Robust non-linear control through neuroevolution

dc.contributor.advisorMiikkulainen, Ristoen
dc.creatorGomez, Faustino Johnen
dc.date.accessioned2008-08-28T21:28:12Zen
dc.date.available2008-08-28T21:28:12Zen
dc.date.issued2003en
dc.descriptiontexten
dc.description.abstractMany complex control problems require sophisticated solutions that are not amenable to traditional controller design. Not only is it difficult to model real world systems, but often it is unclear what kind of behavior is required to solve the task. Reinforcement learning approaches have made progress in such problems, but have so far not scaled well. Neuroevolution, has improved upon conventional reinforcement learning, but has still not been successful in full-scale, non-linear control problems. This dissertation develops a methodology for solving real world control tasks consisting of three components: (1) an efficient neuroevolution algorithm that solves difficult non-linear control tasks by coevolving neurons, (2) an incremental evolution method to scale the algorithm to the most challenging tasks, and (3) a technique for making controllers robust so that they can transfer from simulation to the real world. The method is faster than other approaches on a set of difficult learning benchmarks, and is used in two full-scale control tasks demonstrating its applicability to real world problems.
dc.description.departmentComputer Sciencesen
dc.format.mediumelectronicen
dc.identifierb56808586en
dc.identifier.oclc56087862en
dc.identifier.proqst3116311en
dc.identifier.urihttp://hdl.handle.net/2152/604en
dc.language.isoengen
dc.rightsCopyright is held by the author. Presentation of this material on the Libraries' web site by University Libraries, The University of Texas at Austin was made possible under a limited license grant from the author who has retained all copyrights in the works.en
dc.subject.lcshNonlinear control theoryen
dc.subject.lcshEvolutionary computationen
dc.subject.lcshNeural networks (Computer science)en
dc.titleRobust non-linear control through neuroevolutionen
dc.type.genreThesisen
thesis.degree.departmentComputer Sciencesen
thesis.degree.disciplineComputer Sciencesen
thesis.degree.grantorThe University of Texas at Austinen
thesis.degree.levelDoctoralen
thesis.degree.nameDoctor of Philosophyen

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