Graph-based stochastic control with constraints: A unified approach with perfect and imperfect measurements
KAUST Grant NumberKUS-C1-016-04
Permanent link to this recordhttp://hdl.handle.net/10754/598428
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AbstractThis paper is concerned with the problem of stochastic optimal control (possibly with imperfect measurements) in the presence of constraints. We propose a computationally tractable framework to address this problem. The method lends itself to sampling-based methods where we construct a graph in the state space of the problem, on which a Dynamic Programming (DP) is solved and a closed-loop feedback policy is computed. The constraints are seamlessly incorporated to the control policy selection by including their effect on the transition probabilities of the graph edges. We present a unified framework that is applicable both in the state space (with perfect measurements) and in the information space (with imperfect measurements).
CitationAgha-mohammadi A, Chakravorty S, Amato NM (2013) Graph-based stochastic control with constraints: A unified approach with perfect and imperfect measurements. 2013 American Control Conference. Available: http://dx.doi.org/10.1109/ACC.2013.6580545.
SponsorsThe work of Agha-mohammadi and Chakravorty is supported in part by NSFaward RI-1217991 and AFOSR Grant FA9550-08-1-0038 and the work of Aghamohammadiand Amato is supported in part by NSF awards CNS-0551685, CCF-0833199, CCF-0830753, IIS-0917266, IIS-0916053, EFRI-1240483, RI-1217991, byNSF/DNDO award 2008-DN-077-ARI018-02, by NIH NCI R25 CA090301-11, byDOE awards DE-FC52-08NA28616, DE-AC02-06CH11357, B575363, B575366, byTHECB NHARP award 000512-0097-2009, by Samsung, Chevron, IBM, Intel, Oracle/Sun and by Award KUS-C1-016-04, made by King Abdullah University of Scienceand Technology (KAUST).
Journal2013 American Control Conference