Optimal control using sparse-matrix belief propagation

Treball fi de màster de: Master in Intelligent Interactive Systems

Bibliographic Details
Author: Iribarne, Albert
Format: master thesis
Publication Date:2019
Country:España
Institution:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repository:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:10230/42542
Online Access:http://hdl.handle.net/10230/42542
Access Level:Open access
Keyword:Intel·ligència artificial
Optimal control
Graphical model
Approximate inference
Sparse matrix
Belief propagation
GPU
id ES_93e2715dcdede3d97bdf8d30c9abac0d
oai_identifier_str oai:recercat.cat:10230/42542
network_acronym_str ES
network_name_str España
repository_id_str
spelling Optimal control using sparse-matrix belief propagationIribarne, AlbertIntel·ligència artificialOptimal controlGraphical modelApproximate inferenceSparse matrixBelief propagationGPUTreball fi de màster de: Master in Intelligent Interactive SystemsTutor: Vicenç Gómez CerdàThe optimal control framework is a mathematical formulation by means of which many decision making problems can be represented and solved by finding optimal policies or controls. We consider the class of optimal control problems that can be formulated as a probabilistic inference on a graphical model, known as Kullback- Leibler (KL) control problems. In particular, we look at the recent progress on exploiting parallelisation facilitated by the graphics processing units (GPU) to solve such inference tasks, considering the recently introduced sparse-matrix belief propagation framework [1]. The sparse-matrix belief propagation algorithm was reported to deliver significant improvements in performance with respect to traditional loopy belief propagation, when tested on grid Markov random fields. We develop our approach in the context of the KL-stag hunt game, a multi-agent, grid-like game which shows two different behavior regimes [2]. We first describe how to transform the original problem into a pairwise Markov random field, amenable to inference using sparse-matrix belief propagation and, second, we perform an experimental evaluation. Our results show that the use of GPUs can bring notable performance improvements to the optimal control computations in the class of KL control problems. However, our results also suggest that the improvements of sparse-matrix belief propagation may be limited by the concrete form of the Markov random field factors, specially on models with high sparsity within a factor, and variables with high cardinality.201920192019info:eu-repo/semantics/masterThesisapplication/pdfapplication/pdfhttp://hdl.handle.net/10230/42542reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)InglésAtribución-NoComercial-SinDerivadas 3.0 Españahttp://creativecommons.org/licenses/by-nc-nd/3.0/es/info:eu-repo/semantics/openAccessoai:recercat.cat:10230/425422026-05-29T05:05:01Z
dc.title.none.fl_str_mv Optimal control using sparse-matrix belief propagation
title Optimal control using sparse-matrix belief propagation
spellingShingle Optimal control using sparse-matrix belief propagation
Iribarne, Albert
Intel·ligència artificial
Optimal control
Graphical model
Approximate inference
Sparse matrix
Belief propagation
GPU
title_short Optimal control using sparse-matrix belief propagation
title_full Optimal control using sparse-matrix belief propagation
title_fullStr Optimal control using sparse-matrix belief propagation
title_full_unstemmed Optimal control using sparse-matrix belief propagation
title_sort Optimal control using sparse-matrix belief propagation
dc.creator.none.fl_str_mv Iribarne, Albert
author Iribarne, Albert
author_facet Iribarne, Albert
author_role author
dc.subject.none.fl_str_mv Intel·ligència artificial
Optimal control
Graphical model
Approximate inference
Sparse matrix
Belief propagation
GPU
topic Intel·ligència artificial
Optimal control
Graphical model
Approximate inference
Sparse matrix
Belief propagation
GPU
description Treball fi de màster de: Master in Intelligent Interactive Systems
publishDate 2019
dc.date.none.fl_str_mv 2019
2019
2019
dc.type.none.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
dc.identifier.none.fl_str_mv http://hdl.handle.net/10230/42542
url http://hdl.handle.net/10230/42542
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv Atribución-NoComercial-SinDerivadas 3.0 España
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Atribución-NoComercial-SinDerivadas 3.0 España
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.source.none.fl_str_mv reponame:Recercat. Dipósit de la Recerca de Catalunya
instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str Recercat. Dipósit de la Recerca de Catalunya
collection Recercat. Dipósit de la Recerca de Catalunya
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869413634446196736
score 15,812429

Similar Items