Learning Sparse Relational Transition Models
Abstract
We present a representation for describing transition models in complex uncertain domains using relational rules. For any action, a rule selects a set of relevant objects and computes a distribution over properties of just those objects in the resulting state given their properties in the previous state. An iterative greedy algorithm is used to construct a set of deictic references that determine which objects are relevant in any given state. Feed-forward neural networks are used to learn the transition distribution on the relevant objects' properties. This strategy is demonstrated to be both more versatile and more sample efficient than learning a monolithic transition model in a simulated domain in which a robot pushes stacks of objects on a cluttered table.
Cite
Text
Xia et al. "Learning Sparse Relational Transition Models." International Conference on Learning Representations, 2019.Markdown
[Xia et al. "Learning Sparse Relational Transition Models." International Conference on Learning Representations, 2019.](https://mlanthology.org/iclr/2019/xia2019iclr-learning/)BibTeX
@inproceedings{xia2019iclr-learning,
title = {{Learning Sparse Relational Transition Models}},
author = {Xia, Victoria and Wang, Zi and Allen, Kelsey and Silver, Tom and Kaelbling, Leslie Pack},
booktitle = {International Conference on Learning Representations},
year = {2019},
url = {https://mlanthology.org/iclr/2019/xia2019iclr-learning/}
}