تقرير
Predicate Invention for Bilevel Planning
| العنوان: | Predicate Invention for Bilevel Planning |
|---|---|
| المؤلفون: | Silver, Tom, Chitnis, Rohan, Kumar, Nishanth, McClinton, Willie, Lozano-Perez, Tomas, Kaelbling, Leslie Pack, Tenenbaum, Joshua |
| سنة النشر: | 2022 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Computer Science - Robotics |
| الوصف: | Efficient planning in continuous state and action spaces is fundamentally hard, even when the transition model is deterministic and known. One way to alleviate this challenge is to perform bilevel planning with abstractions, where a high-level search for abstract plans is used to guide planning in the original transition space. Previous work has shown that when state abstractions in the form of symbolic predicates are hand-designed, operators and samplers for bilevel planning can be learned from demonstrations. In this work, we propose an algorithm for learning predicates from demonstrations, eliminating the need for manually specified state abstractions. Our key idea is to learn predicates by optimizing a surrogate objective that is tractable but faithful to our real efficient-planning objective. We use this surrogate objective in a hill-climbing search over predicate sets drawn from a grammar. Experimentally, we show across four robotic planning environments that our learned abstractions are able to quickly solve held-out tasks, outperforming six baselines. Code: https://tinyurl.com/predicators-release Comment: AAAI 2023. Short version appeared at RLDM 2022 |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2203.09634 |
| رقم الأكسشن: | edsarx.2203.09634 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |