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Control applications often feature tasks with similar, but not identical,dynamics. We introduce the Hidden Parameter Markov Decision Process (HiP-MDP),a framework that parametrizes a family of related dynamical systems with alow-dimensional set of latent factors, and introduce a semiparametricregression approach for learning its structure from data. In the controlsetting, we show that a learned HiP-MDP rapidly identifies the dynamics of anew task instance, allowing an agent to flexibly adapt to task variations.

Hidden Parameter Markov Decision Processes: A Semiparametric Regression Approach for Discovering Latent Task Parametrizations