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In many cases an intelligent agent may want to learn how to mimic a singleobserved demonstrated trajectory. In this work we consider how to perform suchprocedural learning from observation, which could help to enable agents tobetter use the enormous set of video data on observation sequences. Ourapproach exploits the properties of this setting to incrementally build an openloop action plan that can yield the desired subsequence, and can be used inboth Markov and partially observable Markov domains. In addition, procedurescommonly involve repeated extended temporal action subsequences. Our methodoptimistically explores actions to leverage potential repeated structure in theprocedure. In comparing to some state-of-the-art approaches we find that ourexplicit procedural learning from observation method is about 100 times fasterthan policy-gradient based approaches that learn a stochastic policy and isfaster than model based approaches as well. We also find that performingoptimistic action selection yields substantial speed ups when latent dynamicalstructure is present.

PLOTS: Procedure Learning from Observations using Subtask Structure