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technologyreview.com·2h ago
AI Research
arXiv CS.AI·May 26·5 min read
Learning to Search and Searching to Learn for Generalization in Planning
TL;DR
Combinatorial generalization in Deep Reinforcement Learning (DRL) is challenging, particularly in sparse-reward environments. A self-improving WA* learning framework was developed, utilizing a Relational Graph Neural Network to guide search and update heuristics through Q-learning.
✦ Why It Matters
Engineers can leverage this framework to enhance planning algorithms in complex environments without extensive retraining.
Key Takeaways
- Explore using WA* for improved planning efficiency.
- Utilize Relational Graph Neural Networks for heuristic development.
- Implement Q-learning to enhance heuristic adaptability.
- Achieve zero-shot generalization in complex planning tasks.
Full Summary
Combinatorial generalization is a significant hurdle in Deep Reinforcement Learning (DRL), especially in environments with sparse rewards where exploration is ineffective. Classical planning methods, like best-first search, provide a structured way to tackle this issue.
The authors introduced a WA* learning framework that integrates a value heuristic represented by a Relational Graph Neural Network. This heuristic not only guides the search process but also updates itself through Q-learning based on the search data generated.
The results showed that heuristics trained on simpler instances could solve much larger problems, achieving zero-shot generalization. For instance, heuristics trained on Blocksworld with fewer than 30 blocks successfully tackled instances with 488 blocks.
This advancement suggests that planners can effectively generalize to new problems without extensive retraining.