Summary of Invit: a Generalizable Routing Problem Solver with Invariant Nested View Transformer, by Han Fang et al.
INViT: A Generalizable Routing Problem Solver with Invariant Nested View Transformer
by Han Fang, Zhihao Song, Paul Weng, Yutong Ban
First submitted to arxiv on: 4 Feb 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 In this paper, researchers propose a novel architecture called Invariant Nested View Transformer (INViT) to address the issue of deep reinforcement learning models not generalizing well to unseen distributions or different problem scales. The INViT architecture is designed to enforce a nested design and invariant views inside the encoders to promote the generalizability of learned solvers. The model uses a modified policy gradient algorithm enhanced with data augmentations, and it achieves dominant performance on both TSP and CVRP problems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us learn faster heuristics for solving routing problems like traveling salesman problems (TSP) and vehicle routing problems (CVRP). Right now, most of these solvers don’t work well when they see new or bigger problem sizes. To fix this, the researchers created a special kind of transformer called INViT. It helps the model learn in a way that makes it good at solving many different kinds of problems. |
Keywords
* Artificial intelligence * Reinforcement learning * Transformer
