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Several goal-oriented problems in the real-world can be naturally expressedas Stochastic Shortest Path Problems (SSPs). However, the computationalcomplexity of solving SSPs makes finding solutions to even moderately sizedproblems intractable. Currently, existing state-of-the-art planners andheuristics often fail to exploit knowledge learned from solving otherinstances. This paper presents an approach for learning \emph{GeneralizedPolicy Automata} (GPA): non-deterministic partial policies that can be used tocatalyze the solution process. GPAs are learned using relational, feature-basedabstractions, which makes them applicable on broad classes of related problemswith different object names and quantities. Theoretical analysis of thisapproach shows that it guarantees completeness and hierarchical optimality.Empirical analysis shows that this approach effectively learns broadlyapplicable policy knowledge in a few-shot fashion and significantly outperformsstate-of-the-art SSP solvers on test problems whose object counts are fargreater than those used during training.

Learning Generalized Policy Automata for Relational Stochastic Shortest Path Problems