Learning Portable Representations for High-Level Planning

@InProceedings{pmlr-v119-james20a,
  title = 	 {Learning Portable Representations for High-Level Planning},
  author =       {James, Steven and Rosman, Benjamin and Konidaris, George},
  booktitle = 	 {Proceedings of the 37th International Conference on Machine Learning},
  pages = 	 {4682--4691},
  year = 	 {2020},
  editor = 	 {III, Hal Daumé and Singh, Aarti},
  volume = 	 {119},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {13--18 Jul},
  publisher =    {PMLR},
  pdf = 	 {http://proceedings.mlr.press/v119/james20a/james20a.pdf},
  url = 	 {https://proceedings.mlr.press/v119/james20a.html},
  abstract = 	 {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 operators expressed in that vocabulary, and then learns to instantiate those operators on a per-task basis. This reduces the number of samples required to learn a representation of a new task.}
}