Summary of Gabar: Graph Attention-based Action Ranking For Relational Policy Learning, by Rajesh Mangannavar et al.
GABAR: Graph Attention-Based Action Ranking for Relational Policy Learning
by Rajesh Mangannavar, Stefan Lee, Alan Fern, Prasad Tadepalli
First submitted to arxiv on: 6 Dec 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 learns relational policies for classical planning by learning to rank actions. A novel graph representation is introduced, capturing action information and a Graph Neural Network architecture with Gated Recurrent Units (GRUs) is designed to learn action rankings. The model is trained on small problem instances and generalizes to larger instances where traditional planning becomes computationally expensive. Experimental results across standard planning benchmarks show that the approach achieves generalization to significantly larger problems than those used in training. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers plan better by teaching them to rank actions. It creates a special kind of graph to represent action information and uses a type of neural network to learn how to rank actions. The model is trained on small planning problems and can then solve bigger ones that would normally take too long. This means the approach could be used in many real-world applications where planning is important. |
Keywords
» Artificial intelligence » Generalization » Graph neural network » Neural network
