Summary of Imtsp: Solving Min-max Multiple Traveling Salesman Problem with Imperative Learning, by Yifan Guo et al.
iMTSP: Solving Min-Max Multiple Traveling Salesman Problem with Imperative Learning
by Yifan Guo, Zhongqiang Ren, Chen Wang
First submitted to arxiv on: 1 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG); Robotics (cs.RO)
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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 paper presents a novel approach to solving the Min-Max Multiple Traveling Salesman Problem (MTSP), which involves finding tours for each agent to collectively visit all cities while minimizing the length of the longest tour. The authors address challenges in existing data-driven methods by reformulating MTSP as a bilevel optimization problem using imperative learning (IL). This involves introducing an allocation network that decomposes the MTSP into multiple single-agent traveling salesman problems (TSPs), which are then self-supervised to learn optimal tours. The authors also introduce a control variate-based gradient estimation algorithm to tackle high-variance gradient issues during optimization. Experiments show that these designs enable faster convergence and shorter tour lengths compared to existing methods, including the Google OR-Tools MTSP solver. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about solving a big problem called Min-Max Multiple Traveling Salesman Problem (MTSP). This problem involves finding the best way for multiple agents to visit all cities. The authors use a new approach to solve this problem, which involves breaking it down into smaller problems and then combining the solutions. They also develop a new way to estimate gradients, which helps the algorithm converge faster and find better solutions. The results show that their method can find much shorter tour lengths than existing methods. |
Keywords
» Artificial intelligence » Optimization » Self supervised
