@article {DBLP:journals/corr/Doshi-VelezK13,
	title = {Hidden Parameter Markov Decision Processes: A Semiparametric Regression Approach for Discovering Latent Task Parametrizations},
	journal = {CoRR},
	volume = {abs/1308.3513},
	year = {2013},
	abstract = {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 a low-dimensional set of latent factors, and introduce a semiparametric regression approach for learning its structure from data. In the control setting, we show that a learned HiP-MDP rapidly identifies the dynamics of a new task instance, allowing an agent to flexibly adapt to task variations.},
	author = {Finale Doshi-Velez and George Konidaris}
}
