Latent Kullback Leibler Control for Continuous-State Systems using Probabilistic Graphical Models

Takamitsu Matsubara NAIST, Vicenç Gómez Radboud University Nijmegen, Hilbert Kappen Radboud University
Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence, PMLR R12:371-380, 2014.

Abstract

Kullback Leibler (KL) control problems al- low for efficient computation of optimal con- trol by solving a principal eigenvector prob- lem. However, direct applicability of such framework to continuous state-action sys- tems is limited. In this paper, we propose to embed a KL control problem in a proba- bilistic graphical model where observed vari- ables correspond to the continuous (possi- bly high-dimensional) state of the system and latent variables correspond to a dis- crete (low-dimensional) representation of the state amenable for KL control computation. We present two examples of this approach. The first one uses standard hidden Markov models (HMMs) and computes exact opti- mal control, but is only applicable to low- dimensional systems. The second one uses factorial HMMs, it is scalable to higher di- mensional problems, but control computa- tion is approximate. We illustrate both ex- amples in several robot motor control tasks.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR12-naist14a, title = {Latent {K}ullback {L}eibler Control for Continuous-State Systems using Probabilistic Graphical Models}, author = {NAIST, Takamitsu Matsubara and Nijmegen, Vicen{\c{c}} G{\'o}mez Radboud University and University, Hilbert Kappen Radboud}, booktitle = {Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence}, pages = {371--380}, year = {2014}, editor = {Zhang, Nevin L. and Tian, Jin}, volume = {R12}, series = {Proceedings of Machine Learning Research}, month = {23--27 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r12/main/assets/naist14a/naist14a.pdf}, url = {https://proceedings.mlr.press/r12/naist14a.html}, abstract = {Kullback Leibler (KL) control problems al- low for efficient computation of optimal con- trol by solving a principal eigenvector prob- lem. However, direct applicability of such framework to continuous state-action sys- tems is limited. In this paper, we propose to embed a KL control problem in a proba- bilistic graphical model where observed vari- ables correspond to the continuous (possi- bly high-dimensional) state of the system and latent variables correspond to a dis- crete (low-dimensional) representation of the state amenable for KL control computation. We present two examples of this approach. The first one uses standard hidden Markov models (HMMs) and computes exact opti- mal control, but is only applicable to low- dimensional systems. The second one uses factorial HMMs, it is scalable to higher di- mensional problems, but control computa- tion is approximate. We illustrate both ex- amples in several robot motor control tasks.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Latent Kullback Leibler Control for Continuous-State Systems using Probabilistic Graphical Models %A Takamitsu Matsubara NAIST %A Vicenç Gómez Radboud University Nijmegen %A Hilbert Kappen Radboud University %B Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2014 %E Nevin L. Zhang %E Jin Tian %F pmlr-vR12-naist14a %I PMLR %P 371--380 %U https://proceedings.mlr.press/r12/naist14a.html %V R12 %X Kullback Leibler (KL) control problems al- low for efficient computation of optimal con- trol by solving a principal eigenvector prob- lem. However, direct applicability of such framework to continuous state-action sys- tems is limited. In this paper, we propose to embed a KL control problem in a proba- bilistic graphical model where observed vari- ables correspond to the continuous (possi- bly high-dimensional) state of the system and latent variables correspond to a dis- crete (low-dimensional) representation of the state amenable for KL control computation. We present two examples of this approach. The first one uses standard hidden Markov models (HMMs) and computes exact opti- mal control, but is only applicable to low- dimensional systems. The second one uses factorial HMMs, it is scalable to higher di- mensional problems, but control computa- tion is approximate. We illustrate both ex- amples in several robot motor control tasks. %Z Reissued by PMLR on 04 October 2026.
APA
NAIST, T.M., Nijmegen, V.G.R.U. & University, H.K.R.. (2014). Latent Kullback Leibler Control for Continuous-State Systems using Probabilistic Graphical Models. Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R12:371-380 Available from https://proceedings.mlr.press/r12/naist14a.html. Reissued by PMLR on 04 October 2026.

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