Summary of Towards Learning Foundation Models For Heuristic Functions to Solve Pathfinding Problems, by Vedant Khandelwal et al.
Towards Learning Foundation Models for Heuristic Functions to Solve Pathfinding Problems
by Vedant Khandelwal, Amit Sheth, Forest Agostinelli
First submitted to arxiv on: 1 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 This paper introduces a novel foundation model that leverages deep reinforcement learning to train heuristic functions, enabling seamless adaptation to new problem domains without additional fine-tuning. Building upon the DeepCubeA model, the authors enhance the heuristic function by providing domain-specific state transition information, improving its adaptability. The study demonstrates the model’s ability to generalize and solve unseen domains using a puzzle generator for 15-puzzle action space variation domains. Evaluations show a strong correlation between learned and ground truth heuristic values across various domains, as measured by robust R-squared and Concordance Correlation Coefficient metrics. This foundation model has the potential to establish new standards in efficiency and adaptability for AI-driven solutions in complex pathfinding problems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a super-smart computer that can learn to solve puzzles and navigate paths without needing to be taught again for each new puzzle or problem. That’s what this research paper is about! The scientists created a special kind of model that uses deep learning to teach the computer how to adapt to new situations quickly and efficiently. They tested it on different types of puzzles and found that it could solve them all with ease, even when the puzzles were completely new. This breakthrough has big implications for things like robotics, artificial intelligence, and more. |
Keywords
» Artificial intelligence » Deep learning » Fine tuning » Reinforcement learning
