  A hallmark of intelligence is the ability to deduce general principles from
examples, which are correct beyond the range of those observed. Generalized
Planning deals with finding such principles for a class of planning problems,
so that principles discovered using small instances of a domain can be used to
solve much larger instances of the same domain. In this work we study the use
of Deep Reinforcement Learning and Graph Neural Networks to learn such
generalized policies and demonstrate that they can generalize to instances that
are orders of magnitude larger than those they were trained on.
