Explore the context: optimal data collection for context-conditional dynamics models

  • In this paper, we learn dynamics models for parametrized families of dynamical systems with varying properties. The dynamics models are formulated as stochastic processes conditioned on a latent context variable which is inferred from observed transitions of the respective system. The probabilistic formulation allows us to compute an action sequence which, for a limited number of environment interactions, optimally explores the given system within the parametrized family. This is achieved by steering the system through transitions being most informative for the context variable. We demonstrate the effectiveness of our method for exploration on a non-linear toy-problem and two well-known reinforcement learning environments.

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Author:Jan Achterhold, Jörg StücklerORCiDGND
Frontdoor URLhttps://opus.bibliothek.uni-augsburg.de/opus4/107974
Parent Title (English):Proceedings of The 24th International Conference on Artificial Intelligence and Statistics, 13-15 April 2021, Virtual
Publisher:ML Research Press
Place of publication:Maastricht
Editor:Arindam Banerjee, Kenji Fukumizu
Type:Conference Proceeding
Year of first Publication:2021
Release Date:2023/10/10
First Page:3529
Last Page:3537
Series:Proceedings of Machine Learning Research (PMLR) ; 130
Institutes:Fakultät für Angewandte Informatik
Fakultät für Angewandte Informatik / Institut für Informatik
Fakultät für Angewandte Informatik / Institut für Informatik / Professur für Intelligente Perzeption in technischen Systemen