Summary of Self-improvement For Neural Combinatorial Optimization: Sample Without Replacement, but Improvement, by Jonathan Pirnay and Dominik G. Grimm
Self-Improvement for Neural Combinatorial Optimization: Sample without Replacement, but Improvement
by Jonathan Pirnay, Dominik G. Grimm
First submitted to arxiv on: 22 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 approach simplifies the training process for end-to-end constructive neural combinatorial optimization by sampling multiple solutions from the current model in each epoch and selecting the best solution as an expert trajectory for supervised imitation learning. This method combines round-wise Stochastic Beam Search with a policy improvement strategy that refines the policy between rounds, achieving progressively improving solutions with minimal computational overhead. The approach is evaluated on several problems, including the Traveling Salesman Problem, Capacitated Vehicle Routing Problem, and Job Shop Scheduling Problem, outperforming existing state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers has found a way to make it easier to train machines that can solve complex problems like planning routes for delivery trucks or scheduling jobs in a factory. Usually, these machines are trained by trying many different solutions and seeing which one works best. This new approach makes the training process faster and more efficient by using the machine’s own predictions as a guide. The method was tested on several problems and performed well, even outdoing some of the current best methods. |
Keywords
* Artificial intelligence * Optimization * Supervised
