  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.
