ASNets: Deep Learning for Generalised Planning
  In this paper, we discuss the learning of generalised policies for
probabilistic and classical planning problems using Action Schema Networks
(ASNets). The ASNet is a neural network architecture that exploits the
relational structure of (P)PDDL planning problems to learn a common set of
weights that can be applied to any problem in a domain. By mimicking the
actions chosen by a traditional, non-learning planner on a handful of small
problems in a domain, ASNets are able to learn a generalised reactive policy
that can quickly solve much larger instances from the domain. This work extends
the ASNet architecture to make it more expressive, while still remaining
invariant to a range of symmetries that exist in PPDDL problems. We also
present a thorough experimental evaluation of ASNets, including a comparison
with heuristic search planners on seven probabilistic and deterministic
domains, an extended evaluation on over 18,000 Blocksworld instances, and an
ablation study. Finally, we show that sparsity-inducing regularisation can
produce ASNets that are compact enough for humans to understand, yielding
insights into how the structure of ASNets allows them to generalise across a
domain.
