Learning Generalized Policies for Fully Observable Non-Deterministic Planning Domains

arXiv:2404.02499v1 Announce Type: cross
Abstract: General policies represent reactive strategies for solving large families of planning problems like the infinite collection of solvable instances from a given domain. Methods for learning such policies from a collection of small training instances have been developed successfully for classical domains. In this work, we extend the formulations and the resulting combinatorial methods for learning general policies over fully observable, non-deterministic (FOND) domains. We also evaluate the resulting approach experimentally over a number of …

abstract arxiv collection cs.ai cs.lg domain domains families general generalized instances observable planning policies small strategies training type work