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.

从小数据域中学习推导出可适用于远大于其所训练数据范围的领域的普遍性规则，本文研究了深度强化学习和图神经网络在学习这种广义策略的应用，并证明其具有普遍性。

深度强化学习的广义计划

Generalized Planning With Deep Reinforcement Learning

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.

本文介绍使用 Action Schema Networks（ASNets）学习概率和经典计划问题的广义策略，ASNets 是一种神经网络架构，它利用（P）PDDL 计划问题的关系结构来学习一组通用权重，可以应用于该领域内的任何问题，我们扩展了 ASNets 架构以使其更具表现力，同时仍保持不变，还提出了瞬时诱导正则化方法，产生足够紧凑的 ASNets，以便人们可以理解，实验结果表明 ASNets 可以产生通用反应策略，可以更快地解决大型问题实例。