to Job Shop Scheduling Problem, by Jaejin Lee et al.
Attention-based Reinforcement Learning for Combinatorial Optimization: Application to Job Shop Scheduling Problem
by Jaejin Lee, Seho Kee, Mani Janakiram, George Runger
First submitted to arxiv on: 29 Jan 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 attention-based reinforcement learning method addresses job shop scheduling problems by integrating policy gradient reinforcement learning with a modified transformer architecture. The approach demonstrates its ability to be repurposed for larger-scale problems and outperforms recent studies and heuristic rules. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Job shop scheduling is a complex problem that requires finding the best way to schedule jobs in a factory. Traditional methods can take a long time or don’t work well with new problems. Researchers developed a new approach using machine learning, which allowed them to train models on some job shop scheduling problems and then use those same models on bigger problems they hadn’t seen before. This method is better than previous approaches and could be used in the future to help factories schedule jobs more efficiently. |
Keywords
* Artificial intelligence * Attention * Machine learning * Reinforcement learning * Transformer
