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Reinforcement learning in problems with symbolic state spaces is challengingdue to the need for reasoning over long horizons. This paper presents a newapproach that utilizes relational abstractions in conjunction with deeplearning to learn a generalizable Q-function for such problems. The learnedQ-function can be efficiently transferred to related problems that havedifferent object names and object quantities, and thus, entirely differentstate spaces. We show that the learned generalized Q-function can be utilizedfor zero-shot transfer to related problems without an explicit, hand-codedcurriculum. Empirical evaluations on a range of problems show that our methodfacilitates efficient zero-shot transfer of learned knowledge to much largerproblem instances containing many objects.

Relational Abstractions for Generalized Reinforcement Learning on Symbolic Problems