Note: This content is accessible to all versions of every browser. However, this browser does not seem to support current Web standards, preventing the display of our site's design details.

  

Approximate dynamic programming using radial basis functions: A reach-avoid problem

Author(s):

N. Kariotoglou
Conference/Journal:

Stanford, California, USA
Abstract:

We consider finite horizon reach-avoid problems for discrete time stochastic systems with additive Gaussian mixture noise. Our proposed approximation scheme involves relaxing the recursive equations that characterize the optimal value function into inequalities, projecting the optimal value function to a finite dimensional basis and sampling the associated infinite set of constraints. We focus on a specific function parameterization using Gaussian radial basis functions that enables the analytical computation of the one-step reach-avoid reward in the case of hyper-rectangular safe and target sets. We demonstrate the method on a pair of reach-avoid problems involving linear and non-linear dynamics.

Year:

2014
Type of Publication:

(06)Talk
Supervisor:

J. Lygeros

File Download:

Request a copy of this publication.
(Uses JavaScript)
% No recipe for automatically generating a BibTex entry for (06)Talk
Permanent link