Summary of Solving the Qap by Two-stage Graph Pointer Networks and Reinforcement Learning, By Satoko Iida and Ryota Yasudo
Solving the QAP by Two-Stage Graph Pointer Networks and Reinforcement Learning
by Satoko Iida, Ryota Yasudo
First submitted to arxiv on: 31 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 deep reinforcement learning model, the two-stage graph pointer network (GPN), is designed to solve the Quadratic Assignment Problem (QAP) more efficiently than heuristics. By extending a previously developed GPN for Euclidean Traveling Salesman Problem (TSP) and adding new algorithms for QAP, the authors demonstrate that their approach can provide semi-optimal solutions for benchmark problem instances from TSPlib and QAPLIB. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers tackle the challenging Quadratic Assignment Problem (QAP), which is NP-hard and difficult to solve with traditional methods. They propose a deep reinforcement learning model called two-stage graph pointer network (GPN) that can find good solutions quickly. The GPN is extended from a previous model for Euclidean Traveling Salesman Problem (TSP) and modified to work specifically for QAP. The authors test their approach on benchmark problem instances and show that it provides semi-optimal solutions. |
Keywords
* Artificial intelligence * Reinforcement learning
