Hidden parameter markov decision processes: A semiparametric regression approach for discovering latent task parametrizations


Conference Paper

Control applications often feature tasks with similar, but not identical, dynamics. We introduce the Hidden Parameter Markov Decision Process (HiPMDP), 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. We show that a learned HiP-MDP rapidly identifies the dynamics of new task instances in several settings, flexibly adapting to task variation.

Duke Authors

Cited Authors

  • Doshi-Velez, F; Konidaris, G

Published Date

  • January 1, 2016

Published In

Volume / Issue

  • 2016-January /

Start / End Page

  • 1432 - 1440

International Standard Serial Number (ISSN)

  • 1045-0823

Citation Source

  • Scopus