TL;DR
This paper introduces ASNets, a neural network architecture that learns generalized policies for probabilistic and classical planning problems by exploiting relational structures, enabling quick solutions to larger instances across various domains.
Contribution
The work extends ASNets to be more expressive and invariant to symmetries, demonstrating their effectiveness through extensive experiments and interpretability analysis.
Findings
ASNets outperform heuristic planners on multiple domains
ASNets generalize well to large problem instances
Regularization yields interpretable, compact ASNets
Abstract
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,…
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