Sep 11, 2020 · Title:Planning with Learned Object Importance in Large Problem Instances using Graph Neural Networks ; Subjects: Machine Learning (cs.LG); ...
Our work uses graph neural networks (GNNs) (Scarselli et al. 2008; Kipf and Welling 2016; Battaglia et al. 2018), an in- creasingly popular choice for ...
This work proposes a graph neural network architecture for predicting object importance in a single inference pass, thus incurring little overhead while ...
This repository houses code for the AAAI 2021 paper: Planning with Learned Object Importance in Large Problem Instances using Graph Neural Networks.
Sep 11, 2020 · In this work, we learn to predict a small subset of objects that is sufficient for planning, leading to significantly faster planning than both ...
Duration: 4:47
Posted: Sep 11, 2020
Posted: Sep 11, 2020
@InProceedings{LIS287, title = {Planning with Learned Object Importance in Large Problem Instances using Graph Neural Networks}, author = {Tom Silver and ...
@inproceedings{silver2020planning, title={Planning with Learned Object Importance in Large Problem Instances using Graph Neural Networks}, author={Tom ...
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Planning with learned object importance in large problem instances using graph neural networks. arXiv preprint arXiv:2009.05613. Ståhlberg, S.; Bonet, B ...
We conclude that learning to predict a sufficient set of objects for a planning problem is a simple, powerful, and general mechanism for planning in large ...