dc.contributor.author |
Iribarne, Albert |
dc.date.accessioned |
2019-10-29T10:53:24Z |
dc.date.available |
2019-10-29T10:53:24Z |
dc.date.issued |
2019 |
dc.identifier.uri |
http://hdl.handle.net/10230/42542 |
dc.description |
Treball fi de màster de: Master in Intelligent Interactive Systems |
dc.description |
Tutor: Vicenç Gómez Cerdà |
dc.description.abstract |
The 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. |
dc.format.mimetype |
application/pdf |
dc.language.iso |
eng |
dc.rights |
Atribución-NoComercial-SinDerivadas 3.0 España |
dc.rights.uri |
http://creativecommons.org/licenses/by-nc-nd/3.0/es/ |
dc.subject.other |
Intel·ligència artificial |
dc.title |
Optimal control using sparse-matrix belief propagation |
dc.type |
info:eu-repo/semantics/masterThesis |
dc.subject.keyword |
Optimal control |
dc.subject.keyword |
Graphical model |
dc.subject.keyword |
Approximate inference |
dc.subject.keyword |
Sparse matrix |
dc.subject.keyword |
Belief propagation |
dc.subject.keyword |
GPU |
dc.rights.accessRights |
info:eu-repo/semantics/openAccess |