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Humans can learn many novel tasks from a very small number (1--5) ofdemonstrations, in stark contrast to the data requirements of nearly tabularasa deep learning methods. We propose an expressive class of policies, astrong but general prior, and a learning algorithm that, together, can learninteresting policies from very few examples. We represent policies as logicalcombinations of programs drawn from a domain-specific language (DSL), define aprior over policies with a probabilistic grammar, and derive an approximateBayesian inference algorithm to learn policies from demonstrations. Inexperiments, we study five strategy games played on a 2D grid with one sharedDSL. After a few demonstrations of each game, the inferred policies generalizeto new game instances that differ substantially from the demonstrations. Ourpolicy learning is 20--1,000x more data efficient than convolutional and fullyconvolutional policy learning and many orders of magnitude more computationallyefficient than vanilla program induction. We argue that the proposed method isan apt choice for tasks that have scarce training data and feature significant,structured variation between task instances.

Few-Shot Bayesian Imitation Learning with Logical Program Policies