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In many settings (e.g., robotics) demonstrations provide a natural way tospecify tasks; however, most methods for learning from demonstrations either donot provide guarantees that the artifacts learned for the tasks, such asrewards or policies, can be safely composed and/or do not explicitly capturehistory dependencies. Motivated by this deficit, recent works have proposedlearning Boolean task specifications, a class of Boolean non-Markovian rewardswhich admit well-defined composition and explicitly handle historicaldependencies. This work continues this line of research by adapting maximumcausal entropy inverse reinforcement learning to estimate the posterioriprobability of a specification given a multi-set of demonstrations. The keyalgorithmic insight is to leverage the extensive literature and tooling onreduced ordered binary decision diagrams to efficiently encode a time unrolledMarkov Decision Process. This enables transforming a naive exponential timealgorithm into a polynomial time algorithm.

Maximum Causal Entropy Specification Inference from Demonstrations