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dc.contributor.authorArslan, Oktay
dc.contributor.authorTheodorou, Evangelos A.
dc.contributor.authorTsiotras, Panagiotis
dc.date.accessioned2015-03-27T19:31:49Z
dc.date.available2015-03-27T19:31:49Z
dc.date.issued2014-12
dc.identifier.citationArslan, O., Theodorou E., and Tsiotras, P. "Information-Theoretic Stochastic Optimal Control via Incremental Sampling-based Algorithms" IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning, Orlando, FL, Dec. 9-12, 2014, pp. 71-78. 10.1109/ADPRL.2014.7010617en_US
dc.identifier.urihttp://hdl.handle.net/1853/53269
dc.descriptionCopyright © 2014 IEEEen_US
dc.descriptionPresented at IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning, Orlando, FL, Dec. 9-12, 2014
dc.descriptionDOI: http://dx.doi.org/10.1109/ADPRL.2014.7010617
dc.description.abstractThis paper considers optimal control of dynamical systems which are represented by nonlinear stochastic differential equations. It is well-known that the optimal control policy for this problem can be obtained as a function of a value function that satisfies a nonlinear partial differential equation, namely, the Hamilton-Jacobi-Bellman equation. This nonlinear PDE must be solved backwards in time, and this computation is intractable for large scale systems. Under certain assumptions, and after applying a logarithmic transformation, an alternative characterization of the optimal policy can be given in terms of a path integral. Path Integral (PI) based control methods have recently been shown to provide elegant solutions to a broad class of stochastic optimal control problems. One of the implementation challenges with this formalism is the computation of the expectation of a cost functional over the trajectories of the unforced dynamics. Computing such expectation over trajectories that are sampled uniformly may induce numerical instabilities due to the exponentiation of the cost. Therefore, sampling of low-cost trajectories is essential for the practical implementation of PI-based methods. In this paper, we use incremental sampling-based algorithms to sample useful trajectories from the unforced system dynamics, and make a novel connection between Rapidly-exploring Random Trees (RRTs) and information-theoretic stochastic optimal control. We show the results from the numerical implementation of the proposed approach to several examples.en_US
dc.language.isoen_USen_US
dc.publisherGeorgia Institute of Technologyen_US
dc.subjectOptimal controlen_US
dc.subjectStochastic processesen_US
dc.subjectLarge-scale systemsen_US
dc.titleInformation-Theoretic Stochastic Optimal Control via Incremental Sampling-based Algorithmsen_US
dc.typePost-printen_US
dc.typeProceedingsen_US
dc.contributor.corporatenameGeorgia Institute of Technology. School of Aerospace Engineeringen_US
dc.identifier.doi10.1109/ADPRL.2014.7010617
dc.embargo.termsnullen_US


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