Uncertainty in Artificial Intelligence
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Latent Kullback Leibler Control for Continuous-State Systems using Probabilistic Graphical Models
Takamitsu Matsubara, Vicenc Gomez, Hilbert Kappen
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.
Keywords:
Pages: 583-592
PS Link:
PDF Link: /papers/14/p583-matsubara.pdf
BibTex:
@INPROCEEDINGS{Matsubara14,
AUTHOR = "Takamitsu Matsubara and Vicenc Gomez and Hilbert Kappen",
TITLE = "Latent Kullback Leibler Control for Continuous-State Systems using Probabilistic Graphical Models",
BOOKTITLE = "Proceedings of the Thirtieth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-14)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "2014",
PAGES = "583--592"
}


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