We present a framework for autonomously learning a portable representation that describes a collection of low-level continuous environments. We show that these abstract representations can be learned in a task-independent egocentric space specific to the agent that, when grounded with problem-specific information, are provably sufficient for planning. We demonstrate transfer in two different domains, where an agent learns a portable, task-independent symbolic vocabulary, as well as rules expressed in that vocabulary, and then learns to instantiate those rules on a per-task basis. This reduces the number of samples required to learn a representation of a new task.
@article{arxiv.1905.12006,
title = {Learning Portable Representations for High-Level Planning},
author = {Steven James and Benjamin Rosman and George Konidaris},
journal= {arXiv preprint arXiv:1905.12006},
year = {2019}
}